AI Foundations Course – Python, Machine Learning, Deep Learning, Data Science

Key Takeaways

learn about machine learning and AI with this comprehensive 11-hour course this is not just a crash course this course covers everything from fundamental concepts to Advanced algorithms complete with real world case studies in recommender systems and Predictive Analytics this course goes beyond Theory to provide Hands-On implementation experience career guidance and great insights from industry professionals it also includes a career guide on how to build a data science career launch a startup and prepare for interviews T van vahi from lunarch developed this course over 80% of the companies worldwide are unable to find data scientists and AI professionals to bring their ideas into the market and to become more competitive in the next decade the demand for data science and ml professionals is only going to increase as the market in the data science nii is projected to pass the 400 billion valuation about 5% of all employees worldwide are asking their employers to get training in the field of generative Ai and machine learning and if this didn't convince you to get into this lucrative and highly demanded industry then let me tell you that the salaries of data science and AI professionals are at the moment about the $150 up to $200,000 in us and in some cases the salaries of most in demand Ai and machine learning Prof professionals can pass the $500,000 us welcome to this involved crash course in machine learning and data science in this 11 hours you will get a comprehensive overview of the machine learning and data science from different perspective both the theory practice implementation career insights and what you can expect from this career this will be a great course for anyone who wants to become a machine learning engineer or AI engineer so here's what we are going to cover as part of this comprehensive Crush course in machine learning so we are going to start with the machine learning roadmap for 20124 here we are going to provide you a structured overview of the machine learning landscape helping you understand what is like to become a machine learning engineer what you can expect from this career what exactly you need to learn what kind of skill sets from what kind of Industries and also you are going to see what kind of career directions you can take in the field of machine learning so how you can get into machine learning how you can Kickstart a career and what is that that you need to learn after that we are going to get into the top machine learning algorithms so here you will learn the most important machine learning algorithms from linear regression to Advanced algorithms like the boosting algorithms of course this won't be your comprehensive machine learning course because this is aimed to provide you the basics and the fundamentals but this would be a great starting point for you to get a taste what it's like to learn the theory of machine learning you will learn the theory you will also learn the definitions the pros and the cons of these algorithms along with the Practical python implementation so this will be great way to learn the basics and to also learn how to implement this in Python of course as a prerequisite for this it is required that you know basics in Python like how to create list how to work with pyit learn or how to create variables so for this this is important next up we have the handson case studies so after learning the basics in machine learning in terms of the theory and implementation in Python with the real examples you are ready to get into a handson machine learning work and this won't be just quick case studies that you can complete in 30 minutes but rather those will be involved three different case studies so we will start with the basics like performing a behavior analysis and data analytics which is always a must when it comes to becoming machine learning or AI Engineers so you will learn how to perform data analytics how to perform customer segmentation using python how to perform data wrangling how to do exploratory data analysis all in Python and and then to make those important conclusions and tell your data story this is really important as AI professional to know data science and data analytics so this first case study which is the superstore customer Behavior Analysis that will be conducted and presented to you by vah asan co-founder of ler Tech that will provide you a good insights into the basics of machine learning and how to do a data analytics and data science in a real live case study in the second case study we will then get more Hands-On with machine learning and uh we will be predicting the Californian house prices we will do expiratory data analysis we will use Python to clean the data use statistics to perform outl detection data visualization we will also perform causal analysis and we will be using linear regression to perform the predictions by leveraging practical data analytics but this time also data science skills and combining this with python libraries like psychic learn and the third case study will be about building a movie recommender system so here we will explore the NLP the natural language pre processing another very important topic in the field of AI and machine learning these days and here we'll be using NLP we will use also machine learning data science tool to develop a recommended algorithm so this project will then enhance your skills in the text Data analysis how to process this text Data how to use Python for doing that as well as practical machine learning applications like building a recommender system keep in mind that you can also put this case studies on your resume to Showcase your experience after we are done with this tree end to endend invol case studies we are going to provide you career insights now as a data science and AI professional you have two choices you can either decide to get into the corporate world so become a data scientist or a professional or you can decide to build your own startup and to provide you information on both of these directions in the first conversation you will join me and the data science manager from Aliens Cornelius where you can learn from him