Generative AI Full Course 2026 | Gen AI Tutorial for Beginners | Gen AI Explained | Simplilearn

Hey everyone, welcome to Simply Learn's generative AI film course. Whether you are a beginner or want to boost up your skills, you are in the right place. We'll start with the basics and guide you through key concepts like large language models, deep learning and agentic AI all explained very simple. You learn how to use tools like openi codeex hugging face and lang chain plus gets hands-on demo with machine learning reinforcement learning and easy AI workflows. We'll also cover no code options transformers GANs and advanced topics like quantum AI and on the top of it I will prepare you for the AI job interviews and special tools to help you stand out. So let's get started. Before we comments, if you are interested to master the future of technology, then the professional certificate course in generative AI and machine learning is the perfect opportunity for you. Offered in collaboration with the NIC Academy, IT Kpur. This 11month live and interactive program provides hands-on expertise in cutting edge areas like generative AI, machine learning, and tools like chat, GPT, DL2, and even hugging face. You'll also gain practical experience to 15 plus projects, integrated labs and live master classes delivered by esteemed ID KPU faculty. So hurry up and enroll now and find the course link in the description box below and in the pin comments. Now the agenda for today's session. Firstly, let's get started with the agenda for today's session. Now we will have a brief introduction to this session. Followed by that we will have a briefing of what exactly is AI? Then how does it work? Next we will understand the key technologies and frameworks involved and we will also go through some everyday use cases where we can take help of AI and also some of the responsibilities that we need to keep in mind for the ethical use of AI and lastly we will explore some prompts. Now that I have made myself clear with the agenda let's get started with the first part introduction. How can AI simplify daily task? As we discussed in the use case, a human might have a lot of routine and mundane tasks. For example, automation. AI can simplify your daily tasks like checking up emails and drafting email responses, enhancing productivity by getting AI assistance for research in marketing trends or any other researches based on your job profile. And you can have a personalized AI assistance, for recommendations, for learning and much more. And after that it will save you a lot of time reducing effort. Now the next part get set up with generative AI models like Microsoft Copilot and try some of the examples. So if you get access to one of the generative AI models like GitHub then you can access GitHub copilot easily use it in Microsoft 365 apps like Word, Excel and Teams. Then you can also explore prompting basics to start up with simple commands like drafting emails or summarizing documents. And proceeding ahead we have another opportunity to experiment with different use cases like trying copilot for brainstorming, planning and organization. And fourth one customize and iterate. Improvise AI generated results by refining prompts based on your needs and requirements. Now let's proceed ahead and understand what exactly is AI and how it works which will involve some of the key topics like understanding the basics and technologies involved etc. So generative AI is an advanced type of artificial intelligence that can sync up with human activities and gather requirements to assist humans in their daily tasks improving efficiency and effectiveness. Not just these professional tasks, AI can create new content like text, images, music, and videos. It uses natural language processing and neural networks to learn patterns from existing data and then generate new content. Now, let's see how we can use AI to simplify our daily task and make life easier. Firstly, AI can create unique content, not just analyze data and make predictions. It can assist you in real-time generation of original text, images, music and more. Next is AI as a creative partner. Generative AI can produce original text, images, music and much more. It goes beyond traditional AI and like predictive AI, generative AI creates new content rather than just analyzing data. Proceeding ahead, we have the next functionality where it can enhance human creativity by assisting with brainstorming ideas, storytelling, design, and content generation. Lastly, it is widely used in multiple fields found in marketing, education, entertainment, and software development. Now, let's proceed ahead and understand the key technologies behind generative AI. Large language models is the first one trained on vast amounts of text to generate humanlike responses. for example, CH GBD, Palm, Llama and Microsoft Copilot. It is used for writing summarization of code and emails and much more. Now the next part is natural language processing. It enables AI to understand, interpret and generate human type languages. It powers chat bots, voice assistance and translation tools. Next, it helps improve communication between humans and AI. Now let's proceed ahead and understand the everyday use cases of AI assistance. Starting with creative writing and content creation, you can create and generate blog posts, articles, social media captions and scripts. AI assistant in brainstorming ideas, editing text and improving clarity. And lastly, he can also create poetry, stories or some song lyrics using AI. The second one is organizing and planning. He can make use of AI to plan a trip it based on preferences and budget. He can generate meal plans and grocery lists as well. He can schedule events and set reminders and manage to-do lists. The next use case is personal assistance. You can use AI to draft and refine emails or messages. It also helps you by providing phone books and recommending movies and music. And lastly, summarize documents, articles, or even meeting notes. Lastly, we have some other practical uses which include the following. Learning new skills with AI generated study plans. Translating languages to provide real-time interpretations. Offering health and fitness suggestions based on personal goals. Now, proceeding ahead, we will understand AI agents. AI agents are advanced virtual assistants designed to perform tasks autonomously. They interact with users, process information and execute commands. Some of the examples are chat GPT, Microsoft Copilot, Gemini and others. Now, what do AI agents actually do? So, AI agents conduct the following tasks. First, automate some repetitive tasks. Repetative tasks like scheduling meetings, setting up reminders, and managing emails is done by AI assistants. It can enhance decision- making by providing datadriven insights and recommendations. Third is it can act as a personalized digital assistant which can help you with learning, travel planning, productivity and much more. Lastly, integrating with tools and apps which can help you work across Microsoft 365 CRM platforms and much more. And now let's proceed with the major part of today's session where we have to focus on how to use AI responsibly and effectively. So here we have a detailed Microsoft copilot AI overview and also AI agents and their role in productivity. Let's go through that first. So here we have a quick briefing of Microsoft 365 copilot and we have also some links to the copilot success kit, copilot chat, agent starter kit and the community lens over here and resources copilot mobile version what's new and all and everything you need to know about copilot about the technical readiness etc. Now back to the presentation where we were discussing about the responsible use of AI. So the importance of ethical use of AI. AI should be used in a way that it is fair, transparent and accountable. Understanding AI limitations will help you prevent misinformation and misuses. Ethical AI usage ensures trust and reliability in everyday applications. Proceeding ahead, we will discuss about ensuring transparency and accountability. First one, clearly communicate when AI is used in content creation. provide with the specific prompts that you're expecting in your output so that it cannot miss on the target. Secondly, we need to verify AI generated information before sharing or acting on it because there might be instances where we might go wrong explaining our inputs or there might be some discrepancy in the data provided to analyze and generate a report. And lastly, understand how AI makes decisions to avoid over reliance. You need to understand the thought process of an AI and expect what kind of results it might generate so that you don't have to rely on one single prompt every single time. Now let's move ahead and address some potential biases in AI. AI can sometimes reflect biases present in its training data. Common biases include general bias, racial bias, cultural bias, confirmation bias, and socioeconomic biases. Let's discuss each one of them. Firstly, gender bias. AI generated job descriptions may reinforce stereotypes. Example, associating engineers roles with men. Second one is racial bias. Facial recognition systems may misidentify individuals from certain ethnic backgrounds. Third one, cultural bias. AI might prioritize Western perspectives in content generation. Fourth one, confirmation bias. It may reinforce existing beliefs by favoring information similar to past searches. Lastly, socioeconomic bias. For example, AI generated loan approvals may favor individuals from higher income backgrounds. Now, you also need to focus on responsible prompting. Use clear and neutral language to avoid influencing bias results. Ask AI to provide diverse perspectives and sources when generating responses. Be mindful of data privacy and avoid sharing sensitive personal information. Now let's go ahead and check some of the prompt examples. So now we are on the Microsoft Copilot prompt gallery which will show you some of the sample prompts that you can make use of. For example, get a to-do list, add an image, quiz yourself, create a technology, write more confidently. For example, you might have a description. You can just add it to improvise or add more impact onto it. Find a specific information, draft an email, create a shopping list and so on. So here you can also go through some other prompts as well by clicking on view all prompts and a whole new world of prompt gallery will open up for you. Try it out and let us know your experience in the comment section below. If you ever wondered how machine learning can now understand and generate humanlike text, you are in the right place. From chatboards like chat jeepy to AI assistant that powers search engines, LLMs are transforming how we interact with technology. One of the most exciting advancement in this space is Google's Gemini or OpenAI Charging large language model designed to push the boundaries of what AI can achieve. In this video, we will explore what LLMs are, how they work, and why models like Geminy are critical for the future of AI. Google Gemini is part of a new wave of AI models that are smarter, faster, and more efficient. It is designed to understand context better, offer more accurate responses, and integrate deeply into service like Google search and Google Assistant, providing more humanlike interactions. So, we will break down the science behind LLMs, including their massive training data set, transformer architecture, and how models like Gemini use deep learning innovation to change industries. Plus, we will compare Google Gemini to other popular LMS such as OpenAI Chat GBT models, showing how each of these technologies is used to power chat bots, virtual assistants, and other AIdriven application. By end of this video, you will have a clear understanding of how large language models like Gemini work, their key features, and what they mean for their future AI. Don't forget to like, subscribe, and hit the bell icon to never miss any update from Simply Learn. So, what are the large language models? Large language models like CH GPD4 generate a pre-trained transformer 4 O and Google Gemini are sophisticated AI system designed to comprehend and generate humanlike text. These models are built using deep learning techniques and are trained on vast data set collected from the internet. They leverage self attention mechanism to analyze relationship between words or tokens allowing them to capture context and produce coherent relevant responses. LLMs have significant application including powering virtual assistant, chatboards, content creation, language translation and supporting research and decision making. Their ability to generate fluent and contextually appropriate text has advanced natural language processing and improved human computer interaction. So now let's see what are large language model used for. Large language models are utilized in scenarios with limited or no domain specific data available for training. These scenarios include both few short and zero short training approaches which rely on the model's strong inductive bias and its capability to derive meaningful representation from a small amount of data or even no data at all. So now let's see how are large language model trained. Large language models typically undergo pre-training on a board. All encompassing data set that shares statical similarities with the data set specific to the target task. The objective of pre-training is to enable the model to require highlevel feature that can later be applied during the fine-tuning phase for a specific task. So there are some training processes of LLM which involves several steps. The first one is text prep-processing. The textual data is transformed into a numerical representation that the LLM model can effectively process. This conversion may be involve techniques like tokenization, encoding and creating input sequences. The second one is random parameter initialization. The model's parameter are initialized randomly before the training process begins. The third one is input numerical data. The numerical representation of the text data is fed into the model of processing. The model's architecture typically based on transformers allows it to capture the conceptual relationship between the words or tokens in the next. The fourth one is loss function calculation. A loss function calculation measure the discrepancy between the model's prediction and the actual next word or token in a syntax. The LLM model aims to minimize this loss during training. The fifth one is parameter optimization. The model's parameter are registered through optimization technique. This involves calculating gradient and updating the parameters accordingly gradually improving the model's performance. The last one is iterative training. The training process is repeated over multiple iteration or epochs until the model's output achieve a satisfactory level of accuracy on that given task or data set. By following this training process, large language model learn to capture linguistic patterns, understand context and generate coherent responses enabling them to excel at various language related task. The next topic is how do large language models work. So large language models leverage deep neural network to generate output based on patterns learned from the training data. Typically a large language model adopts a transformer architecture which enables the model to identify relationship between words in a sentence irrespective of their position in the sequence. In contrast to RNNs that rely on recurrence to capture token relationship transformer neural network employ self attention as their primary mechanism. Self attention calculates attention scores that determine the importance of each token with respect to the other token in the text sequence facilitating the modeling of intricate relationship within the data. Next let's see application of large language models. Large language models have a wide range of application across various domains. So here are some notable application. The first one is natural language processing NLP. Large language models are used to improve natural language understanding tasks such as sentiment analysis, named entity recognition, text classification and language modeling. The second one is chatbot and virtual assistant. Large language models power conversational agents, chatbots and virtual assistant providing more interactive and humanlike user interaction. The third one is machine translation. Large language models have been used for automatic language translation enabling text translation between different languages with improved accuracy. The fourth one is sentiment analysis. LLMs can analyze and classify the sentiment or emotion expressed in a piece of text which is valuable for market research, brand monitoring and social media analysis. The fifth one is content recommendation. These models can be employed to provide personalized content recommendations enhancing user experience and engagement on platforms such as news website or streaming services. So these application highlight the potential impact of large language models in various domains to improving language understanding, automation and interaction between humans and computers. In this tutorial, we will learn about OpenAI's gen AI agents. So, generative AI agents are advanced AIdriven systems designed to autonomously perform tasks, generate content, and assist in decision making by leveraging large language models or also known as LLM and machine learning algorithms. Imagine an AI agent automating repetitive processes, enhancing productivity, and providing personalized recommendations across various domains, including customer support, marketing, and software development. By understanding context and generating human-like responses, they improve user engagement and streamline workflows. Businesses benefit from cost savings, faster problem solution, and improved efficiency. Such are a few out of many use cases of AI agents. Today, we will cover the OpenAI Gen AI agents and their facts, their challenges, and future implications. That's it. If these are the type of videos you'd like to watch, then hit that like and subscribe buttons and the bell icon to get notified. Also, just that you know, if you want to upskill yourself, master generative AI and artificial intelligence skills and land your dream job or grow in your career, then you must explore Simply Learn's code of various generative AI courses and certifications. Simply learn offers various certification programs in collaboration with some of the world's leading universities like Perdue, IIT, Guati and many more. Through our courses, you will gain knowledge and work really expertise in skills like advanced Python, machine learning, generative AI and over a dozen others. That's not all. You also get the opportunity to work on multiple projects and learn from industry experts working in top player data and product companies and also academicans from top universities. After completing these courses, thousands of learners have transitioned into an AI and machine learning role as a fresher or moved onto a higher paying job and profile. If you are passionate about making your career in this field, then make sure to check out the link in the pin comment and description box below to find a generative AI program that fits your experience and areas of interest. Now without further delay, let's get started. Open AI Gen AI agents. So first let's get started with the agenda for today's session. We will have a brief introduction to generative AI agents. Followed by that we will understand the core components of gen AI agents. Then how generative AI agents work, architectures and deployment, enhancing AI agents with external tools, challenges and limitations of gen AI agents, future trends and advancements in AI agents will be the last one. So I hope I made myself clear with the agenda. Now let's get started with the first part which is introduction to generative AI agents. So basically what exactly do you mean by the term generative AI agents? So an AI agent is just an LLM that can take actions and it can do so autonomously without any human supervision and now also reason about the tasks. An AI agent is software entity that autonomously interact with environments, make decisions and execute tasks. Generative AI agents extend by leveraging large language models like open eye GPT to generate intelligent responses and perform tasks beyond rule-based automation. Now we will also look into the evolution of AI agents. Traditional rule-based AI agents had limited to predefined rules like if you define a process and give the steps it will just perform them and complete it. It will not take a step beyond it. And machine learning based AI agents can learn from data but need structured inputs. And then comes the generative AI agents which are capable of understanding, reasoning and generating humanlike responses autonomously like they understand the environment and learn through it and take their steps based on the principle they have been trained for and also they will provide the reason for which they have taken a certain step. Now moving ahead, why generative AI agents matter a lot? So there are three key points to consider. Firstly, efficiency. They automate repetitive task with minimal human intervention. Next is high scalability. They handle large volumes of work. Example, customer support, content generation, and many more. And finally, intelligence. They can adapt to different use cases dynamically. Nobody has to write rules for them. Now, let's focus on Open AI and its latest release of agents. So, OpenAI just released two new groundbreaking agents, the operator agent and deep research agent. These two releases are absolutely massive for all AI agent developers. So, operator is the first AI agent released by OpenAI and what it does is it mimics human actions on your browser. So, it means it can scroll, type, click and navigate Google web pages for you just like any other human would. But more importantly and what many people have missed out is this agent is actually based on a completely new model called CUA which stands for computer using agent. So this agent unlike standard GPD models wasn't trained to just output text. You like write a prompt and expect an output in the text format. Instead of that it can go beyond it. This agent was actually trained to output mouse and keyboard clicks, which is exactly what makes this agent so powerful in your browser. But not only that, it can also reason about every single action. This is also something that we haven't seen before. This agent doesn't just perform actions in your browser. It actually reasons about every single step and what it should do next. Just like we discussed before about the generic overview of generative AI agents same as the open AI operator here it takes the step based on the environment and also it will give you a particular reason for which or based on which it took a certain action and it also asks you what to do next. Let's say if you want to book a ticket it'll navigate to all the websites. It can go through some uh better flight options for you. Let's see the low cost ones, the low uh hours of flight ones and best timings and then finally selects the best one for you based on your recommendations and asks you if it should proceed with booking or not. So that's how it works. Now of course there are some limitations as well. The first limitation is it has still a lower bit of accuracy. is really high compared to other projects that we have seen in the past where people just prompt GPD models to perform actions in the browser that can browse web using GPT4 or vision capabilities but it often struggles with other models. So whenever there was a model on a web page like some other random model available working readily on a web page it would just accidentally pop up and it would not be able to do anything because it would just get confused. it would not be able to close it and then basically you would have to get involved yourself and close this model manually. And the second limitation is high cost. Right now this particular operator agent is only available on $200 plus plan. So we can probably guess it's going to be pretty expensive in the API. Also I do think that if you are focusing on the right thing then costs are not going to be an issue. Now let's talk about the second agent that open AAI deployed. Now the second agent which was deployed by open AI is called as deep research. So this second agent released by OpenAI was trained to perform comprehensive research. It can search the web. It can pull up the necessary resources and then it can compile all that information into a really comprehensive document or maybe report as well. The groundbreaking thing about this agent is it's powered by the new 03 model. So again, similar to the operator agent, but it doesn't just search the web and provide you with results. It actually reasons about every single source and then it thinks about what other information it needs to find out next. And it's really incredibly powerful. Deep research doesn't just output the most average thing on the internet just like some other standard GPD models. It would compile novel insights. Now let's move on to the real world applications. There are a wide variety of real world applications. First one is customer support AI agents which helps you in automating help desk responses. Next powerful AI assistants like copilots for professionals. Third one is research and analysis agents for data summarization literature reviews and many more. Fourth one is creative writing and content generation automating social media post emails etc. And lastly AIdriven code assistance like GitHub copilot, openAI codeex and many more. Now let's go through the next part which is core components of these generative AI agents. Let's begin with the foundation models. So the foundation models are GPD4, Dolly and Whisper. GPD4 for natural language processing, Dolly for image generation and Wesper for speech to text conversion. The next one would be prompt engineering and context handling. So here designing effective prompts for AI agents to generate relevant responses using few short and zeroshot learning for adaptability and lastly implementing context aware AI agents that remember past interactions from the user. Now next would be the memory and long-term context storage. AI agents typically process short-term prompts but memory based approaches for example lang chains memory module enable long-term reasoning as well. Now storing and retrieving user specific data for personal responses is also one of the priority. Next will be the tool use and API integrations using external API to fetch real-time data for example weather store prices news etc and integrating databases for structured information retrieval and proceeding further we have autonomous decision making AI agents evaluate multiple options before making a decision planning and reasoning techniques to enhance responses is a priority for them proceeding ahead we will discuss how generative AI agents work. Starting with the first step, input processing and context awareness. AI agents analyze user input, detect intent and extract entities. For example, in a travel booking AI agent, it identifies destination date and budget from user queries. Just like the example we discussed before, right? You provide the point A and point B, the best time for you to fly and the best number of hours that you focus on flying and lastly the destination, right? All these things will be evaluated in real time, the cost, the number of hours, the flight, the options provided, the amenities provided on the flight and all those things and also budget as well and based on that they take a decision. Now proceeding ahead generating intelligent responses. So here R A or also known as retrieval augmented generation. AI searches external databases responding. Next we have chain of thoughts reasoning or also known as coot reasoning. And AI breaks down problems into fine steps. And last step is fine-tuning the models. Customtrained AI for specific industries. For example, if you're preparing an AI agent for a dedicated sector like healthcare, legal, customer support, based on that, you will fine-tune your AI agent or model. Now, next is decision trees and multi-step reasoning. AI can simulate a thought process, breaking down complex questions into manageable steps. For example, whenever you open Deepseek or whenever you open Open AI charge GPD for the most premium version, you will write a prompt and there will be an option called think, right? If you just click on that, it will explain you the thought process it is going through and how is it planning on giving a reason for you. Right? And followed by that we have handling uncertaintity and error correction. AI self-corrects through confidence scoring and human feedback loops. Last step in this particular stage is fine-tuning and reinforcement learning or also known as RLHF. Human in the loop training to improve responses and AI learns from real world interactions and adapts over time. So it's basically called overtime learning where it adapts to the real world environment and learns things and provides the best feasible solution. Now the next stage is architecture and deployment of AI agents. So in the first one we have standalone versus multi- aent systems. So firstly the standalone AI agents they perform task independently. For example chart GPD multi- aent systems in the other hand work together to achieve a complex task. For example one agent for summarization another for execution. Now next in this particular stage is agent orchestrator modeling. A central AI agent coordinates multiple AI sub agents. For example, in an AI powered research assistant, one agent gathers all the data that is required. The second agent summarizes all the information collected and the third agent formats the output. Followed by that we have cloud-based AI agents and edgebased AI agents. Cloud-based AI agents compute, for example, OpenAI API, Azure AI, and edge-based AI agents run on local devices for faster processing. For example, Tesla's AI in self-driving cars. Followed by that, we have using APIs to build AI agents. OpenAI's API for GPD4 powered by AI agents, lang framework for AI agent orchestration, and Llama index for document-based AI agents. The next stage we will discuss about enhancing AI agents with external tools. In that the first step is integrating with databases. AI agents can query SQL and non-SQL query databases for structured data retrieval. Proceeding ahead we have connecting with web scrapers and APIs. AI agents can fetch realtime data using tools like beautiful soup scrappy and open AI plugins. Next ahead, using vector databases or context recall. F AIS Chroma DB store long-term memory for AI agents. For example, legal AI agents retrieve case law from vector embeddings. Lastly, we have autonomous agents and workflows. AutoGPD and baby AGI are best examples. These AI agents that self-improve by learning from previous task and true AI which is a set of multiple AI agents collaborating to solve a complex problem. Now the main part challenges and limitations of generative AI agents in that the first one will be the ethical considerations. AI bas and fairness concerns are the primary ones addressing AI hallucinations which is false information generation. And then we have computational cost and efficiency which is a major concern amongst all the industries. Large AI models require significant computational power. Strategies to optimize AI agent efficiency will also considered to be expensive to hire many training models or also human programmers. Followed by that we have security risks and data privacy. Risk of data leaks or data breaches and AI jailbreaking is one of the highest security threat. Implementing secure AI architecture is one of the primary solutions that you need to focus on. And lastly, we have legal and compliance issues. This would major be concerned from the enterprise level. GDPR, HIP, AA and AI regulations compliance is mandatory and AI liability in decision making is also important. And now the last stage of today's discussion which is future trends and advancements in AI agents. Starting with the first one, AI agents with emotional intelligence are also known as effective AI. AI is capable of detecting emotions and adapting responses accordingly. For example, if you have designed a therapic AI chatbot for mental health. Now the next one, self-improving AI systems. So metalarning and AutoML are a few examples. AI agents that learn from new data autonomously. For example, AI adjusting marketing campaigns based on customer responses. And now the next one, AGI and road map to super intelligence. Well, this always ends up with question, will AI agents evolve into AGI or also known as artificial general intelligence? And are the implications of self-arning AI real? This is the major question. And lastly, the role of AI agents in the future of work. Now we can see there are a lot of AI agents like copilot which will assist humans in their day-to-day task whether it is business healthcare or education and also you have a lot of automation and transformation happening in the IT industry which will reduce the work hours and stress on the employees by automating all those mundane task by the help of co-pilots also the generative AI agents. Are you feeling like every other video, article, or tweet you see these days is about generative AI? You're not wrong. It's the tech world's current obsession. But instead of just nodding along, wondering what it all means, what if you could actually master it? In this video, we are cutting through the hype, giving you a practical step you need to truly understand. I will be breaking down the entire journey into manageable phases from fundamental knowledge to building your own gen AI projects. Before we begin, let me tell you what generative AI or Gen AI means. As the name suggest, it is generating data. It's a technology where it uses existing data for creating new ones. It might be in the form of text, images, audio, video, code or even 3D models. For better understanding, I will be dividing the entire road map into four phases plus one additional phase as a bonus phase for you to excel in this field. Phase one is all about laying the foundation which includes the basics of AI and machine learning. Think of artificial intelligence as making computers smart enough to do things which people can do. Machine learning is just one of the way to do it. allowing computers to learn from example instead of being told exactly what to do. It's a learning by seeing and doing it not just by a rule book. There are three main types of machine learning. Supervised learning, unsupervised learning and reinforcement learning. Supervised learning is where the computers learn with correct answers. Whereas unsupervised learning involves finding patterns on its own and reinforcement learning is about learning through rewards. You will need Python 2. Key tools include numpy for numerical operations, pandas for organizing data and mattplot lib and seaborn for data visualization. These help you to work with and understand data. You need to understand data preparation which involves cleaning and organizing data. Also learn about the feature engineering which is the process of selecting important parts of the data of the model. Gen AI uses deep learning. You can think of deep learning as a computer network with many layers. Understand the basic idea of how these networks learn from data. This is a key for understanding more complex gen AI models. Moving on to phase two where you can start exploring the Gen AI models. Some of Gen AI models includes GANs which are two networks competing, one making fake data and other tries to spot it. This model works well for creating realistic outputs. The second model is VAEs. This model learns a hidden representation of data to create new or similar data. The next model is transformers which are excellent at understanding context in sequence like text, audio and even images using attention. The last model is diffusion model which can create highquality data by reversing or noising process. This model is very popular for images. Apart from gen AI models, you need to cover text generation model which falls under natural language processing which is NLP. These models power text creation from writing articles to translating languages. And lastly, you will cover image generation using GANs and diffusion models. In phase three, we will focus on developing our practical skills. You can start with small projects such as generating basic images or text with small data set. Do experiment and learn the workflow. Join platforms like hugging face, Reddit, Stack Overflow and Kaggle forums to ask question and learn from others to showcase your work. You can use GitHub to share your projects and code. Phase 4 is truly about staying ahead. Gen AI is always changing. So you need to be aware of it. New architectures are constantly being built. So briefly be aware that more advanced models are continuously being developed beyond the basics that we have covered. I would also recommend learning about deploying models on cloud platforms like AWS, Google Cloud or Azure which will be useful for larger projects. Additionally, you can read research papers, follow tech expert online whether on Twitter or LinkedIn and stay curious about this field. Continuous learning is the key in this field. Now that you have built a strong foundation in generative AI, phase 5 is all about mastering the future of technology. A certification course provides formal validation of specialized knowledge and skills, making individuals stand out to employees and demonstrating a commitment to professional development. This recognition often leads to enhance credibility that improves career opportunities with specific field. Now we will begin with the agenda for MCP tutorial for beginners. So firstly we will have a quick briefing of what exactly is an MCP or as it is called multi- aent collaboration protocol. Followed by that we will understand some basic protocols and standards in communication. Next we will try to relate MCP to the understanding of protocols and standards. Followed by that we will get started with our demonstration on developing a web scraper agent using MCP and for that we will be needing NodeJS cursor AI and firecroll API. So I will be walking you through those demonstration steps one after the other and this will be a very beginnerfriendly tutorial and practical demonstration on MCP. Now that I've made myself clear with the agenda, let's get started with MCP. So what exactly is MCP? As MCP on their web page says, MCP is a model context protocol or multi- aent collaboration protocol is an open source protocol that standardizes how applications supply context to large language models. Similar to how USBC unifies hardware connections, MCP offers a consistent way to integrate AI models with various data sources and tools. Now let's go to the official documentation of MCP to have a broader overview. So as they say MCP is one such tool if you remember N8N and all the AI tools and data sources that you used. For example, if you wanted an AI travel agent as we have an already existing tutorial on our channel, you can go through it for a brief overview. So you might be needing an email writing agent and you might be needing an airport code searching AI agent, hotel searching AI agent, flight booking AI agent and a lot of agents connected to one other through a complex network using an ATN platform. Right now in this particular MCP that will be minimized. So you will have a standardized approach where you just eliminate all those boilerplate coding agents, manual searching for databases, manual search for knowledge bases, manual search for agents which are specifically designed for the purpose. You just explain your initiative, attach the API keys and MCP takes over and gives you a fully furnished and finished product within no time. That's the main approach of MCP. So here you can see an overview image where we have the paths MCP host clients servers local data sources remote services and local data sources. If you wanted to go ahead with local data sources or local knowledge base you can go ahead with that. But in case if you wanted some open source then you can also go ahead with that. So here we have all the quick starts and guides and example and tutorials to how to begin with that one and all the explore pages that you want to go ahead with if you wanted to have a deep dive into core architecture the resources available prompts tools transport samplings and a lot more. And in case if you wanted to contribute your dedicated resources for the community you can also go ahead with that and you can also have some support and feedback for MCP. Now that we have a brief understanding of MCP and still clueless about what exactly is MCP then let's go ahead with some standards and protocols so that you can get a template and you can relate MCP to that template and understand what's the role or importance of MCP in AI agent development. Now understanding the standards and protocols. Now let us imagine that you get a team of people to work on a project and you are the manager that leads and directs this particular team for a purpose. Right? Now the complex problem no one is friend of anyone. Everyone is new to everyone on the team and each and every person in this team belongs to a different country and does not have a common language in between. For example, person A speaks Mandarin, person B speaks Hindi, person C speaks French, so on. There is no common language between any one of those. Now, imagine each and every one of these team members as an AI agent or a tool working for the purpose. In this scenario, you might have to hardcode the ports of communication instructions on how to do and you need to go on to each and every single AI agent and explain the stuff in a very dedicated and a specific way. This could take a very long time. In the same way, if you relate this to a person, then you might have to reach to every single person and speak to them in their own tongue and explain them the procedures and the steps to be taken to finish the task. Now, how would you do that? It would be a lot complex. Mandarin, you might not even know, French or a different language. And in that particular scenario, you might be stuck. What if there was one single language that everyone knew, for example, English? Now, you can get all of them together and explain the stuff in one single call and close it. And what might be so wonderful is that you'll not even waste a lot of time and there is no hard coding. There is no manual effort there. Right? In the same way if you relate this to a web server then you are the client you have the server and you just let's say you're working on Facebook or Instagram you try to post something you try to write a command you try to like something right that is the activity and these particular activities will go through four different types of requests. One is the get request put request post request and last is the delete request. Right now if you are connected to a single server this might not be as tough as possible but if you are getting connected to multiple servers then you might be requiring different protocols every single time now this can be combination of two parts HTTP and the URL that you're trying to get connected to right now to eliminate the efforts between multiple servers and multiple requests at one go you have one single tool called as API And the most popular API is the rest API which has a standard way of communicating. So here the rest API works as the language English that we discussed before. In the same way, it has maintained a standard where each and every web client can have his communication to his dedicated server or multiple servers in one go using one single language or medium or standard that is rest API. Without rest API and the standards and protocols, it might be very confusing and you might end up in a complex situation which might not yield in the type of result you're expecting. Now if you can relate the development of multiple AI agents for a single purpose to the web communication and a meeting you also expect if there were a template for development of AI agents which could make the process less complex just like rest API did in a web service and just like English did in a team meeting then MCP comes into the picture. MCP is your template for it. Right? So just like your REST API and the meetings, you have a MCP. Earlier without MCP, we used to have one tool called NA10 where you had all the tools connected to it and all the LLMs programmed to it and you had to do this hardcode coding for each and every tool that you have included in the network and each and every agent that you have related to it. You had to specifically instruct, write down steps, develop a persona to your AI agent, right? There was a lot of stuff. But if you eliminate this particular complexity with one tool, then that would be your MCP. You just had to include MCP and tell your purpose and your job would be done. Now that you have a brief understanding or a general overview of what exactly is MCP, now let's go ahead and build one. Now the steps would include the following. For the project that we are building which is a web crawler or web scraper we might need the help of NodeJS. So firstly we will install and set the environment for NodeJS. Followed by that we will be needing an editor. So for that we will be choosing the cursor AI editor. And next we will use an API. So for that we will be using the firecrawl API. And lastly, we can get started with building one once we have all the required resources. So let's get started. So as we discussed before, we will get started by installing NodeJS into our operating system. So we're working on Windows operating system. So we might have to go ahead with Windows. So I'll choose Windows and the architecture will be the one which I'm working with. So that would be x64. But in case if you are working with a different operating system say Mac OS then you might have to select ARM or any suitable architecture that you can be compatible with for Linux you'll have to choose with the same and so on. So since we're working with Windows so Windows it is and X64 architecture and click on that Windows installer MSI file. So it will be getting started to install and you'll have to click on that Windows installer MSI file and it'll get downloaded. While it is getting downloaded, let's go to the next web page and download and install the cursor AI that we will be needing for this demonstration today. You'll have to login, create an account, and login. And automatically, it'll give you the version that suits our operating system. Since we're on Windows, it gave me a Windows operating system. Just sign up or login. So, since you've already an account, just log into your account and confirm it's that you and followed by that you'll have uh installed your cursor AI. Choose your theme. I'll go with the cursor dark theme. And here you make sure that you have cursor tab control k agent and you can also choose the data sharing option. I'll go with the privacy mode in case if you want the other one. Go ahead with that. And uh here you have a few review settings. And there you go. Now you're on the new window for cursor. And meanwhile let's check on the NodeJS installation. I think uh the wizard has already started installing the NodeJS setup. So uh this is the window. Let's quickly uh go through each and every instance that we have gone through so far in this particular window from the beginning. So it started off with some u packages that we would be needing. So uh yeah chocolatey and uh KB299. Yeah. Further we have and the last one is Python 13 right version 3.13.3 is approved so far so good I think we can now get started with the next step so here we are on the cursor AI so we might have to uh get started going through the cursor AI settings to create our new MCP so there are quite a few methods that you can try out to reach to settings one is you can go to the file and there you may have to choose the preference ences and then you have the cursor settings. You just can use that vertical list or you can go ahead with the search options as well. So you can use that step or you can also use the other step where you can just go to the search window and search for settings. Here you can see uh MCP on the left window. So let me also walk you through a few other steps. So go to the search window and uh type down cursor AI or cursor settings. I think there has been a discrepancy but not to worry we'll go with the regular approach where we'll go to uh the file preferences and cursor settings and there you have uh the options on the left hand side go to MCP and so far you don't have any MCP servers because this is the first time you logged into cursor AI so you just have to add new global MCP server which is available on the blue color button just click on that and now you'll have a MCP.json JSON file which is completely empty. You'll have to write down a few hard-coded codes here which are not too complex related to the earlier ones that you have worked on in A10. So you might need a few more u tools here. So go to the firecrawl website. Before that, let's go through some uh awesome MCP servers GitHub