how to break into the field into the field of data science and machine learning especially from traditional background here you can get lot of tips on succeeding in this field how to get promoted what to expect from interviews what is like that selection process and much more about data science nii corporate career so once we are done with that conversation then we will provide you the next choice which is about the building of a startup as a machine learning or AI professional so here you can then listen to the conversation between co-founder of lunar Tech vah asan and a Serial entrepreneur and successful investor Adam coffee so here Adam coffee will be then provide you a lot of insights on how to launch a startup how to raise funds what to expect from this uh type of career so once we are done with all this with this career insight as well we will then get into the final part of this course which is about interview preparation we'll conclude with providing you the most popular machine learning interview questions with the corresponding detailed answers this will be great for anyone who wants to Ace their interviews and who is now preparing for machine learning or AI interviews this crash course for 11 hours is more than just a short introduction it's an involved comprehensive overview of everything that you can expect from the world of machine learning and AI want to become uh more handson and get the entire comprehensive overview and learn everything in one place to become a job ready machine learning and professional then make sure to check the lunch. our data science boot camps and many other courses that will provide you that all in one approach to become a job rate professional if you like this video make sure to like subscribe and comment so if you ready I'm really excited let's get started hi there in this video we are going to talk about how you can get into machine learning in 2024 first we are going to start with all the skills that you need in order to get into machine learning step by step what are the topics that you need to cover and what are the topics that you need to study in order to get into machine learning we are going to talk about what is machine learning then we are going to cover step by step what are the exact topics and the skills that you need in order to become a machine learning researcher or just get into machine learning then we're going to cover the type of exact projects you can complete so examples of portfolio projects in order to put it on your resume and to start to apply for machine learning related jobs and then we are going to also talk about the type of Industries that you can get into once you have all the skills and you want to get into machine learning so the exact career path and what kind of business titles are usually related to machine learning we are also going to talk about the average salary that you can expect for each of those different machine learning related positions at the end of this video You're are going to know what exactly machine learning is where is it used what kind of skills are there that you need in order to get into machine learning in 2020 4 and what kind of career path with what kind of compensation you can expect with the corresponding business titles when you want to start your career in machine learning I'm DF co-founder of lunar Tech and I come from econometrical and statistical background I been in the tech field and specifically in data science and AI for The Last 5 Years working across different data science and AI projects across the globe and now I'm going to tell you what exactly machine learned is and what are the skill sets that you need in order to get into machine learning in 2024 so without further Ado let's get started so what is machine learning machine learning is a brand of artificial intelligence of AI that helps to uh build models based on the data and then learn from this data in order to make different decisions so we will first start with the definition of machine learning what machine learning is and what are the different sorts of applications of machine learning that you most likely have heard of but you didn't know that it was based on machine learning so machine learning is a brand of artificial intelligence that is uh using data in order to uh learn from this data by using different sorts of algorithms and it's being used across different Industries uh starting from Healthcare till entertainment in order to improve uh the customer uh experience custom identify customer behavior um improve the sales for the businesses uh and it also helps um governments to make decisions so it really has a wide range of applications so let's start with the healthcare for instance machine learning is being used in the healthcare to help with the uh diagnosis of diseases it can help to uh diagnose cancer uh during the co it helped many hospitals to identify whether people are getting more uh severe effects or they are getting P pneumonia um based on those pictures and that was all based on machine learning and specifically computer vision uh in the healthcare is also being used for drug Discovery it's being used for personalized medicine for personalizing treatment plans to improve the operations of the hospitals to understand what is the amount of uh people and uh patients that hospital can expect in each of those uh uh days week and also to estimate the amount of doctors that need to be available the amount of uh people uh that the hospital can expect in the uh emergency room based on the day or the time of the day and this is basically not a machine learning application then we have uh machine learning in finance machine learning is being largely used in finance for different applications starting from fraud detection in credit cards or in other sorts of banking operations um it's also being used in trading uh with specifically in combination with quantitative Finance to help traders to make decisions whe they need to go short or long into different stocks or bonds or different assets just in general to estimate the price that those talks will haveen Assets in the real time in the most accurate way uh it's also being used in uh retail uh it helps you understand an estimated demand for