link. So here we have almost all information related to the MCP. So let's open a new window and open all these links. What is MCP? Clients, tutorials, community, legend, server implementations, framework, tips and tricks. Right? For further information you can go through all these web pages individually. So back to the homepage here you have everything you needed the basic information of what exactly is MCP and further you have some server implementations like if you scroll down so you have awesome MCP services. This is where the beginning is and next you have server implementations aggregators art and culture coding execution right customer data platforms. So here you have aggregators, art and culture dedicated web spaces. So and you have also cloud platforms that you can work along with. And moving further you have uh code executions, coding agents, command lines as well and communication related uh stuff. So you can go along and perform your operations, customer data platforms, data platforms and much more. Let's uh close all these windows and get started with the next stage where we need an API. Right? So for that uh the project that we're working on which is uh webcrawler, we will be needing an API from one of the uh providers which is fire crawl API. Okay, that's the one firecrol mcp. So that's the one. Click on that particular link here to generate an API. First you might have to sign up with fire crawl. So uh before we sign up let's go through the website of fire crawl from the beginning. So that's the template code that you'll be working on. And also here we have a wide variety of uh uh crawl options, media parsing options, smart weight actions, right? Reliability first and all those things. And also we have the pricing details. For now we'll go ahead with the free plan. And uh yeah, let me add down my details. my email address and you might also have to add a pass. So, let me type down something. Yeah, so the password is not too strong enough. That's a good thing to have a really strong website. So, uh that's a sign you need to have strong passwords and also make sure that you don't share your passwords and the API keys that you generate using fire roll. Now that my password is a little too longer, I think it should be access. Yeah, it's accessible and is a successful account generation. Now you need to check your email and confirm that u login credentials are right and everything. So once you are signed up let's go through another website for generating our API keys. So if I crawl API scroll a little bit. So API documentation. So here you'll find some documentation related to fireroll and all the details that you need to go through for API key generation. So fireroll mcp server. Yeah, that's the website that you're looking for. Firecrol MCP server. That's the one. And here you have overview content tools and other resources that you're looking for. Now this is the template server configuration that you'll be needing. So make sure that you have an eye on it. And now you will also have uh the details that you might have to go through like basically it'll have uh the steps that you need to follow in the content page. Click on content. There you go. So it has all the uh features and uh details that you need to go ahead with and tools. You have the crawling extraction scraping tool that you can work along with fire crawl and uh few comments here. Now yeah the email has not been confirmed yet. Let's try to resend and uh reconfirm the email so that we just sign up or login and create our API keys as soon as possible. So there you go. I have verified my email in my phone. So I'll just enter the credentials and sign in. Successful. You are signed in. On the left hand side you can see the overview playground extract u activity logs usage API keys and all the settings. So here you can see my API key has been by default hidden. So not visualize it now. So you have two options either view or copy. So we will be copying it for the first time to be on the safe side. So uh since we are new to this particular website so we don't have any activities registered on this particular web page. So now we have the API keys. Now let's go to the content page to verify the steps that we need to follow. So we need to install npm and we need to go to the cursor AI settings and add a new MCP server which we already did. So we just need to copy the template code and we just need to place it there. And also don't worry about uh where exactly to place your API keys in the template. We have that place. So we are back on the cursor AI. Now we will replace this template code with the code that we just copied. And remember, if you're using Windows, try cmd/ C. Okay, if you're not on Windows, you can eliminate that uh cmd/ C. Uh but if you are working in Windows, then you definitely need that cmd/ C. Okay, let's erase this and replace that. So I'll name my tool as fire crawl and I'll go to the new line where I'll write down my command and the command will be the code that I just copied from the fire crawl window. So let me type down the command in double quotes in new line and I'll paste the command here which is the code that we copied from the patrol window. Ctrl V. There you go. So the white stuff which is in cmd/ C. So that's the one you need to focus on. If you are working on Windows that you definitely need it. If you're not working on Windows then you can just omit it. Now uh there might be some concerns with the codes. Don't worry. Now to crossverify you can just go back to the firecrawl homepage and uh cross verify it with the template for configuration that they have uh made us available. Let's go back to the firewall window and at the same time you can see your API key here. So that's where you need to place your API key. So here we are on the back roll window. You need to copy the API key and place it in the place where they have mentioned your API key. So if you have cross verified with the configuration code that's fine and now you can just uh check the double quotes. So that's the one you need to focus on. Let me uh kind of uh if there is a way to zoom it in. Let me try to zoom in. Yeah, that's the one. CMD slash C. If you're on Windows, you definitely need this. If you're not on Windows, you can just choose to permit it. Now, let me just do a one last final check. I think I missed a double quote somewhere. Yeah, I need to remove it from this place and add it to the end. Just a second. Okay, remove the double quote at that place. Now, it should be after MCP. And once everything is done, you can just quickly uh choose to save and close this particular window. Check the distance between the API key and the double amp places. That's fine. And saving is really simple. Choose those three dots. Close. Save. And once that is clicked, it's all done. You get to, you know, choose to close this particular mcp.json file and get back to your agent. So once you have closed it, you will have your new server that you've just created in the cursor settings. But in a few times you get uh no tool connected, no server connected or there might be some errors. So in that case you just need to crossverify the npm installation, NodeJS installation and uh check all the files that are there or not. But even if everything is installed and you can see uh you know the versions available and if you're still seeing this or watching that particular error then don't worry just close the cursor AI and restart it or you can also choose the refresh option which is right available there. So I've closed and restarted it and the same procedure go to file preferences cursor settings and there you'll see the MCP models and there you'll find it right there. Correct. Now close it and go to the window where you'll find the uh server. Yeah, that's the one. And here you choose the agent. Make sure that you choose the agent instead of anything else. Not man, not ask. I choose the agent. And now I'll write down a simple prompt. Search for simply learn on Google. Now it should give me results which it finds on Google. Maybe it's a homepage. Maybe it's a social media handle like LinkedIn or YouTube or Instagram. anything that it finds related to simply done only the term simply learn. So here you can see it has given me three different results. First one is simply done website. Second one is simply done YouTube channel and the third one is LinkedIn page. So let's get started with the official web page. So click on the simply.com. Now yeah it's asking me permission open. Now we are on simply learn homepage. Similarly let's go back and select for the YouTube uh page. So that's the one. Open again. Get with permissions. So you're on the Simply on YouTube page now. I think it might ask for a login for LinkedIn. But anyways, let's try to check if it can open without logging in to LinkedIn or not. So yeah, you can see I've logged into the YouTube. Yeah, it is asking me to login. So let's quickly log in. It might take a couple of seconds. So there you go. I've logged into LinkedIn. Now open. So I can see Simply Learns homepage getting loaded in a second. Yeah, there you go. On LinkedIn, we have opened the Simply Learns homepage successfully. Open just dropped something huge. It's called Codeex. If you're writing code or just love tech, this is about to blow your mind. This AI is not just smart. It rewrites how we write code like for real. But here's the thing. Is all this hype actually worth it? That's what we are diving into today. We'll break down what Codeex is, how it works, the cool features that make it special, how safe and reliable it really is, and how it stacks up against other AI coding tools out there. So, if you're curious whether Codeex is a gamecher or just a fancy buzz, stick with me and let's get into it. All right, so what exactly is Codeex? Imagine having a supercharged AI developer in the cloud. It's like a team of expert who can juggle writing new features, fixing bugs, answering questions about your code all at the same time. Codeex runs in these safe isolated sandboxes loaded with your exact code base. So, it's working with your code just like a real teammate would. But what powers codeex? a special AI model called Codeex1. Trained on tons of real coding projects to produce code that looks just like a human wrote it. It follows instructions carefully, keeps testing until everything works perfectly, and even adapts to your coding style. Oh, and there's a cool fact. In OpenAI's own coding benchmark test called SWE Bench, Codex nailed up to 85% accuracy solving real world coding problems. That's a massive jump over other AI models. So, it's not just a good-looking code, it's a trustworthy code. Now, you might be wondering, how do I actually use codeex? Now, if you have got charg or enterprise, codeex is right there in the sidebar. You just type what you want. Maybe write me a login feature or fix this bug in the payment system and hit the code button. Easy. Want to ask questions about your code? Hit ask and get answers right away. Each task CEX handles run in its own secure cloud workspace. It reads your file, edits them, run tests, llinters, and checks type basically everything a human dev does. Task take anywhere from a minute to half an hour depending on how tricky they are. And you can watch the whole process live. When it's done, Codex commits the changes and gives you all the receipt, terminal logs, and the results, citations, and so you can easily see what it did. You can review the work, ask for changes, create GitHub pull requests, or merge directly. You can even set up a codeex environment to match your real development setup for smoother results. Wantex to work even better? Just add agents.mmd file to your repository like kind of a guide book telling Codeex how your project works, what commands to run for testing and your coding standards. Think of it like onboarding a new developer, giving clear instructions and Codex becomes smarter and more helpful. But even without that, Codex is already rocking in OpenAI's internal test. Talking about is Codex safe or not. Look, handing over code to AI can feel scary, right? But OpenAI built Codeex with safety first. It runs in logged on cloud containers with zero internet access, so it can't browse random sites or grab sketchy stuff. And Codeex shows you everything it does. Log test results. Even when things go wrong, you can always stay in control. No surprise, just transparency. Now, who's already using Codeex? This isn't just theory. Open engineers live with codecs. They offload boring stuff like renaming, refactoring, writing test, and fixing bugs so they can focus on the cool creative part. Cisco uses it to speed up features and untangle huge code bases. Superhum speeds up testing and test product managers do small code tweaks without bugging engineers. Codex helps team work smarter and faster, cutting down distractions and keeping projects moving. Let's talk about the updates to Codex CLI and development experience. If you love working with the terminal, OpenAI launched Codeex CLI, a lightweight open-source assistant that brings Codeex powers to your local workflow. They also made a smaller, faster version called Codeex Mini for quick QA and editing. Perfect when you want instant help. Signing is in a breeze now. Just use your charge GPT account, pick up your AP organization, and boom, you're all set. Plus and pro users even get free credits to start. Right now, Codex is rolling out globally to charge GPT pro team and enterprise users. Plus, and education users, you are next in line. For API users, Codex Mini cost around 1.50 per million input tokens and $6 per million output tokens with discounts if you reuse promps a lot. Heads up, Codeex is still in early days. It can't get process images or change course mid task and remote task. Delegation takes longer than quick edits, but it's evolving fast and will soon handle more complex work. What's next? The future. Open AI vision developers own the work they love and delegate the rest to AI agents boosting productivity like never before. So your chat with codeex mid task guide it get progress updates and assign jobs from your IDE GitHub or issue tracker. Imagine a smooth unified workflow where AI is your teammate across all your tools. Now how does Codex stacks up against other AI coding assistants? GitHub Copilot is amazing for realtime code suggestions while you type like a buddy sitting next to you. Codeex works more like a remote teammate that handles entire coding task asynchronously, writing features, fixing bugs, running tests, and submitting pull requests, freeing you up for bigger picture stuff. and GPD4. It's great at code snippets and explanation, but Codeex is trained specifically for software engineering, producing cleaner, more reliable code tailored to real projects. Together, these tools form an unstoppable AI coding dream team. But Codex's ability to multitask safely and transparently is pushing the limits of what AI coding can do. So whether you're a solo developer or a part at a team, Codeex is here to change the way you build software. It's available now for charge GBD Pro team and enterprise with plus and education coming soon. Try it out, play with different tasks and see how it speeds up with your workflow, cuts headaches and makes coding way more fun. Now if you like this deep dive, smash that like button, subscribe for more tech news, and drop your thoughts or questions about Codeex below. Welcome to the short course on generative AI models for beginners by simply learn. So in this course we will learn about the basic understanding of generative AI. Then we will move on to the C applications of the many Gen AI tools. And thirdly we will understand the Gen AI models and then we will create our first Gen AI model. And then we will move on and see the real world impact of the generative AI models. And then we will create a chatbot using the genai model and then we will conclude the course with the limitations of the genai models. And finally we will see the future of genai models. Now let us begin with the first chapter that is basic understanding of generative AI. So let us begin with what is generative AI. So generative AI is a type of artificial intelligence that creates new content such as text, images, music or even code from scratch based on what it has learned. So unlike the traditional AI which just analyzes or classifies data like recognizing faces or recommending products, generative AI creates. It's like the difference between a judge and an artist. One evaluates and the other one imagines and produces. So let's break it down with a simple real world examples. The first one is chippity that is a text generation. So what it does? So chat jeopetity like the one you are talking to right now generates humanlike conversations and answers. So you can ask it to write stories, emails, explain difficult topics or even debug a code. For example, you say, "Write me a bedtime story about a robot and a unicorn." The chart jubety creates an entire story for you on that particular spot. So let's move on and see Delhi that is for the image generation. So Delhi creates pictures from words that is the text props. Then artists and marketers use it to generate logos, illustrations or more without hiring a designer. For example, you type a cat wearing a space suit on Mars and Delhi draws it instantly. Now moving on to GPT3 that is text plus logic task. So what it does? So, GPD3 is a powerful language model that can create and write essays, summary books, translate languages, and even write computer code. So, developers use it in apps to create smart features like the AI writing assistant. So, then you give JP3 a blog title and it writes the full article with an intro, subheadings, and conclusion. So, after knowing all this, let's move on and see why are generative models. So generative models are changing the way we create content across multiple fields. So under the text field we are writing books, articles, reports or even poetry. And then we are assisting in the customer support with the chat bots. And then under the images section, we are helping the artist generate concepts and visuals. And then we are even handling the speeding up design and marketing content creation. And then under the music part, we are composing the background scores for videos or games. And then we are helping musicians brainstorm the melodies or the lyrics. And then finally we write the boiler plate load under this coding section debugging or even creating full web apps. So now moving on we'll see traditional AI versus generative AI. So let's compare both side by side with the simple explanations. So the main goal of traditional AI is to analyze and make decisions and the main goal of generative AI is create new content. For example, under traditional AI, predicting stock prices or recommending books on movies and the generative AI, we are writing stories, generating images and so on. So, traditional AI handles data. It uses data to classify or forecast. And generative AI learns from data to generate something new. The traditional AI, it labels, scores, and categories as output. and generative AI the text images videos and so on. For example, traditional AI is face recognition, fraud detection, spam filters and generative AI is sharp, Delhi, music LM and so on. So down the line in the next chapter we will see the applications of some generative AI tools like the chat GPT Delhi and GP3. So imagine this. You are working on a college project. It's late at night and the deadlines is tomorrow. You need a well-written report and a poster design and a clear presentation. So total chaos, right? But what if I tell you that there's an AI assistant that can help you with all of this in just minutes. So welcome to the world of generative AI. Today we'll explore how tools like chat jippity deli and jity3 are being used by the students content creators and professionals to do amazing things faster easier and smarter. So let's break down each tool and see how you can actually use them in real life. So this is chaty 4 if you're not aware of what chat chip is. So, Chad Jippety is an AI chatbot that can create, write essays, summaries, articles, explain topics, solve coding problems, and even conduct mock interviews all through one single chart. So, let's look at real ways you can use it. So, chop model, it uses this GPT4, the latest in the generative AI pre-trained transformer that is a GPT family. So GP4 is designed for more nuanced and humanlike conversation. So have a look at the real ways we can use it. So the first room we are going to try is explain the greenhouse effect like I am in class 12. So basically this prompt is asking for the greenhouse effect explanation and it is asking the charge to behave that it is explaining to a class 12 person. So we will give this. So you can basically see a humanlike generation just like here the greenhouse effect is a natural process that warms the earth surface. It happens when certain gases in the earth's atmosphere trap the heat. Here's how it works. It will have a detailed explanation on the solar energy, earth surface absorption, greenhouse gases, heat trapping and all. So after all these four points, it has also given without the greenhouse effect the earth would be too cold to sustain life. However, human activities like as burning fossil fuels, deforestation and industrial processes. So you can basically think it has given one nutshell and if you just understand this you have a clear vision of what a greenhouse effect is. So the first point we can see over here is that it is like a own tutor. So the first point we have just been to chat that is founded by chat.openi.com. We have typed a question and we have got a simple and easy to understand answer. And even you can have a follow-up question like if I ask is solar energy related to greenhouse effect. You can even have a followup question. Yes, solar engine is related to the greenhouse effect. So it's almost like its own tutor. So great for the late night cramming or the quick concept clarity. So this is it. And now moving on to the second thing that it can help with that writing assignments or essays in just minutes. So we will ask charge Jibbiti write a 350word essay on climate change for college purpose and you can see I have provided conditions. You can even customize it by saying make it simple and yet engaging and awesome. So it will basically create it has even created a title a growing concern for our future climate change. It has created paragraphs and basically you can even customize this word count and topic. Then review the generated draft. You can even personalize it to glide this personality and done and dusted. You can just have this. Now writing feels less stressful and it would be more fun. It can even give you an interview experience. For example, just imagine you are having your interview on the next Monday and you are preparing and you're very confused about what to prepare and what not. Then you can basically ask charge to behave like a interviewer for a particular job. I can even paste your job description from that particular company and you can ask that what are the probable questions I can have. So let's have a demo. Ask me three HR questions for a software development internship and suggest improvements. And we can even post this. and in no time it has started generating. Here are the three HR questions for a software development internship. The first one, can you walk me through a recent coding project you have worked on and explain how you approach solving the problem? And you know what this is a very very common question and it has given one tip also suggestion. This question evaluates your problem solving skills, your technical ability and how you approach development task. It gives the interviewer insight into your practical experience and thought process. So how do you manage the deadlines when working on the multiple projects or the task simultaneously? So this question assesses your time management skills and ability to prioritize the task. So the software development often involves juggling multiple assignments. So this gives the interviewer an idea of your organization skills. So this is very important. It has also written how do you keep yourself updated with the latest programming languages and technologies. So basically this question looks after your own learning ability. So the self-improvement part so it has even created one qu answer over it and it is also suggestions for the improvement provide specific examples highlight the soft skills prepare for the problem solving questions and so on and if I for example if I write customize it with a data analyst pressure role having no experience. What all the interviewer can ask me in the technical round and it will