certain products in certain Warehouse houses it also helped you understand what is the most appropriate or closest uh uh warehouses that the items for that corresponding customer should be shipped so it's optimizing the operations it's also being used to build different Dr Commander systems and search engines like the famous Amazon is doing so every time when you go to Amazon and you are searching for project or product you will most likely see many article recommenders and that's based on machine learning because Amon zone is uh Gathering the data and comparing your behavior So based on what you have bought based on what you are searching uh to other customers and those items to other items in order to understand what are the items that you will most likely will be interested in and eventually will buy it and that's exactly based on machine learning and specifically different sorts of recommended system algorithm and then we have uh marketing where machine learning is being heav used because this can help to understand uh what are these different tactics and specific targeting uh groups that you belong and how retailers can Target you uh in order to reduce their marketing cost and to result in high conversion rates so to ensure that you buy their product then we have machine learning in autonomous vehicles that's based on machine learning and specifically uh deep learning applications uh and then we have also so um uh natural language PR processing which is highly related to the famous Chad GPT I'm sure you are using it and that's based on the machine learning and specifically the large language models so the Transformers large language models where you will going and providing your text and then question and the chat GPT will provide answer to you or in fact any other uh virtual assistant or chat boats those are all based on machine learning and then we have also uh smart home devices so Alexa is based on machine learning also in agriculture uh machine learning is being used heavily these days to estimate what the weather conditions will be uh to understand what will be the uh production of different plants uh what will be the um outcome of this uh to understand and to make decisions uh also how they can optimize those uh crop uh yields to monitor for uh soil health and for different sorts of applications that can just in general uh improve the uh revenue for the farmers then we have of course in the entertainment so the Vivid example is Netflix that uses the uh data uh that you are providing uh related to the movies and also based on what kind of movies you are watching Netflix is uh building this super smart recommender system to recommend you moving that you most likely will be interested in and you will also like it so in all this machine learning is being used and it's actually super powerful topic and super powerful uh field to get into and in the upcoming 10 years this is only going to grow so if you have made that decision or you are about to make that decision to get into machine learning continue watching this video because I'm going to tell you exactly what kind of skills you need and what kind of uh practice Pro objects you can complete in order to get into machine learning in 2024 so you first need to start with mathematics you also need to know python you also need to know statistics you will need to know machine learning and you will need to know some NLP to get into machine learning so let's now unpack each of those skill sets so independent the type of machine learning you are going to do you need to know mathematics and specifically you need to know linear algebra so you need to know what is matrix multiplication what are the vectors matrices dot product you need to know how you can uh multiply those different matrices Matrix with vectors what are these different rules the dimensions also what does it mean to transform a matrix the inverse of the Matrix identity Matrix diagonal matrix uh those are all Concepts as part of linear algebra that you need to know as part of your mathematical skill set in order to understand those different machine learning algorithms then as part of your mathematics you also need to know calculus and specifically differential Theory so you need to know these different theorems such as chain rule the rule of uh differentiating when you have sum of instances when you have constant multiply with an instance when you have um uh sum but also subtraction division multiplication of two items and then you need to take the uh derivative of that what is this idea of derivative what is the idea of partial derivative what is the idea of haian so first order derivative second order derivative and it would be also great to know a basic integration Theory so we have differentiation and the opposite of it is integration Theory so this is kind of basic you don't need to know uh too much when it comes to calculus but those are basic things that you need to know uh in order to succeed in machine learning uh then the next Concepts uh such as discrete mathematics so you need to know uh what is this idea of uh graph Theory uh what are this uh combinations combinators uh what is uh this idea of complexity which is important when you want to become a machine learning engineer because you need to understand what is this Big O notation so you need to understand what is this complexity of uh and squ complexity of n complexity of n log n um and about that you need to know uh some basic uh mathematics when it comes which comes from usually high school so you need to know multiplication division you need to understand uh multiplying uh uh amounts which are within the parenthesis you need to understand um different symbols that represent mathematical um values you need to know this idea of using X is y uh and then what is x s what is y^ 2 What is X the^ 3 so different exponents of the different variables then you need to know what is logarithm what is logarithm at the base of two what is logarithm at the base of e and then at the base of 10 uh what is the idea of e so what is the idea of Pi uh what is this idea of uh exponent logarithm and how does those uh transform when it comes to taking derivative of the logarithm taking the derivative of the uh