give you answer in no time. HR questions for a data analyst fresher. Why are you interested in pursuing a career as a data analyst and what are the steps you have taken to prepare yourself for this role? Can you describe a situation where you had to analyze data or solve a problem using numbers or data in your studies or personal projects? How do you approach learning new tools or technologies especially when it comes to data analysis software? And then we have this technical question. What is the difference between the structured unstructured data? What is data normalization? How comfortable are you with the data visualization tools? Can you describe a visualization you would use to represent sales data over time? Can you explain what a private table is when it is used in data analysis? And there are suggestions that you are showcase your learning journey. Focus on the problem solving skills. Prepare for the basic technical questions. Since you are a fresher, the technical questions may focus on the fundamentals like the Excel, Python, uh that is the libraries, panders and so on. So in no time it can even have a mock interview over here. So it can even create ideas over here. It can brainstorm you with the ideas available. For example, you have a group project and you want the idea. I'll type over here give five creative ideas for a group project on sustainability. And if you post this in no time it has created these five ideas sustainable packaging design challenge objectives design are sustainable. It has created one objective. It has given one approach for you and one outcome. Then it has ced community based urban garden and for each topic if you see for each idea if you see it has created the objective the approach and the outcome. So these projects can not only be educational but also impactful. So you never run out of inspiration from chart dippity. So chip is a lot and here if you can actually see it has various search deep search and create image and you can basically do everything you want from Jabit. Let's now move on to Deli. The next we would be seeing is Delhi. So Deli is an AI that creates images from text. You tell it what to draw and it generates the artwork, logos and visuals. So you would be getting Deli from the strat GP only and in the explore GPTs in the sidebar you have to just mention Delhi 2. So there are various versions of Delhi and we'll start chatting. And now if you close this sidebar you can basically see Delhi too. So Deli is an AI that creates the images and Delhi this model uses a version of the clip model that is a contrastive language image pre-training combined with the VQ VA2 or other generative image models. So Deli creates images from the text descriptions generating highly creative visuals based on the natural language input. So let's see how you can use it. So for example we you're going to design when the college poster design. So instantly it would be done. So we would write create a poster for a college fest with DJs, neon lights and a crazy crowd. Okay. And you can basically give the prompt and in no time it will create one great image for you. You can write the and you can see it has created a great a great image. You can basically download this image and do anything. Here's the poster for your college fest featuring a vibrant DJ scene with the neon lights and the energetic crowd. So forget Photoshop, let AI do the heavy lifting. So then you can create presentations over here adding better visuals. So I'll write draw a cartoon showing water pollution in a river. And and you can basically see this. Now you can insert it into the PowerPoint or Canva and design it on your own. It acts as a vector. It acts as icon and anything you want to add. Then you can instantly create club logos and branding. So you can even generate a logo for over here through Deli too. logo for a coffee club with books and steaming mugs and you can see it has created a drastic logo and it feels like real. So you have just given one prompt and in and get four to five variations of logo. You can even ask it for a different version. So great for the student clubs or college events. Then you can even have it for the costume design or any ideas again. So, we'll draw futuristic costume design for uh sky five play with glowing elements. And even you can see here it has created a futuristic costume design for a scientific fiction play with the glowing elements and it is very amazing. The qualities of the images are of high quality and use vivid descriptions use more detailed prompts and you will get a great great output from Delhi too. So art students and theater class this is your new secret weapon. So let's move on to GPD3. Our next Chennai tool is GPD3. So GP3 is the powerhouse engine behind the tools like chat GP. So it doesn't just chat. It can write full code, research summary papers, business emails and more. So GPD3 is a predecessor of GPT4 based on the same GPT architecture but with a 175 billion parameters. It was one of the most advanced language models when it was released. So let's explore how it helps in the real world task. So initially we can use chart GPD because it is a chart GP4 version. There is a GPD3 is basically a predecessor of GPD4. So I'm using chart GP over here. You can also use chat GPT4 to understand the GP3. So the first one is building websites without coding. So you can even write write a HTML for personal portfolio with my name, bio and contact form. And in no time it has generated this. You can just copy it, paste it in VS code or any of the code editors you like and you can definitely get a proper output of this. Perfect for the beginners or the design students. Then you can even summarize a lengthy research paper. You can have to just upload the paper and if you see this plus section you can just click on this you can just upload from their computer and summarize it in 2 seconds. It can even brainstorm you with the project and the startup ideas like suggest five unique app ideas for college students. Now you can see the first one is study buddy finder help students find partners based on their courses schedules and many preferences and you can even see campus event hub course material exchange mental wellness check-in foodie finder and so on. It has even created bullet points for better understanding. So it is a noisefree study timer. use it to kickstart your next hackathon or competition or anything. So I'm not dragging it more because as I have already showed you the use of charge GPT. So that's it. Moving on to the next lesson of understanding the generative AI models. Imagine you are teaching a computer to be creative like painting a picture, writing a poetry or designing a dress. That's exactly what generative AI models aim to do. So learn from the data and generate new content that looks sound or feels real. But how do this pull off? Let's break it down into the three major families of models. The first one is GANs that is the generative adversarial networks. Then the second one we have is VA is a variational autoenccoders. And the third one, the final one is transformers like the GPT. So let's understand this in a detailed one. The first one is generative adversarial networks GANs. The artist versus the critic game. So how it works? Think of GANs like a competition between the two neural networks. The generator is like a rookie artist trying to create a fake art. The discriminator is the tough critic trying to tell the real art from the fake. So the artist creates random images. The critic says fake or real and the artist learns to get better. So over the time the fake images become indistinguishable from the real ones. So there's a real world analogy. It's like a student that is a generator forging a signature and a teacher discriminator spotting it. As both improve the forgeries become nearly perfect. So there are certain applications. The first application is creating realistic human faces. Then style the transfer in the art. And then we have fashion design prototypes. And then finally we have deep fakes that is controversial but technically fascinating. And then let's move on to the challenges. So training instability that the second one is it requires a lot of data and computing. So moving on we'll understand variational autoenccoders that is the VA the data compressor with creativity. So VAS are like the compress and the rebuild systems. They take input data like an image, compress it into a lowdimensional space that is called a sleen space and then reconstruct it. But here's the twist. The VAS don't just remember, they imagine. So by sampling different points in the latent space they can generate new similar looking content. So let's move on and see the applications. First application is anomaly detection. So what doesn't fit in the normal data. Then the second one is generating handwritten digits and faces. And then we have data compression. And then finally we can see drug discovery that is generating molecular structures. So now let's move on to the strengths. So more stable than the GANs and it gives a well structured latent space for the interpolation. So now moving on to the third model we have transformer models that is a master of the sequences. So unlike the GANs or the VAS which focus more on images transformers shine in handling sequences especially the text the code data idea is attention mechanism a way to let the model focus on the different words when generating a sentence. So for example when GPT generates a sentence I like ice cream because it's sweet. It pays attention to the context understanding how because relates to the ice cream and sweet. So let's now move on to the applications. The first one is chat bots like the chat GPT and then we have code generation that is the GitHub copilot and then thirdly we have language translation and finally we have summarization and content creation and then let's move on and see why transformers stand out. So the first one is they scale data well. The second one is they can be fine-tuned on specific task like the medical summarization or legal drafting. Then finally we have they handle the long range dependencies better than the older models like the RNN or the LSTMs. Then we have a comparison and the use cases. So the first one is GANs and it is best at image generation. And then we have an example used that is the face synthesis and the associated strength is hide realism and the weakness is it is hard to train. Then we are seeing the VAS and it is best at data compression plus it is also good in anomaly detection. So it is used as a Latin space exploration. And then coming to the strength it is very stable training. And then coming to weakness it has a less sharp outputs. So then coming to the model type that is a transformers and it is best at text and language and then we have an example use that is a chargeity and summarization and then we have strength that is powerful with a large data and then we have certain weakness that is expensive to train. Now moving on let's clear something which to use and when. So if you don't know which generative model to use for word, it can mess things up fast. So let's say you are building an app that needs to write emails, make pictures, answer questions, and turn speech into text. If you use the wrong model for the wrong job, your results will be poor, things will be slow, and you will waste time. So let's understand which models performs what. So for a sharp realistic images we are using GANs and then coming to the next for variation and latin space analysis we are using VAEs and then finally for the language or the textbased task we are using transformers. So finally how do these models actually work? So the GAN training process is the first process is generator makes the fake data. Then the step two is the discriminator tries to classify the real versus fake. Then the third step is both get better at their jobs with each round. So the final goal is to fool the discriminator with the realistic outputs. So the transformers under the hood pays attention mechanism. So it helps model understand which words matter more. Then we have self attention that is allows the model to look at all the words in a sentence at once. And then we have positional that is encoding and it helps track the orders of the words as transformers don't read left to right like humans. So this is why models like GPG4 can summarize, translate and generate in text so well. So understanding the differences between these generative models is like knowing which tool to use for which creative job. So GANs are your digital painters, VAS are the smart compressors with imagination and transformers are your text wizards. So down the line we'll see how to create our first generative AI model. In this chapter we will be creating our first generative AI model. So the objective of this lesson is breaking down the steps of building a generative AI model using a pre-trained model. So we are here not training a model from the scratch. So instead we will fine-tune or directly use a pre-trained model. So this makes it fast and beginner friendly. So if I tell the steps in nutshell it will be we will be first choosing a pre-trained model for example GPD2 because it is lightweight and good for beginners. The source from which we will getting the model is hugging faces model hub and then we will be setting up the environment in Google collab as you can see. Then we will be installing the libraries the transformers torch and then loading the pre-tuned model and the tokenizer. Then we will be writing a prompt and generating a text. So here we will be using the pipeline API for simplicity and parameters like max length and temperature. So let's begin. So here I am inside this Google collab and I have chosen a new notebook to begin with and then I'll just insert exclamation pip install transformers and shift it and it may take some time and you can see it has actually installing all the required transformers. So pip install means install this package in collab and transformers is a library by the hugging phase. So it gives us access to the powerful AI models like GP2 bird and more. And then we will see we will install from transformers import pipeline and then we will be using the loading generator GPD2 text generator. So generator equal to and you can see pipeline text generation and tab. So we'll execute this and basically this pipeline is a shortcut tool. It hides the all the complex code needed to run the AI models. So here the text generation tells like hey I want a model that can write text for me and GP2 is the actual model name. It was trained by the open AI to predict what word comes next in a sentence. And then basically you can see that it has model seven densers 100% has loaded everything and and the storage is also enabled for this repository. and then we'll begin with the prompt. Okay. So let's give the prompt and let's keep it very simple. prompt equal to once upon a time in a world full of AI com and then in the next line I will be writing result is equal to generator and we'll passing the prompt inside it and then we'll be giving the max length one parameter and I will be specifying it as 100 and I will be giving the number return sequence number return sentence is equal to one and I don't require this. So I will be directly printing the result. the first one, the first doc will print and then we'll be passing the generated text and I will run this and so the prompt I have given this is a starting sentence you give to the AI like saying hey start a story with this and this and this. So max length is equal to 100. So this particularly means generate up to 100 words or characters after the prompt and then you can even see I have given this num return sequence is equal to one. This is asking for just one version of the result. So you can basically ask for two three versions. I have just asked for only one and then the result you can see and in the third bracket we have given a zero and the generated text it extracts the generated text from the result. So you will see the AI write a continuation of your prompt basically. So that's your first generative AI output. You can basically see once upon a time in the world of AI, your only limit was when you could kill monsters, but now you can for the first time ever. The new monsters are in the battle in the blood of elves. Now, not the usual black and white battles that may you have experienced with the past couple of years and it has created a great one. So you can even see the power of generative AI. It has the power of completing your text, your prompt. So then let's write result equal to generator. And if I want to write prompt, comma enter then max length I will again mention equal to 100 then enter. I want the num return sequence equal to 1 and have to put a comma over here. Okay, so don't forget this guys. and then temperature is equal to 0.7 comma top. Okay, don't worry I'll explain one by one as I write equal to 50 then top P = to 0 95 then I'll write do samples else equal to true basically a boolean value. So then I'll after this I'll print the result. print result and in third brackets will the first one and then again third brackets will give in generated text and then we're basically going to shift enter. So here basically we have parameters like temperature. So temperature what it does it controls the creativity the lower is equal to safe and higher is the wild and creative is 0.7 medium so this top k top_k is equal to 50 so pick from the top 50 likely next words instead of just the top one then we have this top_p is equal to zero it is the nuclear sampling that is a more natural randomness Then we have do sample is equal to true that is enables the randomness. So without this the output is too predictable. So once you run this code you will see something like once upon a time in a world full of AI and with every advancement in AI machines there has been a change in the way we think about the world and we need to make sure that we can make sure that we can make sure and it has repeated it so many times. So every time you run it, you might get a slightly different result. So over here you have used the hugging faces transformers library to load the GP2. Then you wrote a prompt and then the AI continued it using its training. So you called it so you did it at without any complex machine learning setup inside the Google Collab. So this is your very first generative AI model. In the next chapter, we'll be seeing the real world of impact of generative AI models. So, generative AI is not just about another buzz word. It's fundamentally changing how businesses operate, solve problems, and serve customers. From generating product descriptions to designing aircraft paths, generating AI is stepping into roles that once required human creativity and intelligence. So in this session we will explore how industries across the board are using generative AI in meaningful and practical ways. The first one is healthcare that is a faster diagnosis and drug discovery. So in healthcare generative AI is helping doctors and scientists make faster decisions. The first one is medical imaging. So AI can analyze medical images like the X-rays or MRIs and create reports to help doctors quickly identify the problems. For example, a hospital uses AI to write reports for doctors, cutting down the time spent on each patient. Then we have drug discovery. So AI helps scientists discover new medicines faster by predicting how proteins fold which is important for the creating drugs. For example, a company used AI to discover a new drug in just 18 months which usually takes much longer time. Then moving on to the next one we have finance that is a smarter risk management and report generation. So in finance AI is making it easier to analyze data and detect the problems. The first one is automated financial reports. So AI can create financial reports based on data saving time for analyst. For example, a hitch found uses AI to write the market reports in minutes. Then we have fraud detection. So AI helps bank detect fraud by generating the fake transaction data to test their security systems making them more reliable. So for instance, Mastercard uses AI to create realistic transaction data to improve their fraud detection system. So now moving on to the third, we have retail and that is the personalized shopping experience. So retailers use AI to create personalized shopping experiences for customers. The first one we have is a product descriptions. So AI writes product descriptions that are customized for different customer groups such as casual descriptions for young buyers and formal ones for the professionals. For example, a clothing brand uses AI to automatically write descriptions for thousands of products. And then we have virtual tryons. So AI lets customers try on clothes or makeup virtually to see how it would look on them before buying. So for instance, Sephora's app uses AI to suggest makeup and lets users see how it looks on their face using their phone camera. So now moving on to the fourth one we have manufacturing. There's a faster design and maintenance. So AI is also making manufacturing more efficient. The first one is AI generated designs. AI helps create lighter, stronger parts for products, reducing waste and material cost. For example, an airplane company used AI to design parts that were 50% lighter but just as strong as a traditional designs. Then moving on, we have predictive maintenance. So AI helps factories predict when machines will need maintenance, reducing downtime and improving efficiency. For example, a factory uses AI to automatically create maintenance schedules for machines based on their performance data. So now moving on to the fifth one, we have education that is a personalized learning for students. So AI is making education more personalized and accessible. The first one is AI tutors. So AI powered tutors can help students with the homework by guiding them step by step through problems. For example, a student stuck on a math problem can get hints and explanations from an AI tutor available at any time. Then we have automated feedback. So teachers use AI to create quizzes and provide feedback on assignments saving them hours each week. For instance, a teacher uses AI to review essays and provide comments in just minutes instead of hours. So now moving on, we have sixth one is entertainment that is creating content at a scale. So in entertainment, AI is being used to create content faster and cheaper. First one is script writing. AI can help writers come up with ideas, generate dialogues, and even create story boards before filming. For example, a filmmaker uses AI to plan scenes and create visuals before they start shooting save time in the pre-production. So then we have virtual characters. So AI can create virtual characters that speak and act which is useful for movies, games and social media contain. For instance, AI is used to create virtual influencers who interact with their followers on social media. So now moving on down the line we will see how to create a chatbot in Google collab. So before we dive into the code let's first understand what the hugging face transformers library is. So the hugging face transformers library is one of the most popular libraries for the natural language processing and it provides the pre-trained models that can perform a variety of task like the text generation, text classification, name entity recognition and more. So here in the code we will be using a model called dialog small which is a variant of GPD2 designed for conversation generation. So the direct GPD was trained on the large conversational data sets making it a perfect fit for building the chart balls. So let's now build the setting up the environment. So for this we will install pip install transformers. So this line basically installs the transformers library. You need this to work with the pre-trained models provided by the hugging face. So this will allow you to load the models like the dialog GPT. We will be using here a conversational AI model and use it to generate the chat responses. And then and here we will begin with importing the necessary libraries from transformers. Import the auto model for the casual LM auto tokenizer import. And then we will run this. So here we are importing the necessary components like the automodel id for the casual lm. So this class basically loads a pre-trained model for the casual language modeling which is used for the text generation task. So we will use it to load the dialog GPT model and the auto tokenizer the tokenizer converts the text into tokens tokens which are known as numbers that the model can process and then converts the model's output back into the human readable text. So the torch is then PyTorch