exponent those are all values and all uh topics that are actually quite basic they might sound complicated but they are actually not so if someone explains you uh clearly then you will definitely understand it from the first goal and uh for this uh to understand all those different mathematical Concepts so linear algebra calculus differential Theory and then discrete mathematics and those different symbols you need to uh go for instance uh and look for courses or um YouTube tutorials that are about uh basic mathematics uh for machine learning and AI uh don't go and look further you can check for instance Can Academy which is uh quite favorite when it comes to learning math uh both for uni students and also for just people who want to learn mathematics and this will be your guide um or you can check our resources at lunch. cuz we are going also to uh provide this resources for you uh in case you want to learn mathematics for your machine Learning Journey the next skill set that you need to gain in order to break into machine learning is the statistics so you need to know this is a must statistics if you want to get into machine learning and in AI in general so there are few topics that you must um study when it comes comes to statistics and uh those are descriptive statistics multivariate statistics inferential statistics probability distribution and some bial thinking so let's start with descriptive statistics when it comes to descriptive statistics you need to know what is side of mean uh median standard deviation variance and uh just in general how you can uh analyze the data with using this descriptive measure me so distance measures but also variational measures then the next topic area that you need to know as part of your statistical journey is the inferential statistics so you need to know those Infamous theories such as Central limit theorem the law of uh large numbers uh and how you can um relate to this idea of population sample unbias sample and also u a hypothesis testing confidence interval statistical sign ific an uh and uh how you can test different theories by using uh this idea of statistical significance uh what is the power of the test what is type one error what is type two error so uh this is super important for understanding different SS of machine learning applications if you want to get into machine learning then you have probability distributions and this idea of probabilities so to understand those different machine learning Concepts you need to know what are probabilities so what is this idea of probability what is this idea of Sample versus population uh what is what does it mean to estimate probability what are those different rules of probability so conditional probability uh and um those uh probability uh values and rules that usually you can uh apply when you have uh a probability of um multipliers probability of sums um and then uh you need to understand some uh popular and you need to know some popular probability distribution functions and those are pero distribution binomial distribution uh normal distribution uniform distribution exponential distribution so those are all super important distributions that you need to know in order to understand uh this idea of normality normalization uh also uh this idea of bar noly trials and uh relating uh different probability distributions to different uh uh higher level statistical Concepts so rolling a dice the probability of it how it is related to bero distribution or to binomial distribution and those are super important when it comes to hypothesis testing but also for uh many other machine learning applications so then we have the ab thinking this is super important when it comes to more advanced machine learning but also some basic machine learning you need to know what is the Bas theorem which arguably is one of the most popular statistical theorems out there comparable also to the central limit theorem you need to know what is conditional probability what is this biased theorem and how does it relate to conditional probability uh what is this uh bation uh statistics Ide at very high level you don't need to know everything in uh super detailed but you need to know um the these Concepts at least at higher level in order to understand machine learning so to learn statistics and the fundamental concepts of Statistics you can check out the fundamentals to statistics course at lunch. here you can learn all this required Concepts and topics and you can practice it in order to get into machine learning and to gain the statistical skills the next skill set that you must know is the fundamentals to machine learning so this covers not only the basics of machine learning but also the most popular machine learning algorithms so you need to know this uh different um mathematical side of these algorithms step by step how they work what are the benefits of them what are the demo and and which one to use for what type of applications so you need to know this uh categorization of supervised versus unsupervised versus semi-supervised then you need to know what is the idea of classification regression or uh clustering then you need to know uh also time series analysis uh you also need to know uh these different popular algorithms including linear regression also logistic agression LDA so linear discriminant analysis you need to know KNN you uh need to know uh decision treats both classification and regression case you need to know uh random Forest bagging but also boosting so popular boosting algorithms like uh light GBM GBM uh so gradient boosting models and you need to know uh HG boost uh you uh also need to know um some supervised learning algorithm such as K means uh usually Ed for class string you need to know DB scan which becomes more and more popular in uh class string algorithms you also need to know hierarchal class string um and um for all this type of uh models you need to understand the idea behind them what are the advantages and disadvantages whether they can be applied for unsupervised versus supervised versus semi-supervised you need to know whether they are for regression classification or for uh clustering beside of this popular algorithms and models you also need to know the basics of uh