is a deep learning framework used by the model. It's used for the tensor manipulations as models rely on this tensils for the input and output. So let's begin. Then we will begin with the loading the model and the tokenizer. So here this converts the raw text that is the user input into tokens into that model understands. So each word or phrase is mapped into the unique ID making it easier for the model to process and generate a response. So then model is that pre-trained dialog small model is loaded over here. So this model has been trained on a large corpus of dialogues which enables it to generate the relevant responses in a conversational manner. So then we will begin with the chat initialization. And before beginning with the chat initialization, we will see that the tokenizer config JSON is 100% complete. And the best part of collab is it will give you all the status of how it is complete and for which particular merges do.ext.work.json which is completing and what problem is there. So let's just run this and let me explain. So chat history ids. So this is a variable where we store the history of the conversation. So this helps the model maintain the context across multiple user inputs. Then we have having a print statement that start chatting with your AI. Type quit to exit. So this provides a welcoming message to the user to start chatting and gives the exit command. So then we will begin with the chat loop that is the main logic of the chatbot. So then we'll begin with this chat loop logic. So we have written for step in range five user input is equal to input and then u and then semicolon and then a space and then we have written if user input dot lower is equal to equal to quit then I have to break it. break the loop break. So then I'll run this and this section of the code basically uses input to get the user's message and exit condition. If the user types quit the loop will break and the chat ends. So let's see and here basically you can see I have written hey and still it doesn't break. So let's write quit over here and let's see what happens. So you can see that it has stopped generating means it has got out of the logic. So here we have this encoding user input. So encoding the user input is converted into token ids using the tokenizer dot encode. So the EOS token that is the end of sequence token is added at the end of the user input. This tells the model that the input has ended and it prepares for generating a response. then tensors. So this is the return underscore tensors equal to pt. That part ensures that the encoded input is returned as a pi torch tensor. This is the format that the model expects. So we will run this and then we will be moving on to managing the conversation in history. So here we are having this managing conversation history and it is up to maintaining the context. So we want to keep the track of the conversation history so that the model can respond in context. So if there's previous chat history that is the chat history ids it is concatenated with the new input allowing the model to consider the entire conversation so far. So if there is no previous history that is the first user input the new input ids are used directly. So then we will be moving on to generating a response. So here we are concentrating on generating a response. Chat history ids is equal to model generated bot input ids. Max length equal to,000 and p token id is equal to tokenizer eos token ID. So model response. So this part sends the concatenated input to the model that is model.generate generate and generates a response. The maximum length specifies the maximum length of the generated text. So in this case it is set to th00and tokens. Then we have pad token ID that is ensures the padding tokens are handed correctly. That is the model needs to know which tokens are just padding. Then we will be moving on to the decoding the response. And here we are seeing response is equal to tokenizer dot decode chart history ids. This is a board input ids dotshape and everything. So basically decoding this generated token ids are converted back into the human readable text using the tokenizer do decode. Then we have skip the special tokens. The skip special tokens is equal to true. It ensures that any special tokens like the EOS are not included in the final output. So we will run this and then finally we are actually printing the response that is displaying the response. So the print just generating the response is displayed to the user. So we'll run this and you can see but I'm so sorry. So what happens when you run this code? You initiate the chat and the board welcomes you to start chatting. So every time you input something, the board takes your message, encodes it and sends it to the model along with the conversation history. The model generates a response considering the entire conversation so far and the bot outputs that response. So this loop continues until you type quit ending the conversation. So building a chatbot is essential because it enhances the user engagement by providing the real time personalized interactions making it more dynamic and the userfriendly compared to the static content. So chat bots are available 24 by7 ensuring the users can get immediate responses at any time. So by automating the repetitive task like answering common questions, processing orders or providing basic customer support, chat bots free up valuable resources allowing human employees to focus on more complex task. So then additionally, chat bots can improve customer satisfaction by quickly resolving the inquiries, reducing the wait times and enhancing the overall user experience. So implementing the context management allows the bot to remember the previous interactions and sharing a continuous and more personalized experience for the user. So finally enabling the multi-turn conversation handling means the chatbot can engage in back and forth dialogues rather than only answering the isolated questions. So making it feel more conversational and humanlike. So these features collectively help create an intelligent, efficient and engaging chatbot that can significantly improve user interactions and streamline the business operations. So you can even fine-tune your own model. So this could be your own data set to make it more specific to a particular domain or a use case. You can even make it worth handling the longer conversations so that the it remembers the chat history for a long time. So guys, you have made your first chatbot and down the line let's get improved. So in this lesson we will understand about the limitations of generative AI models. So the first one is lack of understanding. So AI models generate contained by predicting what comes next based on the patterns in data but they don't truly understand the content. For example, when an AI creates an article or a code, it doesn't know what it's writing. It's just mixing patterns based on the past data. Then moving on to the dependence on the data quality. So the AI's performance is only as good as the data it's trained on. So if it has biased or incomplete data, it can generate biased or inaccurate results. Think about biased hiring algorithms or flawed healthcare recommendations. Real issues caused by the bad training data. So now moving on to the third one, we have creativity limitations. So AI can produce impressive creative outputs like artwork or music. So however it struggles to innovate or produce truly original ideas like a human artist would. It's more about remixing the existing content rather than inventing something groundbreaking. Now moving on to the next we have ethical concerns. So AI models can sometimes generate harmful content from offensive language to deep fakes that are hard to distinguish from the real footage. So this poses risk for misinformation and ethical dilemmas especially in media and entertainment. So now moving on to the next we have context and nuance. So generative AI can miss the subtle nuance in conservations or emotions. For instance, if you ask the model to help with the sensitive topics like mental health, it might offer an advice that's generic and more tailored to the individual's emotional needs. Next, we have cost and resource intensive. So training a generative AI model requires huge amounts of computing power, time and energy. So these models often run on the powerful servers consuming a lot of resources which raises concerns about the sustainability and the environmental impact. So now moving on to the next one, we have limited problem solving capabilities. While generative AI is great at task like writing essays or summarizing text, it's not well suited for the task requiring the deeper reasoning or the complex problem solving. It can simulate problem solving but it doesn't truly understand the solution in the way humans do. Now moving on to the next one, we have risk of over reliance. So with AI generating more and more content, there's a risk of becoming too dependent on the technology. So people might rely on AI for the task that require human expertise or critical thinking which could hinder the personal creativity or decision making. So now moving on we have data privacy issues. So AI models can sometimes generate content based on the personal data raising concerns about privacy. So this is especially problematic in the areas like healthcare where sensitive data can be inadvertently shared or misused. So now moving on to the next one we have generalization challenges. So generative models are great at specific tasks but struggle with the broader generalization. For instance, a model trained to generate images of the cats may struggle to create the convincing images of a completely different animal showing that AI is limited in its flexibility. Now moving on down the line we will understand the future of genai models. The first one is improved understanding and comprehension. So the future of generative AI will likely see models that can understand the context more deeply providing more relevant and meaningful outputs. So we might see AI models that can truly comprehend the meaning behind the responses rather than just spitting out the data. So next we'll move on to the enhanced creativity. So as AI models evolve, we can expect them to push boundaries and become more innovative. So with advancements in the techniques like the reinforcement learning and neural architecture search, we could see AI producing truly original content potentially revolutionizing fields like the art, music and literature. So now moving on, we have more ethical AI. So in the future, we will likely see more regulations around AI usage to prevent the harmful outcomes. So AI companies are already working on improving the transparency, reducing bias, and making their models more ethical and responsible. So now moving on, we have better personalization. So the future holds AI that can offer more tailored personalized experience. So imagine an AI that truly understands your needs and preferences. So providing you with the content, products or services that feel personalized and relevant. Now moving on to the next one, we have energy efficiency. So as the demand for the generative AI grows, researchers are focusing on creating more energy efficient models, we may see smaller, more powerful models that can run on less computational power, reducing the environmental impact. So next we have human AI collaboration. So rather than replacing humans, the future of generative AI models is likely to be centered on collaboration. So we will see AI helping people be more productive, augmenting creativity and providing insights that humans can use to make better decisions. So now moving on to the next we have integration with the new technologies. So generative AI will continue to integrate with the new technologies like augmented reality that is the AR then the virtual reality that is the VR the blockchain and and opening up new possibilities for the immersive experiences and the decentralized applications. So now moving on we have solving complex real world problems. So in the near future, we could see AI models working alongside scientists to tackle the big challenges like climate change, pandemics, and global food security. So AI could help analyze data, predict trends, and offer innovative solutions to this pressing problems. So now we'll see the next one. We have more accessible AI. So as technology advances, AI will become more accessible, tools will be easier to use and people from all the walks of life, not just the technical experts, will be able to harness the power of AI to create and innovate. So while generative AI models are still evolving, their potential to transform the industries, enhance creativity and solve the complex problems is undeniable. But it's important to address their limitations and work towards creating more ethical, efficient and responsible AI systems. So the future is exciting and we are just getting started. So guys, congratulations on completing the Gen AI models for beginners course by simply learn. You have now built a strong foundation on the generative AI models by learning what generative AI was, how it impacted the real world, then creating chat bots, creating your first genai model, and then finally closing it with the future of genai models. So as you move forward, continue experimenting with your own data sets and refining your own pipeline to unlock the full potential of Genai in creating smarter context aare genai systems and models. In this video, we will walk you through how to set up and use OpenAI's powerful language model using Python. Whether you are a beginner or just looking to refresh your skills, you'll learn how to get your API key, configure your enrollment, write your first script and interact with models like GBT40 mini, GBT4. So guys, if you want to learn more about it, watch this video till end. So guys, let us start with first understanding what exactly is LLM. LLM stands for large language model which is a type of artificial intelligence trained on vast amount of text data to understand and generate humanlike language. These models such as OpenAI's GPT or Midas Llama use deep learning techniques to analyze context, predict the next word in a sentence and also provide intelligent responses to text inputs. Unlike traditional rule-based systems, LLM learns patterns, grammar, facts, and even reasoning skills from the data they train on. In simple terms, LLM can read and write text almost like humans, making them incredibly powerful tools for building intelligent applications. Now, let us move ahead and try to understand some of the real world applications of NL. So the first one are chat bots and virtual assistants. Apps use LMS to power smart conversation with users. Whether it's customer support, booking assistance or mental health guidance. LM help deliver humanlike helpful interactions. The next one we have all over here is content generation. From blog intros to product descriptions, LM can generate content at scale, making them valuable for marketing apps and writing tools. Next, we have summarizations and insights. Apps like news aggregator or legal tools use LLMs to summarize long documents into digestible insights. Next one, we have code generation. Developers focus to use LMS to autogenerate code snippets, help debug errors or explain programming concepts. And finally, we have language translation and correction. Language app leverage LLMs to provide realtime translation, grammar correction, and learning suggestions. Overall, LLMs enable apps to understand and respond to user inputs with remarkable fluency, making them central nextG user experiences. Now let us discuss about some of tools and framework from where LMS can be made. So guys depending on your preferred programming language and project type here are some of the key. If I talk about Python guys it is widely used for quick prototyping with LMS libraries like lang chain transformers and openai make it easy to connect with LM APIs. If we talk about NodeJS guys, it is ideal for web based apps and backend services. You can use packages like OpenAI, Axios or Lchain. Now let us discuss something about lang chain. So lang chain is actually a popular framework which simplifies working with LMS. It handles prompt templates, chaining multiple steps, memory management and even data quality. And you could easily integrate with the front-end frameworks like React, Vue or Flutter for building the user interface that interacts with LLM via APIs. So guys, now let us set about environment. But before that, you need to make sure that you have Python installed on system. So I'm basically using Windows operating system and I'm at command prompt. So I will just type Python V. Check the version of Python. So guys, as you can see, I have already installed Python in my system. But if you don't have Python on your system, then you need to install it. You can watch our simply learns video where we have installed Python on Windows operating system. Now after that, what you need to do is guys, you need to run this bash script. Now guys, let us create a virtual environment. So for creating a virtual environment you can type python /m vv lm env. This is the name of our virtual machine. Just right click on this. Okay. Now let us activate our virtual machine. Now after that what you need to type is llmv the name of your virtual environment/crypt/activate. Now this will enable your virtual machine which is llm. Now after this what we have to do is we have to download one of the important libraries. So let us download it. So guys I'll tell you the one of the most secure way. So you'll be using API keys of chat gb over here. So I'm not using but still I would suggest for security reason to always create av file. So for the same purpose what we are going to do I'm going to install openAI Pythonv. So this package will help you manage your API key very smoothly. Just right click on this and it is going to install the env package. Now after that let us set our API key. So guys go over here. So create a new secret key. Let us give it as name as lm test. So you have to go on platform.appi/ API/ keys. So this is the link where you will create your secret key. Now you create your secret key all over here. Copy this. Okay. And just add in your AM file in your project structure. So guys after copying that what you need to do is you need to set your API key. There are two ways to do it. One is very simple way which I would not suggest it like just create a variable and paste your secret key. The next is using env. So by creating av file and setting your variables and then pasting your given API key that is one of the good. Now let us go all over here. Just copy this. Now guys, after creating this variable, just paste your given API key, right click on this and our API key as now guys, what you need to do is open a notepad and this is just for simple demonstration. You just type this code. Now let us discuss about this code for a minute to understand what is going on all over here. So guys as you can see all over here I have imported open AAI and I have also imported operating system. Okay. So this uh OS module open AAI repeat. So guys this open AAI Python library you can use it with the open AAIS API. Then we are also importing OS module to interact with the environment variables on our system. Now finally what we are doing is open API API key equals to os get env and open API key. So this line gets your open API key from an environment variable called open API key. Okay. And uh and it also sets it to the library who knows how to authenticate your request. You need to have this key set up in your system environment variables before running the code. Now next we have response equals to openai.comp completions.create and here I have added my model. So if you are not having paid account then it will be bit troublesome for you because there are different kind of models and like 40 basically comes at a charge prices. So get your plan upgraded and your billing plans. So because based on that the number of tokens are there in that way your API call is going to happen. Now here you could see I have written the messages as role system content you are helpful assistant role user content hello can you assist me using mini module. So basically what we are doing all over here is I'm calling the openi chat completion API to create a method. First of all, I you know specified my model which is basically optimized for faster responses for many and then there is a message parameter which simulates a chat conversation. The system message sets the assistance behavior like you are a helpful assistant and there is a user message which is asking the model hello can you assist me using the GPT 40 model. So basically this prints out the content of the first response message generated by model and the API returns a list of choices usually just one and you can access the message text from the first choice. Now this script basically sends a chat message to the OpenAI GPT4 miniodel and prints the model reply. It uses your API key stored in the environment to authenticate and perform the request. So this is how basically this is happening. I hope so guys you would have got a brief idea regarding this code. Okay. Now after this you need to go to your designated folder. Okay. So I have saved my given file in C users SLP13089. Now going to documents. Okay. So inside this I have this folder called app. py. Now I'm going to run the given file. Now just right click on this and uh let's see what happens. You can see we are getting some error. Okay, you could see the message here is code is 429 and my kota is exceeded. Okay, I need to upgrade my plan. And this is generally going to happen if you have not upgraded your plan because a number of API calls are already set. But this is something very much feasible. Now let me tell you what will be the output of this program. So you could see all over here that in the user it's asking the content as hello can you assist me using GT4 model. Now the answer would be yes I can definitely assist you using the GBT4 mini model. How can I help you today? So that is a kind of a simulated reply that we are going to see in our given uh output and this is a very simple way of integrating LLM model. So in this way you are going to get the output. Okay, I hope so you would have got a brief idea how you could integrate LLM models into your given project. So that is one of the way. Now let us move ahead and discuss about some of the more key concepts all over here. Now let us discuss about designing effective prompts. Crafting a strong prompt is key to unlocking accurate, relevant and context aware responses from large language model. And here are some essential guidelines to help you write better prompts. First of all, be clear and specific. Vague instructions often lead to generic or confusing answers. So instead of asking explain AI, a clearer version would be explain artificial intelligence in simpler terms for a high school student. This not only narrows down the scope but also defines the complexity level and target audience helping the model tailor its response accordingly. Next one we have define the role or tone. LMS can adopt different styles or personas based on your grommet. For instance, saying you are a career coach, give advice to a fresher looking to enter data science will lead to more structured and empathetic guidance. You can also adjust tone, formal, casual, humorous or technical tone based on your app's use case. Next one we have is called providing context. If your prompt depends on previous information or has multiple parts, provide that background clearly. For example, given this réumé summary and job description, write a tailored cover letter in 100 words. This example helps the model understand relationship between inputs and generate more relevant. So guys, keep this mind while you are designing an effective prompt. Now let us discuss some of the effective prompting techniques. First one we have is zero short prompting. Now here you give only one instruction with no examples. Like for example here you will say translate the sentence to French. How are you? Okay. This is great for simple or well-known task. So that is called zeros short prompting. Now let me tell you one more prompting technique which is called as few short prompting. Now here you have to provide one to three example to set the format or tone. Like for example English hello to Spanish hola then good night to unus basically you say hello and then Spanish you add. So this helps guide the model and also it improves the consistency. Now the final one that we are going to discuss that is called chain of thoughts. Now prompt the model to think step by step. So this is all about chain of thought. For example, if you say like if a train leaves at 3 p.m. and travels for 2 hours, what time will it arrive? So let's think step by step. This is ideal for reasoning heavy task like math, logic puzzles or multi-step questions. So guys, mastering this techniques will make your LLM based app more intelligent, reliable and it will be tailored to user needs. Now let us discuss about future scope and advanced topics. So as you grow more confident with building LLMs, there are several advanced techniques and future ready capabilities you can explore to build smarter, more specialized applications. These methods help overcome limitations of generating LLMs making them more accurate, efficient and customized for your specific needs. Now let us discuss about these parameters because these are the future scope which could help you improve your element. The first one we'll talk about finetuning versus prompt tuning. So finetuning basically involves retraining based model on your own data set. This is useful when you want the model to understand industry specific language or behave in a certain way consistently. However, it's resource intensive and usually requires large data sets and GPU support. If I talk about prompt tuning guys on the other hand, it's kind of lightweight. It focuses on crafting or learning optimal prompts often called soft prompts without changing the underlying model. It's faster, cheaper, and works well for specific tasks like classification, summarization, or form fill. Use fine-tuning when you need deep domain adaptation. use prom tuning for faster task specific improvements. Now next thing what you could include is rag. So if I talk about rag, so rag stands for retrieval augmented generation. This basically combines the power of lm with a knowledge retrieval. So instead just relying solely on the model's internal knowledge rack what it does it it pulls the real time external data like PDF websites or databases okay to provide grounded and up-to-date answer for example let's say if you are saying to chat GBT like summarize the latest policy from this PDF okay so if you give a government generated PDF and you want to know what is the latest policy with certain regards so the retriever fetches the relevant text and the LLM is going to summarize that. So this is how RAG actually is very useful and this is one of the future scopes of LLM. The third thing how to improve it is by connecting to the external data. One of the most exciting advancements in LLM app development is the ability to connect the model to external data sources. So by most default LLMs like GPD4 generate responses based on static pre-trained knowledge but real world applications often need to access up-to-date domain specific or user generated data which is where this integration becomes essential. So instead of relying only on what the LLM knows you can dynamically feed it information from various data systems making your app smarter more relevant and context aware. Examples of external data sources can be you could connect to databases like SQL or MongoDB. Retrieve customer records, inventory data, transaction histories, etc. and let the LLM analyze or summarize them. The next one is cloud storage. So you could use Google Drive, Dropbox, AWS S3 documents, PDF for spreadsheet and answer questions about the contents. Third one if you talk about is knowledge bases. Notion, confluence, sharepoint are those things. Pull the internal wiki pages or team documentation into the LLM for better organizational support. Third one will be APIs and web scraping. Bring in live weather data, stock prices, policy updates or even product listings from e-commerce sites. Then you have search indexes like rack. It uses tools like llama index, lang chain or hastack to retrieve and inject relevant information into the prompt before LLM generates a response. Now guys, why does it matter? Because these are the future scope or where our LLM advancement is heading up to. And these capability will turn your app into live knowledge connected assistant capable of answering user queries with realtime accuracy and deep customization. And this becomes very much useful in scenarios like customer support bots, enterprise dashboards, resource tool or intelligent CRM. So guys, by building LM powered application, it's just not a trend. It's a transformative shift in how we design intelligent usercentric software. From setting up a basic prompt response interface to mastering prompt engineering and exploring advanced techniques like rack external data integrations, you now can understand how LLMs can unlock powerful capabilities in your application. So guys, whether you are creating a chatbot, research tool or an internal assistant, you are no longer just in coding. You are designing intelligent conversation. Keep exploring and keep fine-tuning. And let the power of language model elevate your ideas into smart and dynamic experience. Imagine you are managing a global supply chain company and where you have to handle orders, shipments and demand forecasting but unexpected issues arises where certain shortages like transport delays and the changes in demand. So instead of relying on manual adjustments, what if an AI agent could handle everything automatically? This AI wouldn't just suggest actions. It would decide, execute and continuously improve its strategies. That's the power of agentic AI. With that said guys, I welcome you all on our today's tutorial on what is agentic AI. Now let us start with understanding first the first wave of artificial intelligence which was predictive analytics or we could say data analytics and forecasting. What exactly happened? uh like predictive AI focused more on analyzing the historical data, identifying the patterns and making forecast about the future events and these model do not generate any new content but instead it was predicting outcomes based on the statistical models and machine learning. Now technically how used to work. So basically what we had like we used to take uh suppose this is the ML model. Okay. So this is taking a structured data which could be like suppose any past user activity or it could be a transaction record or any sensor reading for example you can consider say Netflix users watch history okay it could be any movie genre watch time and the user rating so now after this what we were basically doing is we were doing the feature engineering or pre-processing okay now in the feature uh engineering ing process. We were extracting key features like user watch time trends, preferred John rates and watch frequency and we could also apply scaling normalization and encoding techniques to basically make data more usable for the ML model. Then we were using the ML models. Suppose it could be a time series forecasting models like ARMA, LSTM and all those given algorithms which was basically predicting the future movie preferences based on the historical data and in the output guys Netflix AI recommends new shows or movie based on the similar user patterns. So this is how exactly the Netflix model was working incorporating the machine learning model. So this was exactly the first wave of AI. Now let us discuss about the second wave of AI. Now if I discuss about the second wave which was basically content creation and use of conversational AI. So you know L models like chat GPD became very much popular during the second wave of artificial intelligence. So what exactly was happening like generative AI was taking input data and it was producing new content such as text, images, videos or even code and these models learn from patterns in large data sets and it was generating humanlike outputs. Now let us bit understand how exactly this technology was working. So basically first there was a data input. Okay. So basically any prompt from the user. So suppose in the GPT okay so I'll just open GPT all over here and say we are uh suppose we are giving any new prompt say such as write a article on AI. Okay so this was our given prompt and after this what exactly was happening was tokenization and pre-processing. So the input text suppose which I have written all over here write a article on AI. So this text was basically split into smaller parts. For example like uh you could consider certain thing like this. So here you have write as one uh you know and as next and similarly you could carry on you know for the other words. Then what exactly used to happen that these words were you know converted into word embeddings means the numerical vectors representing words like in a higher dimensional space and then we used to perform neural network processing. So here the LLM processes input such as attention mechanisms okay or you know using uh these models like GPT4 bird and llama and with the help of self attention layers they were understanding the context and they were predicting the next word. Okay. Now as a result you were getting output certain thing like this. So which was basically a generative AI feed. So this was guys our second evolution of AI. Now if I talk about our third wave, so it is basically agentic AI or autonomous AI agent. Now what is this guys? So the agentic AI actually goes beyond text generation. So it integrates decision making, action execution and autonomous learning. These AI systems don't just respond to prompts but they also independently plan, execute and optimize the processes. So you could understand something like this. So so here the first uh step was the user input or receiving any code. So user provides any highle instruction. For example, it could be like say optimize warehouse shipments for maximum efficiency. It could something be like that. And unlike generative AI which would generate text, agentic AI executes real world actions. After this would suppose the prompt that we have given like optimize warehouse shipments for maximum efficiency. Then the next step would have been quering the databases. The AI would pull the real-time data from multiple sources. So it could be traditional database like SQL or NoSQL where we are fetching inventory levels or shipment history. Then it could be a vector uh database from where it is receiving some unstructured data like past customer complaints and all those thing. Then with the help of external APIs it is connecting to like uh forecasting services or fuel price APIs or supplier ERP systems and these things are like present with this uh respect. Then uh the third step was the LLM decision making. Now after quering the database the AI agent processes data through the LLM based reasoning engine. Example like decision rules applied like suppose if inventory is low then it could automate supplier restocking orders like if shipment cost is increasing then it is rerouting shipments through cheaper vendors and suppose also if weather condition impacts the route then it is adjusting the delivery schedules. Now you can understand how agentic AI is behaving all over here in the decision-m process. Now next step would be action execution via APIs. So AI is executing task without human intervention. It is triggering an API call to reorder a stock from a supplier or update the warehouse robot workflows to prioritize fastm moving products or even send emails and notifications to logistic partners and about the changes what is going to be happen and after this finally it is continuously learning which is a data flywheel all over here okay the AI is monitoring the effectiveness of its action like u it was restocking efficient or did routing shipments you know reduce the cost and all. So it is moni mon monitoring the effectiveness of the action it has taken and the data flywheel is continuously improving the future decisions. So basically it is using reinforcement learning and fine-tuning to optimize its logic. Okay. Now let's have a just quick recap about the comparison of the all these three waves of AI. So basically predative AI's main focus was on forecasting the trends. Okay. while generative AIS was creating the content and agentic AI on the other hand which is at the final step right now is making decision and taking action. So you could see how the evolution happened of AI in all these stages. And if you uh understand about the learning approach then predictive AI was basically analyzing the historical data while generative AI was learning from the patterns like using text image generation. Okay. And but agentic AI is basically using the reinforcement learning or the self-arning to improve its learning approach. Now if we just look at the user involvement in predictive AI. So human is asking for the forecast and all here human is giving the prompts but in the agentic AI the prompts or the intervention of human input has become very much minimal. If you could understand the technology like basically predictive AI was using machine learning time series analytics. So these kind of you know uh algorithms they were using genative AI was using transformers like GPT, Llama, BERT and all those things. Now, agentic AI is doing what guys it is using LLM plus APIs plus autonomous execution. So, we have discussed how this workflow is uh you know in a short way how it is working and uh moving ahead we are also going to discuss uh through an example how exactly all these steps like aent AI is working. So based on the example you could uh understand like uh predative AI you know Netflix recommendation model which they have on their system and uh similarly if you talk about generative AI then you could understand about chat GPT you know writing articles and all those things and agentic AI we could imagine like how AI if incorporated in supply chains how you know things are working out. So guys I hope so you would have got a brief idea regarding the three waves of AI. Now let us move ahead and bit understand about what is the exact difference between generative AI and agentic AI. Now guys let us understand the difference between generative AI and agentic AI. So let us first you know deep dive into what exactly is a generative AI. Okay. So as you can see all over here that generative AI models generally are taking input query. Okay. And they are processing it using LLM or large language model. and basically returning a static response without taking any further action. So in this case for example a chatbot like uh uh chat GPT you know it is taking the input from the user. So as I've shown you earlier that uh say suppose I've given an input like write a blog post on AI in healthcare. So when I have written this uh given uh you know user input or given the query. So when it goes to the large language model these model is actually you know tokenizing all these input query and it is retrieving the relevant knowledge from its training data and it generate text based on the patterns. Now we give the prompt then LLM processes it okay and then we are getting the given output. So now this is basically how you know generative AI is working. So you could see all over here we have GPT model, we have Delhi, we have codeex. So these are some of the you know amazing you know generative AI uh models. Okay. Now let us discuss bit about deli which is actually a you know realistic image generation you know genai. So uh like deli is described as you know the realistic image generation model by the open AI and this actually is a part of you know generative AI category alongside with GPT which is basically for human like language creation purposes this model was created and you could have also codeex for like uh it could be used for advanced code generation purposes. Now let us discuss bit about DALI. So, DALI is like a deep learning model basically which is designed to generate realistic images from the text prompt and it can create highly detailed and creative visuals based on descriptions provided by the users. So, uh some of the aspects of Deli like you could have all over here like text to image generation where users can input text prompts and Deli can generate unique images based on those description. The images generated by uh Deli are highly realistic and creative. Okay. And it can generate photorealistic images, artistic illustration and even surreal or imaginative visuals. We will also have customization and variability where it is allowing variation of an image edits based on text instruction and multiple style. So this is also part of a generative AI model and uh it is this tool is actually playing a very amazing role. So I will show you one example like how generative AI is actually working in the image generation purposes. So guys as you can see all over here I have opened this generative AI tool called Deli. Let us give a prompt to Deli and let us see how the image is generated. So let's say we want a futuristic city. Add sunset filled with neon skyscrapper. So you have flying cars and holographic billboards. The streets are bustling with humanoid robots and we can have people viewing uh let's just say hi-tech you know let's include some technology okay now let us see how uh Deli is trying to create a image so this is how actually generative AI is working. So let it wait for a few seconds as the output comes up. Now you could see all over here that uh this image which is generated basically this is generated by AI and you could see based on our prompt it has given like the kind of you know uh the input we gave and we got the output based on this. Now so this is one of the amazing uh genai tool. We could explore this guys. Okay. Now guys let us discuss about agentic AI or autonomous decision making and action execution. So you could see this diagram all over here. So agentic AI like unlike the generative AI it is not generating responses but it is also executing a task autonomously based on the given query. For example like if you take uh AI in managing a warehouse inventory. Okay suppose we want to optimize the warehouse shipment for the next quarter. So here what is going to happen? So first the agent is going to receive its goal all over here. Okay. And um this AI agent uh you know is going to query the external data sources. So it could uh you know for example it could be your uh you know inventory databases or logistics API and then it retrieves realtime inventory levels and it demands the given forecast. Okay. Now at here it is going to make the autonomous discussions and uh the kind of output we are going to get will be kept in observation by this agent. Okay. So basically it is going to analyze the current warehouse stock product demand for the next quarter check the supplers availability and automate the restocking if inventory is below the given threshold. So u for example you could uh imagine uh you know suppose based on the you know output what we are going to get all over here. So based on this output we could get certain thing like this like uh say current inventory level like say 75% capacity. Okay. Then uh it could have also other thing like uh say demand forecast say 30% increase in expected in uh quarter two and also it is going to go say like say reordering initiated. So this is the output what we are going to get based on the supply chain management you know example what we are trying to get. So as we have seen in generative AI user is giving the input okay prompt then it is using LLM model to generate the given output but agentic AI is doing what guys it is going it is going to take a action you know beyond just generating a text. So in this scenario, it is squaring the inventory databases. It is automating the purchase order. It is going to select the optimal shipping providers which could be you know suitable for the given company. It is going to continuously refine the strategies based on the real-time feedback. So guys let's recap once more. So if we talk about the function base then ji is more concerned with producing a written content or a visual content. Okay. And even it can code from the pre-existing input. But if you talk about agentic AI guys, uh it is actually you know it's all about decision making taking actions towards a specific goal and it is focused on achieving the objectives by interacting with the environment and making the autonomous decision. Genai is exactly relying on the existing data to predict and generate content based on say patterns it has learned during its training phase but it does not adapt or evolve from its experiences. Whereas if I talk about agentic AI, it is adaptive. So it is learning from its actions and experiences. It is improving over time by analyzing the feedback, adjusting its behavior to meet objectives more effectively. With the help of jai, human input is essential to the prompt. So that you know basically with the help of that it could go into the LM model and it could generate the given uh you know output based on your prompt. Once uh you set up the agentic AI, it requires like minimum human involvement. It operates autonomously making decisions and adapting to changes like without continuous human guidance and it can even learn in real time. So that's what the beauty of agentic AI is. So we have given one example of genai like basically giving prompt to the chat GPT or Delhi. Okay. And agentic AI one example could be your supply chain management system. Now let us bit deep dive into understanding the technical aspects of how agentic AI is exactly working. Now guys let us try to understand how agentic AI is exactly working. So there is actually a four-step process of you know how agentic AI exactly works. So the first step is you know perceiving where basically what we are doing is we are gathering and processing information from databases sensors and digital environments and also the next step is reasoning. So with the help of large language model as a decision-m engine it is generating the solutions. If we talk about the third step which is acting. So it is integrating with external tools and softwares to autonomously execute the given task. And finally it is learning continuously to improve through the feedback loop which is also known as the data fly. Okay. Now let us explore each of the step one by one and let us try to understand. So if you talk about perceiving. Okay. So this is actually the first step where agentic AI is actually stepping up. So it is doing the perception where what exactly is happening guys that AI is collecting data from multiple sources. So this data could be from database okay like your traditional and vector databases okay so it could be graphql like uh vector database means the same and uh if you talk about other from data it could be from APIs like it is fetching real-time information from external systems it is uh basically taking data from the IoT sensors like for real world applications like robotics and logistics and also it could take you know data from from the user inputs also like it could be text command, voice commands or a chatbot interaction. Now how it is exactly working guys? So basically let us recolct everything technically and let us see how this is happening. So the first step which is going and perceiving is the data extraction where exactly the AI agent queries the structured uh databases like SQL or NoSQL for relevant records. uh it is also using vector databases to retrieve any semantic data for context aware responses like uh it could be you know any complaints certain uh you know it is trying to find out okay so next after it has got the data extraction it goes for feature extraction and pre-processing where AI is filtering the relevant features from the raw data for example like a fraud detection AI is scanning the transaction log for anomalies the third thing it is entity recognition and object detection. So AI uses basically computer vision to detect objects and images and uh then it applying the named entity recognition. This is a technique okay uh to extract the critical terms from the given text also. So we have three uh step-by-step process which is happening in uh perceiving. The first one is data extraction. Second one is feature extraction and pre-processing. The third one is like entity recognition and object detection. So uh let us take a very simple example like AI based customer support system. So if you consider an agentic AI assistance like for a customer service. So say a customer is asking where is my order. So the AI queries multiple databases all over here. Suppose it is going to query the e-commerce order database to retrieve the order status or it could go to the logistics API to track the real-time shipment location. Also it could go for customer interaction history to provide personalized response. The result what we get all over here is that the AI is fetching the tracking details, identify any delays if it is happening and suggesting the best course of action. Now uh the next step is reasoning. Okay. Now AI's understanding and decision making and problem