training a machine learning model so you need to know uh this process behind training validating and testing your machine learning algorithms so you need to know uh what does it mean to uh perform hyperparameter tuning what are those different optimization algorithms that can be used to optimize your parameters such as uh GD SGD SGD with momentum Adam and Adam V you also need to know the testing process this idea of splitting the data into train validation and then test you need to know resampling techniques why are they used including the um bootstrapping and uh cross viation and there's different sorts of cross viation techniques such as leave one out cross viation kful cross viation validation set approach uh you also need to know um this uh idea of uh Matrix and how you can use different Matrix to evaluate your machine learning models such as uh classification type of metrics like F1 score FB D Precision recall um cross entropy um and also you need to know some Matrix that can be used to evaluate regression type of problems like the uh mean squared error so M root me squared error RMC uh MAA so the uh absolute uh version of those different sorts of Errors um and um or the residual sum of squares for all these cases you not only need to know higher level what the those algorithms or those uh uh topics or concepts are doing but you actually need to know the uh mathematics behind it their benefits the uh disadvantages because during the interviews you can definitely expect questions that will test uh not only your higher level understanding but also this uh background knowledge if you want to learn machine learning and you want to gain those skills then uh feel free to check out my uh fundamentals to machine learning course which is part of the ultimate data science boot camp at lunch. or you can also check out and download for free the fundamentals to machine learning handbook that I published with free cord Camp then the next skill set that you definitely need to gain is a knowledge in python python is actually one of the most popular programming languages out there and it's being used across software Engineers uh AI Engineers machine learning Engineers data scientists so this this is the universal language I would say when comes to programming so if you're considering getting into machine learning in 2024 then python will be your friend so knowing the theory is one thing then uh implementing it uh in in the actual job is another and that's exactly where python comes in handy so you need to know python in order to perform uh descriptive statistics in order to trade machine learning model or more advanced machine learning models or deep learning models you can use for training while ation and uh for testing of your models and uh also for building different sorts of applications so python is super powerful therefore it's also gaining such a high uh popularity across the globe because it has so many uh liaries it has uh tenser flow pie torch both that uh are must if you want to not only get into machine learning but also the advanced levels of machine learning so if you are are considering the AI engineering jobs or machine learning engineering jobs and uh you want to train for instance deep learning models uh or you want to build large language models or generative AI models then you definitely need to learn uh pie torch and tensor flow which are Frameworks that I used in order to uh Implement different deep learning uh which are Advanced machine learning models here are a few libraries that you need to know in order to uh get into machine learning so you definitely need to know pandas numai you need to know psyit learn scipi you also need to know uh nltk for the text Data you also need to know tensor flow and Pythor for a bit more advanced machine learning and um beside this there are also data visualization libraries that I would definitely suggest you to practice with which are the ma plot lip and specifically the pie plot and also the curn when it comes to python beside knowing how to use libraries you also need to know some basic data structures so you need to know what are these variables how you can create variables what are the matrices arrays how the indexing works and also uh what are the lists what are the sets so unique lists uh What uh are the ways that you can what are the different operations you can perform uh how does the Sorting for instance work I would definitely suggest you know um some basic data structures and algorithms such as binary sort so an optimal way to sort your arrays you also need to know uh the data processing in Python so you need to understand how to identify missing data how to uh identify uh duplica in your data how to clean this how to perform feature engineering so how to combine uh multiple variables or to perform operations to create new variables um you also need to know uh how you can aggregate your data how you can filter your data how you can sort your data and of course you also need to know how you can form AB testing in your Python and how you can train machine learning models how you can test it and how you can evaluate them and also visualize the performance of it if you want to Learn Python then the easiest thing you can do is just to Google for uh python for data science or python for machine learning tutorials or blogs or you can even try out the python for data science course at Learner tech. in order to learn all these Basics and usage of these libraries and some practical examples when it comes to python for machine learning the next skill set that you need to gain in order to get into machine learning is the basic introduction to NLP natural language processing so you need to know how to work with text Data given that these days the text data is the Cornerstone of all these different different Advanced algorithms such as uh gpts Transformers the attention mechanisms so those uh applications that you see as part of building chat boat or this uh personalized uh applications based on Tex data they are all based on NLP so therefore you need to know this basics of NLP to just get started with machine learning so you need to know uh this idea of text Data what are those strings uh how you can clean text dat data so