solving is making agentic AI way very uh greater. So here what is exactly happening like once the AI has perceived the data now it should start reasoning it. Okay. So the LM model acts as a reasoning engine you know orchestrating AI processes and integrating with specialized models for various function. So if you talk about the key components uh like here used in the reasoning it could be LLM based decision making. So AI agents could use models like LLMs, like GBT4, cloud, llama to interpret a user intent and generate a response. It is basically coordinating with smaller AI models for domain specific task like it could be like financial prediction or medical diagnostics. So these could be uh you know given example then it is using retrieval augmented generation or rag model. Okay. to with the help of which AI is enhancing the accuracy you know by retrieving any proprietary data from the company's databases for example like uh instead of relying on GP4's knowledge the AI can fetch company specific policies to generate the accurate answers so this could be the one and uh in in the reasoning the final step is AI workflow and planning so it is a multi-step reasoning where AI is breaking down complex task into logical step for example like if I ask to automate a financial report AI is retrieving the transaction data, analyzing the trend and it is formatting the results. So for example, you could use this in uh supply chain management. Suppose consider there is a logistics company which is using the agentic AI to optimize what could be the you know uh shipping routes you know. So a supply chain manager requesting the AI agent to find the best shipping route to reduce the delivery cost. So the AI processes realtime fuel prices, traffic conditions and weather report. So using LLM plus data retrieval, it finds out the optimized routes and selects the cheapest carrier. Result you get is that AI chooses the best delivery option. So here the cost is reduced and improving efficiency. So this is one of the uh use cases guys. So after perceiving you get his uh reasoning. Okay. Now let us move ahead and discuss about the third step which is act. So in this step basically what is happening like AI is taking autonomous actions. So unlike generative AI which stops at generating content. So agentic AI takes uh the real world action. Okay. How AI is executing task autonomously guys. So basically first step is like here the integration with APIs and software could be happen where AI can send automated API calls to the business systems. for example like uh reordering the stock from the supplier's API. So suppose any inventory level is going down. So it could you know reorder that particular stock from the supplers's API. So it is interacting with the given API. Now it could also automate the workflows like AI executes multi-step workflows without human supervision. So here like AI can handle like insurance claims by verifying the documents, checking policies and approving the payouts. And finally AI could operate within predefined business rules. Okay. To prevent any unauthorized actions also. So ethical AI is basically being worked in this direction. For example, like AI can automatically process claims up to say uh $10,000 US, you know, but it is requiring the human approval for the higher amount. So based on you know uh insurance and policy making stuff. So agentic AI could be you know really helpful in this scenario. Uh one example like uh let's consider so let's say we have this agentic AI managing an IT support system. So suppose a user says my email server is down. So the AI can diagnose the issue restart the server and confirms the given resolution. Now if it is unresolved then AI escalates to a human technician. Then uh it results into you know AI is fixing the issues autonomously reducing the downtime. Okay. So this is where your action or act is coming up into the picture. Now if you go on to the next and the final step which is learning. So uh learning basically with the help of data flywheel it is continuously learning. Okay. So this is the feedback loop all over here which is the data fly wheel. So how AI learns over the time if we ask this question. So what is exactly happening that it is interacting with the data collection. Suppose AI logs uh successful and failed actions. For example like if users correct AI generated responses then AI is learning from those corrections. Second thing what you could do is you could model uh you could fine-tune the model and do reinforcement learning. So AI adjusts its decision-m models, you know, basically to improve future accuracy. It uses reinforcement learning basically to optimize workflows based on past performance. Okay. Now, uh third step could be automated data labeling and selfcorrection. So here what is happening that AI is labeling and categorizing past interactions to refine its knowledge base. Example like AI autonomously is updating frequently asked answers based on the recurring user queries. So in this way AI is learning over the time. Uh example one you could consider. So say we have this uh AI is optimizing any financial fraud detection. So say this is uh consider that this is a bank which is AI powered which has this AI powered fraud detection system. So AI is analyzing these financial transaction and it is detecting any suspicious activity and if flagged the transactions are false and AI is learning to reduce this false alerts. So over the time AI is improving the fraud detection accuracy like minimizing disruptions for the customer. So in this way AI is getting smarter over the time like reducing the false alerts and also the financial fraud. So let's have a just quick recap of what uh we studied right now. So agentic AI works in four steps. The first step is perceiving where AI is gathering data from databases, sensors and APIs. The next step is reasoning. So it is using LLM to interpret task, applies logic and generating the solution. The third step is acting. So here AI is integrating with external systems and automating the task. And finally it is learning. So AI is improving over the time you know via feedback loop or which is basically called as data wheel. So guys uh now let us see this diagram and try to understand what this diagram is trying to say. So the first thing you could see an AI agent all over here. So this is an AI agent which is basically an autonomous system. So which has a capability of perceiving its environment making decision and executing actions without any human intervention. Now AI agent is acting as the central intelligence okay in this given diagram and it interacts with the user. Okay. Uh and various other data sources. It processes input, queries databases, makes decision using a large language model and it is executing action and it is learning from the given feedback. Now the next step you could see the LLM model. So if we talk about LLMs, these are the large language model which is kind of an advanced AI model trained on massive amount of text data to understand, generate and reason over natural language. Now if I talk about this LLM so this is actually acting as the reasoning engine all over here and it is interpreting the user inputs and making informed decision. It is also retrieving relevant data from the databases generating uh responses. It can also coordinate with multiple AI models for different task like it could be content generation okay predictions or decision making. Now when the user is asking a chatbot like for example let's say what is my account balance so the LLM processes the query retrieves the relevant data and responds the given bank balance accordingly. Now if you look at the kind of database the LLM is interacting. So we have the traditional database and the vector database. So uh here if I say the database like AI agent basically is quering the structured database. So suppose structured database like it could be a customer records or inventory data or it could be any transactional log also. So traditional databases basically store well definfined you know structured information. Okay. So for example like uh when a bank assistant is processing a query like show my last five transaction. So it is basically fetching the information from a traditional SQL based database. Next we have this vector database also guys. So vector database is a specialized uh kind of a database for storing unstructured data which could be like text embeddings, images or audio representations. So guys like unlike traditional databases that store exact values, vector databases store in a highdimensional mathematical space. It allows AI models to search semantically uh similar data instead of like exact matches. Now AI is retrieving the contextual information from the vector databases which is ex uh actually enhancing the decision making. It is improving the AI memory by allowing the system also to search for you know conceptually similar past interaction. Let us take an example to understand this. For example we have discussed about a customer support jackbot. So suppose if it queries a vector database to find out similar past tickets like when responding to a customer query. So a recommendation engine could use a vector database to find out similar products on a user's past preferences. So this could be done in that scenario. Also some of the like popular vector databases could be like Facebook's AI similarity search pine cone or VV8. These are the certain uh amazing vector databases. Then you could see the next step is you know after it has worked on these given data it is performing the action. So the action component is referring where AI's agent has this ability to now execute task autonomously after the reasoning is done. So AI is integrating with external tools, APIs or automation software to complete the given task. It does not provide only information but it is actually uh say you know performing the given action. So for example like in a customer support the AI can automatically reset a user's password after verifying the identity. If we talk about in finance then AI can approve a loan also like based on the predefined eligibility criteria. Now finally we have the data flywheel. So data flywhe is a continuous feedback loop where AI is learning from the past interactions refining its models and it is always improving over the time. Now every time like the AI is interacting the data or taking an action or receiving a feedback that information is fed into this model. So this is creating a self improving AI system that is becoming smarter over the time. So the data flywheel is allowing AI to learn from every interaction and uh AI is becoming more efficient by continuously optimizing responses and refining strategies. Best thing in could be used in a fraud detection. So in this the AI is going to learn from the past fraud cases and it is going to detect new fraudulent patterns and more effectively. Chatbots also can learn from user feedback and improve the responses. And finally, you have the model customization which is basically you are trying to fine-tune the AI models on specific business need or any industry requirement. So AI models are not static like they can be adapted and optimized for a specific task. So custom fine-tuning is actually improving the accuracy and domain specific application like it could be finance, healthcare or cyber security. So a financial institution say fine-tuning an LLM to generate a investment advice okay on a historical market trends that could be one use case or in healthcare if we discuss like uh the healthcare provider is fine-tuning then AI model to interpret the medical reports and recommend the treatments. So guys based on the given diagram you would have got a brief idea like how uh you know agentic AI is working. Now if we discuss about the future of agentic AI then guys I would say it looks very much promising because it is keep improving itself and it is finding new ways to be useful like with better machine learning algorithms and smarter decision making these AI system will be more uh independent handling complex task on their own and believe me in industries like healthcare finance customer service they have already started to see how AI agents can make more impact act and it could be more efficient from personalization perspective you know managing resources and many more other things. So as the system continue to learn and adapt I think so they will be opening up even more possibilities helping businesses grow improving how we live and work. Now I would say that uh in conclusion that agentic AI is actually paving the way for new opportunities like unlike the older versions of AI which was assisting with generating content or predicting the data you know or responding to any queries but agentic AI can perform techniques independently with minimal human effort and agentic AI has become self-reliant in decision-m way and it is making a very big differences in industry like healthcare logistics customer services which is enabling companies to be more efficient. As a result, it is providing better services to their clients. So what is deep learning? Deep learning is a subset of machine learning which itself is a branch of artificial intelligence. Unlike traditional machine learning models which require manual feature extraction, deep learning models automatically discovers representation from raw data. So this is made possible through neural networks particularly deep neural networks which consist of multiple layers of interconnected nodes. So these neural network are inspired by the structure and the function of human brain. Each layer in the network transform the input data into more abstract and composite representation. For instance in image recognition the initial layer might detect simple features like edges and textures while the deeper layer recognize more complex structure like shapes and objects. So one of the key advantage of deep learning is its ability to handle large amount of unstructured data such as images, audios and text making it extremely powerful for various application. So stay tuned as we delve deeper into how these neural networks are trained, the types of deep learning models and some exciting application that are shaping our future. Types of deep learning. Deep learning AI can be applied supervised, unsupervised and reinforcemental machine learning using various methods for each. The first one supervised machine learning. In supervised learning, the neural network learns to make prediction or classify that data using label data sets. Both input features and target variables are provided and the network learns by minimizing the error between its prediction and the actual targets. A process called back propagation. CNN and RNN are the common deep learning algorithms used for tasks like image classification, sentiment analysis and language translation. The second one, unsupervised machine learning. In unsupervised machine learning, the neural network discovers patterns or cluster in unlabelled data sets without target variables. It identifies hidden pattern or relationship within the data. Algorithms like autoenccoders and generative models are used for tasks such as clustering, dimensionality reduction and anomaly detection. The third one, reinforcement machine learning. In this, an agent learns to make decision in an environment to maximize a reward signal. The agent takes action, observes the records and learns policies to maximize cumulative rewards over time. Deep reinforcement learning algorithms like deep networks and deep deterministic polygradient are used for tasks such as robotics and gameplay. Moving forward, let's see what are the artificial neural networks. Artificial neural networks inspired by the structure and the function of human neurons consist of interconnected layers of artificial neurals or units. The input layer receives data from the external resources and it passes to one or more hidden layers. Each neuron in these layers computes a weighted sum of inputs and transfer the result to the next layer. During training, the weight of these connection are adjusted to optimize the network's performance. A fully connected artificial neural network includes an input layer or more hidden layers and an output layer. Each neuron in a hidden layer receives input from the previous layer and sends its output to the next layer. So this process continues until the final output layer produce the network response. So moving forward let's see types of neural networks. So deep learning models can automatically learn feature from data making them ideal to task like image recognition, speech recognition and natural language processing. So the most common architecture in deep learnings are the first one feed forward neural network FN. So these are the simplest type of neural network where information flows linearly from the input to the output. They are widely used for tasks such as image classification, speech recognition and natural language processing NLP. The second one convolutional neural network designed specifically for image and video recognition. CNN's automatically learn feature from images making them ideal for image classification, object detection and image segmentation. The third one recurrent neural networks RNN are specialized for processing sequential data time series and natural language. They maintain an internal state to capture information from previous input making them suitable for task such as speech recognition, NLP and language transition. So now let's move forward and see some deep learning application. The first one is autonomous vehicle. Deep learning is changing the development of self-driving car. Algorithms like CNN's process data from sensors and cameras to detect object, recognize traffic signs and make driving decision in real time, enhancing safety and efficiency on the road. The second one is healthcare diagnostic. Deep learning models are being used to analyze medical images such as X-rays, MRIs and CT scans with high accuracy. They help in early detection and diagnosis of diseases like cancer, improving treatment outcomes and saving lives. The third one is NLP. Recent advancement in NLP powered by deep learning models like transformers, chat GPT have led to more sophisticated and humanlike text generation, translation and sentiment analysis. So application include virtual assistant, chat bots and automated customer service. The fourth one defake technology. So deep learning techniques are used to create highly realistic synthetic media known as defects. While this technology has entertainment and creative application, it also raises ethical concern regarding misinformation and digital manipulation. The fifth one, predictive maintenance in industries like manufacturing and aviation. Deep learning models predict equipment failures before they occur by analyzing sensor data. The proactive approach reduces downtime, lowers maintenance cost, and improves operational efficiency. So now let's move forward and see some advantages and disadvantages of deep learning. So first one is high computational requirements. So deep learning requires significant data and computational resources for training. Whereas advantage is high accuracy achieves a state-of-the-art performance in tasks like image recognition and natural language processing. Whereas deep learning needs large label data sets often require extensive label data set for training which can be costly and time consuming together. So second advantage of deep learning is automated feature engineering automatically discovers and learn relevant features from data without manual intervention. The third disadvantage is overfitting. So deep learning can overfit to training data leading to poor performance on new unseen data. Whereas the third deep learning advantage is scalability. So deep learning can handle large complex data set and learn from massive amount of data. So in conclusion, deep learning is a transformative leap in AI mimicking human neural networks. It has changed healthcare, finance, autonomous vehicles and NLP. In this video, we will learn about an important popular deep learning neural network called generative adversarial networks. Yan Leon, one of the pioneers in the field of machine learning and deep learning, described it as the most interesting idea in the last 10 years in machine learning. In this video, you will learn about what are generative adversarial networks and look in brief at generator and discriminator. Then we'll understand how GANs work and the different types of GANs. Finally, we'll look at some of the applications of GANs. So, let's begin. So, what are generative adversarial networks? Generative adversarial networks or GANs introduced in 2014 by Ian J. Goodfellow and co-authors became very popular in the field of machine learning. GAN is an unsupervised learning task in machine learning. It consists of two models that automatically discover and learn the patterns in input data. The two models called generator and discriminator compete with each other to analyze, capture and copy the variations within a data set. GANs can be used to generate new examples that possibly could have been drawn from the original data set. In the image below, you can see that there is a database that has real 100 rupee nodes. The generator which is basically a neural network generates fake 100 rupees nodes. The discriminator network will identify if the nodes are real or fake. Let us now understand in brief about what is a generator. A generator in GANs is a neural network that creates fake data to be trained on the discriminator. It learns to generate plausible data. The generated instances become negative training examples for the discriminator. It takes a fixed length random vector carrying noise as input and generates a sample. Now the main aim of the generator is to make the discriminator classify its output as real. The portion of the GAN that trains a generator includes a noisy input vector, the generator network which transforms the random input into a data instance, a discriminator network which classifies the generator data, and a generator loss which penalizes the generator for failing to do the discriminator. The back propagation method is used to adjust each weight in the right direction by calculating the weights impact on the output. The back propagation method is used to obtain gradients and these gradients can help change the generator weights. Now let us understand in brief what a discriminator is. A discriminator is a neural network model that identifies real data from the fake data generated by the generator. The discriminator's training data comes from two sources. The real data instances such as real pictures of birds, humans, currency notes, etc. are used by the discriminator as positive samples during the training. The fake data instances created by the generator are used as negative examples during the training process. While training the discriminator, it connects with two loss functions. During discriminator training, the discriminator ignores the generator loss and just uses the discriminator loss. In the process of training the discriminator, the discriminator classifies both real data and fake data from the generator. The discriminator laws penalizes the discriminator for mclassifying a real data instance as fake or a fake data instance as real. Now moving ahead, let's understand how GANs work. Now GANs consists of two networks. A generator which is represented as G of X and a discriminator which is represented as D of X. They both play an adversarial game where the generator tries to fool the discriminator by generating data similar to those in the training set. The discriminator tries not to be fooled by identifying fake data from the real data. They both work simultaneously to learn and train complex data like audio, video or image files. Now you are aware that GANs consists of two networks a generator G of X and discriminator D of X. Now the generator network takes a sample and generates a fake sample of data. The generator is trained to increase the probability of the discriminator network to make mistakes. On the other hand, the discriminator network decides whether the data is generated or taken from the real sample using a binary classification problem with the help of a sigmoid function that gives the output in the range 0 and one. Here is an example of a generative adversarial network trying to identify if the 100 rupee nodes are real or fake. So first a noise vector or the input vector is fed to the generator network. The generator creates fake 100 rupee nodes. The real images of 100 rupee nodes stored in a database are passed to the discriminator along with the fake nodes. The discriminator then identifies the nodes and classifies them as real or fake. We train the model, calculate the loss function at the end of the discriminator network and back propagate the loss into both discriminator and generator. Now the mathematical equation of training again can be represented as you can see here. Now this is the equation and these are the parameters. Here G represents generator. D represents the discriminator. Now P data of X is the probability distribution of real data. P of zed is the distribution of generator. X is the sample of probability data of X. Zed is the sample size from P of zed. D of X is the discriminator network and G of zed is the generator network. Now the discriminator focuses to maximize the objective function such that d of x is close to 1 and z of zed is close to zero. It simply means that the discriminator should identify all the images from the training set as real that is one and all the generated images as fake that is zero. The generator wants to minimize the objective function such that d of z of zed is one. This means that the generator tries to generate images that are classified as real that is one by the discriminator network. Next, let's see the steps for training a neural network. So, we have to first define the problem and collect the data. Then, we'll choose the architecture of GAN. Now, depending on your problem, choose how your GAN should look like. Then, we need to train the discriminator on real data. That will help us predict them as real for n number of times. Next, you need to generate fake inputs for the generator. After that, you need to train the discriminator on fake data to predict the generator data as fake. Finally, train the generator on the output of discriminator. With the discriminator predictions available, train the generator to fool the discriminator. Let us now look at the different types of GANs. So, first we have vanilla GANs. Now vanilla GANs have minmax optimization formula that we saw earlier where the discriminator is a binary classifier and is using sigmoid cross entropy loss during optimization in vanilla GANs the generator and the discriminator as simple multi-layer perceptrons. The algorithm tries to optimize the mathematical equation using stochastic gradient descent. Up next we have deep convolutional GANs or DC GANs. Now DC GANs support convolutional neural networks instead of vanilla neural networks at both discriminator and generator. They are more stable and generate higher quality images. The generator is a set of convolutional layers with fractional strided convolutions or transpose convolutions. So it unsamples the input image at every convolutional layer. The discriminator is a set of convolutional layers with strided convolutions. So it downsamples the input image at every convolutional layer. Moving ahead, the third type we have is conditional GANs or C GANs. Vanilla GANs can be extended into conditional models by using an extra label information to generate better results. In C GAN, an additional parameter called Y is added to the generator for generating the corresponding data. Labels are fed as input to the discriminator to help distinguish the real data from fake data generated. Finally, we have super resolution GANs. Now, SR GANs use deep neural networks along with adversarial neural network to produce higher resolution images. Super resolution GANs generate a photorealistic highresolution image when given a low resolution image. Let's look at some of the important applications of GANs. So, with the help of DC GANs, you can train images of cartoon characters for generating faces of anime characters and Pokémon characters as well. Next, GANs can be used on the images of humans to generate realistic faces. The faces that you see on your screens have been generated using GANs and do not exist in reality. Third application we have is GANs can be used to build realistic images from textual descriptions of objects like birds, humans, and other animals. We input a sentence and generate multiple images fitting the description. Here is an example of a text to image translation using GANs for a bird with a black head, yellow body, and a short beak. The final application we have is creating 3D objects. So, GANs can generate 3D models using 2D pictures of objects from multiple perspectives. GANs are very popular in the gaming industry. GANs can help automate the task of creating 3D characters and backgrounds to give them a realistic feel. We've looked at a lot of examples of machine learning. So, let's see if we can give a little bit more of a concrete definition. What is machine learning? Machine learning is the science of making computers learn and act like humans by feeding data and information without being explicitly programmed. And we see here we have a nice little diagram where we have our ordinary system, your computer. Nowadays, you can even run a lot of this stuff on a cell phone because cell phones have advanced so much. And then with artificial intelligence and machine learning, it now takes the data and it learns from what happened before and then it predicts what's going to come next. And then really the biggest part right now in machine learning that's going on is it improves on that. How do we find a new solution? So we go from descriptive where it's learning about stuff and understanding how it fits together to predicting what it's going to do to post scripting coming up with a new solution. And when we're working on machine learning, there's a number of different diagrams that people have posted for what steps to go through. A lot of it might be very domain specific. So if you're working on photo identification versus language versus medical or physics, some of these are switched around a little bit or new things are put in. They're very specific to the domain. This is kind of a very general diagram. First, you want to define your objective. Very important to know what it is you're wanting to predict. Then you're going to be collecting the data. So once you've defined an objective, you need to collect the data that matches. You spend a lot of time in data science collecting data and the next step preparing the data. You got to make sure that your data is clean going in. There's the old saying, bad data in, bad answer out or bad data out. And then once you've gone through and we've cleaned all this stuff coming in, then you're going to select the algorithm. Which algorithm are you going to use? You're going to train that algorithm. In this case, I think we're going to be working with SVM, the support vector machine. Then you have to test the model. Does this model work? Is this a valid model for what we're doing? And then once you've tested it, you want to run your prediction. You want to run your prediction or your choice or whatever output it's going to come up with. And then once everything is set and you've done lots of testing, then you want to go ahead and deploy the model. And remember I said domain specific. This is very general as far as the scope of doing something. A lot of models you get halfway through and you realize that your data is missing something and you have to go collect new data because you've run a test in here someplace along the line. You're saying, "Hey, I'm not really getting the answers I need." So there's a lot of things that are domain specific that become part of this model. This is a very general model, but it's a very good model to start with. And we do have some basic divisions of what machine learning does that's important to know. For instance, do you want to predict a category? Well, if you're categorizing thing, that's classification. For instance, whether the stock price will increase or decrease. So in other words, I'm looking for a yes no answer. Is it going up or is it going down? And in that case, we'd actually say, is it going up? True. If it's not going up, it's false, meaning it's going down. This way, it's a yes, no. 01. Do you want to predict a quantity? That's regression. So remember, we just did classification. Now we're looking at regression. These are the two major divisions in what data is doing. For instance, predicting the age of a person based on the height, weight, health and other factors. So based on these different factors, you might guess how old a person is. And then there are a lot of domain specific things like do you want to detect an anomaly? That's anomaly detection. This is actually very popular right now. For instance, you want to detect money withdrawal anomalies. You want to know when someone's making a withdrawal that might not be their own account. We've actually brought this up because this is really big right now. If you're predicting the stock whether to buy stock or not, you want to be able to know if what's going on in the stock market is an anomaly, use a different prediction model because something else is going on. You got to pull out new information in there or is this just the norm? I'm going to get my normal return on my money invested. So being able to detect anomalies is very big in data science these days. Another question that comes up which is on what we call untrained data is do you want to discover structure in unexplored data and that's called clustering. For instance, finding groups of customers with similar behavior given a large database of customer data containing their demographics and past buying records. And in this case, we might notice that anybody who's wearing certain set of shoes goes shopping at certain stores or whatever it is, they're going to make certain purchases. By having that information, it helps us to market or group people together. So then we can now explore that group and find out what it is we want to market to them if you're in the marketing world. And that might also work in just about any arena. You might want to group people together whether they're uh based on their different areas and investments and financial background whether you're going to give them a loan or not before you even start looking at whether they're valid customer for the bank. You might want to look at all these different areas and group them together based on unknown data. So you're not you don't know what the data is going to tell you, but you want to cluster people together that come together. Let's take a quick detour for quiz time. Oh, my favorite. So, we're going to have a couple questions here under our quiz time and um we'll be posting the answers in these part two of this tutorial. So, let's go ahead and take a look at these quiz times questions and hopefully you'll get them all right and it'll get you thinking about how to process data and what's going on. Can you tell what's happening in the following cases? Of course, you're sitting there with your cup of coffee and you have your checkbox and your pen trying to figure out what's your next step in your data science analysis. So the first one is grouping documents into different categories based on the topic and content of each document. Very big these days. You know, you have legal documents, you have uh maybe it's a sports group documents, maybe you're analyzing newspaper postings, but certainly having that automated is a huge thing in today's world. B, identifying handwritten digits in images correctly. So we want to know whether uh they're writing an A or capital A B C what are they writing out in their hand digit their handwriting. C behavior of a website indicating that the site is not working as designed. D predicting salary of an individual based on his or her years of experience the way HR hiring uh setup there. So stay tuned for part two. We'll go ahead and answer these questions when we get to the part two of this tutorial or you can just simply write at the bottom and send a note to simply learn and they'll follow up with you on it. Back to our regular content. Now these last few bring us into the next topic which is another way of dividing our types of machine learning and that is with supervised, unsupervised and reinforcement learning. Supervised learning is a method used to enable machines to classify, predict objects, problems or situations based on labeled data fed to the machine. And in here you see we have a jumble of data with circles, triangles and squares. And then we label them. We have what's a circle, what's a triangle, what's a square. And we have our model training and it trains it. So we know the answer. Very important when you're doing supervised learning, you already know the answer to a lot of your information coming in. you have a huge group of data coming in and then you have a new data coming in. So we've trained our model. The model now knows the difference between a circle, a square, a triangle. And now that we've trained it, we can send in in this case a square and a circle goes in and it predicts that the top one's a square and the next one's a circle. And you can see that this is uh being able to predict whether someone's going to default on a loan because I was talking about banks earlier. Supervised learning on stock market, whether you're going to make money or not, that's always important. And uh if you are looking to make a fortune in the stock market, keep in mind it is very difficult to get all the data correct on the stock market. It is very uh it fluctuates in ways you really hard to predict. So it's quite a roller coaster ride. If you're running machine learning on the stock market, you start realizing you really have to dig for new data. So we have supervised learning. And if you have supervised, we need unsupervised learning. In unsupervised learning, machine learning model finds the hidden pattern in an unlabeled data. So in this case, instead of telling it what the circle is and what a triangle is and what a square is, it goes in there, looks at them, and says for whatever reason, it groups them together. Maybe it'll group it by the number of corners. And it notices that a number of them all have three corners, a number of them all have four corners, and a number of them all have no corners. And it's able to filter those through and group them together. We talked about that earlier with looking at a group of people who are out shopping. We want to group them together to find out what they have in common. And of course, once you understand what people have in common, maybe you have one of them who's a customer at your store, or you have five of them are customer at your store, and they have a lot in common with five others who are not customers at your store. How do you market to those five who aren't customers at your store yet? They fit the demographs of who's going to shop there, and you'd like them to shop at your store, not the one next door. Of course, this is a simplified version. And you can see very easily the difference between a triangle and a circle which is might not be so easy in marketing. Reinforcement learning. Reinforcement learning is an important type of machine learning where an agent learns how to behave in an environment by performing actions and seeing the result. And we have here where the in this case a baby. It's actually great that they used an infant for this slide because the reinforcement learning is very much in its infant stages. But it's also probably the biggest machine learning demand out there right now or in the future. It's going to be coming up over the next few years is reinforcement learning and how to make that work for us. And you can see here where we have our action. In the action in this one, it goes into the fire. Hopefully the baby didn't it's just a little candle, not a giant fire pit like it looks like here. When the baby comes out and the new state is the baby is sad and crying because they got burned on the fire. And then maybe they take another action. The baby's called the agent because it's the one taking the actions. And in this case, they didn't go into the fire. They went a different direction and now the baby's happy and laughing and playing. Reinforcement learning is very easy to understand because that's how as humans, that's one of the ways we learn. We learn whether it is, you know, you burn yourself on the stove, don't do that anymore. Don't touch the stove. In the big picture, being able to have machine learning program or an AI be able to do this is huge because now we're starting to learn how to learn. That's a big jump in the world of computer and machine learning. And we're going to go back and just kind of go back over supervised versus unsupervised learning. Understanding this is huge because this is going to come up in any project you're working on. We have in supervised learning, we have labeled data. We have direct feedback. So someone's already gone in there and said, "Yes, that's a triangle. No, that's not a triangle." And then you predicted outcome. So you have a nice prediction. this is this this new set of data is coming in and we know what it's going to be. And then with unsupervised training, it's not labeled. So, we really don't know what it is. There's no feedback. So, we're not telling it whether it's right or wrong. We're not telling it whether it's a triangle or a square. We're not telling it to go left or right. All we do is we're finding hidden structure in the data, grouping the data together to find out what connects to each other. And then you can use these together. So imagine you have an image and you're not sure what you're looking for. So you go in and you have the unstructured data, find all these things that are connected together and then somebody looks at those and labels them. Now you can take that labeled data and program something to predict what's in the picture. So you can see how they go back and forth and you can start connecting all these different tools together to make a bigger picture. There are many interesting machine learning algorithms. Let's have a look at a few of them. Hopefully this gave you a little flavor of what's out there and these are some of the most important ones that are currently being used. We'll take a look at linear regression, decision tree, and the support vector machine. Let's start with a closer look at linear regression. Linear regression is perhaps one of the most well-known and well understood algorithms in statistics and machine learning. Linear regression is a linear model. For example, a model that assumes a linear relationship between the input variables x and the single output variable y. And you'll see this if you remember from your algebra classes, y = mx + c. Imagine we are predicting distance traveled y from speed x. Our linear regression model representation for this problem would be y = m * x + c or distance = m * speed + c where m is the coefficient and c is the y intercept. And we're going to look at two different variations of this. First, we're going to start with time is constant. And you can see we have a bicyclist. He's got his safety gear on, thank goodness. Speed equals 10 meters/s. And so over a certain amount of time, his distance equals 36 km. We have a second bicyclist who's going twice the speed or 20 m/s. And you can guess if he's going twice the speed and time is a constant, then he's going to go twice the distance. And that's easy to compute. 36 * 2, you get 72 kilometers. And so if you had the question of how fast would somebody going three times that speed or 30 m/s is, you can easily compute the distance in our head. We can do that without needing a computer, but we want to do this for more complicated data. So, it's kind of nice to compare the two, but let's just take a look at that and what that looks like in a graph. So, in a linear regression model, we have our distance to the speed and we have our m equals the ve slope of the line. And we'll notice that the line has a plus slope. And as speed increases, distance also increases. Hence, the variables have a positive relationship. And so your speed of the person which equals y= mx plus c distance traveled in a fixed interval of time. And we could very easily compute either following the line or just knowing it's 3 * 10 m/s that this is roughly 102 km distance that this third bicus has traveled. One of the key definitions on here is positive relationship. So the slope of the line is positive. As distance increase so does speed increase. Let's take a look at our second example where we put distance is a constant. So we have speed equals 10 m/s. They have a certain distance to go and it takes him 100 seconds to travel that distance. And we have our second bicyclist who's still doing 20 m/s. Since he's going twice the speed, we can guess he'll cover the distance in about half the time, 50 seconds. And of course, you could probably guess on the third one, 100 divided by 30 since he's going three times the speed. You can easily guess that this is 33.333 seconds time. We put that into a linear regression model or a graph. If the distance is assumed to be constant, let's see the relationship between speed and time. And as time goes up, the amount of speed to go that same distance goes down. So now your m equals a minus v slope of the line. As the speed increases, time decreases. Hence, the variable has a negative relationship. Again, there's our definition. positive relationship and negative relationship dependent on the slope of the line and with a simple formula like this um and even a significant amount of data. Let's uh see what the mathematical implementation of linear regression and we'll take this data. So suppose we have this data set where we have xyx= 1 2 3 4 5 standard series and the y value is 3 22 43. When we take that and we go ahead and plot these points on a graph, you can see there's kind of a nice scattering and you could probably eyeball a line through the middle of it. But we're going to calculate that exact line for linear regression. And the first thing we do is we come up here and we have the mean of Xi. And remember mean is basically the average. So we added five plus 4 plus 3 plus 2 plus 1 and divide by five. And that simply comes out as three. And then we'll do the same for y. We'll go ahead and add up all those numbers and divide by five. And we end up with a mean value of y of i equals 2.8 where the x i references it's an average or means value. And the yi also equals a means value of y. And when we plot that, you'll see that we can put in the y= 2.8 and the x= 3 in there on our graph. We kind of gave it a little different color so you could sort it out with the dash lines on it. And it's important to note that when we do the linear regression, the linear regression model should go through that dot. Now, let's find our regression equation to find the best fit line. Remember, we go ahead and take our y= mx plus c. So, we're looking for m and c. So, to find this equation for our data, we need to find our slope of m and our coefficient of c. And we have y = mx + c where m equals the sum of x - x average * y - y average or y means and x means over the sum of x - x means squared. That's how we get the slope of the value of the line. And we can easily do that by creating some columns here. We have xy. Computers are really good about iterating through data. And so we can easily compute this and fill in a graph of data. And in our graph you can easily see that if we have our x value of 1 and if you remember the x i or the means value is three 1 - 3 equals a -2 and 2 - 3 = a -1 so on and so forth and we can easily fill in the column of x - x i y - yi and then from those we can compute x - x i^ 2 and x - x i * y - yi and you can guess it that the next step is to go ahead and sum the different columns for the answers we need. So we get a total of 10 for our x - x i^ 2 and a total of 2 for x - x i * y - yi and we plug those in. We get 2/10 which equals2. So now we know the slope of our line equals2. So we can calculate the value of c. That'd be the next step is we need to know where it crosses the y ais. And if you remember, I mentioned earlier that the linear regression line has to pass through the means value, the one that we showed earlier. We can just flip back up there to that graph. And you can see right here, there's our means value, which is 3, x= 3, and y= 2.8. And since we know that value, we can simply plug that into our formula. y = 2x + c. So we plug that in, we get 2.8 8 =2 * 3 + c and you can just solve for c. So now we know that our coefficient equals 2.2. And once we have all that, we can go ahead and plot our regression line. y =2 * x + 2.2. And then from this equation, we can compute new values. So let's predict the values of y using x= 1 2 3 4 5 and plot the points. Remember the 1 2 3 4 5 was our original x values. So now we're going to see what Y thinks they are, not what they actually are. And we plug those in, we get Y of designated with Y of P. You can see that X= 1= 2.4, X= 2= 2.6, and so on and so on. So we have our Y predicted