how you can clean uh those um dirty data that you get and what are the steps involved such as lower casing uh removing punctuation tokenization uh also what is this idea of stemming lemmatization stop wordss how you can use the nltk in Python in order to perform this cleaning you also need to know uh this idea of embeddings and uh you can also learn this idea of uh the uh tfidf which is a basic uh NLP algorithm uh you also uh can learn this idea of word embeddings uh the subword embeddings uh and the character embeddings if you want to learn the basics of NLP you can check out those Concepts and learn them as part of the blogs there are many tutorials on YouTube you can also try the introduction to uh NLP course at lunch. in order to learn this uh different Basics that form the NLP if you want to go beyond this uh intro till medium level machine learning and you also want to learn bit more advanced machine learning and this is something that you need to know after you have gained all these previous skills that I mentioned then you can gain uh this uh knowledge and the skill set by learning deep learning and also uh you can consider uh getting into generative AI topics so you can for for instance learn what are the RNN what are the Anns what are the CNN you can learn what is this uh outand coder concept what are the variational out hand coders what are the uh generative adversarial networks so gens uh you can understand what is this idea of reconstruction error uh you can understand this um this different sorts of neural networks what is this idea of back propagation the optimization of these algorithms by using the different optimization algorithms such as GD D HGD um HD momentum Adam Adam W RMS prop uh you uh can also go One Step Beyond and you can uh get into generative AI topics such as um uh the uh variation Auto encoders like I just mentioned but also the large language models so if you want to move towards the NLP side of generative Ai and you want to know how the ched GPT has been invented how the gpts work or the birth mode Ro uh then you will definitely need to uh get into this topic of language model so what are the end grams what is the attention mechanism what is the difference between the self attention and attention what is uh one head self attention mechanism what is multi-head self attention mechanism you also need to know at high level this uh encoder decoder architecture of Transformers so you need to know the architecture of Transformers and how they solve different problem s of reur narrow networks or RNN and lstms uh you can also look into uh this uh uh encoder based or decoder based algorithm such as uh gpts or Birch model and those all will help you to not only get into machine learning but also stand out from all the other candidates by having this advaned knowledge let's now talk about different sorts of projects that you can complete in order to train your machine learning skill set that you just learned uh so there are few projects that I suggest you to complete and you can put this on your resume to start to apply for machine learning roles the first application in the project that I would suggest you to do is building a basic recommender system whether it's a job recommender system or a movie recommender system in this way you can show case how you can use for instance text Data from those job advertisement how you can use numeric data such as the ratings of the movies in order to build a top end recommended system this will showcase your understanding of the distance measures such as cosign similarity this Cann algorithm idea and this will help you to uh uh tackle this specific uh area of data science and machine learning the next project I would suggest you to do will be to build a regression based model so in this way you will showcase that you understand this idea of regression how to work with a Predictive Analytics and predictive model that has a dependent variable response variable that is in the numeric format so here for instance you can uh estimate the salaries of the jobs based on the uh characteristics of the uh job based on this data which you can get for instance from uh open source uh web pages such as kegle and you can then uh use different sorts of regression algorithms to perform your predictions of the salaries evaluate the model and then compare the uh performance of these different machine learning regression based algorithms for instance you can use the uh linear regression you can use the decision trees regression version you can use the um uh random Forest you can use uh GBM XG boost in order to Showcase and then in one uh graph to compare this uh performance ments of these different algorithms by using single regression uh ml modal metrics so for instance the rmsc this project will showcase that you understand how you can train a regression model how you can test it and validate it and it will showcase your understanding of optimization of this regression algorithm you understand this concept of hyperparameters unun the next project that I would suggest you to do in order to Showcase your classification knowledge so when it comes to uh predicting a class for an observation given uh the feature space would be uh to uh build a classification model that would classify emails being a Spam or not a Spam so you can use a publicly available data that will be uh describing a specific email and then you will have multiple emails and the idea is to uh build a machine learning model that would classify the email to the class zero and class one where class zero for instance can be your uh not being a Spam and one being a Spam so with this binary classification you will showcase that you know how to train a machine learning model for classification purposes and you can here use for instance logistic regression you can use also the decision tra for classification case you can also use random Forest the uh EDG for classification GBM for classification and uh with all these models you can then obtain the performance metrics such as uh F1 score or you can plot the r curve uh or the uh area under the curve metrix and you can also compare those different classification models so in this way you will also tackle another area of expertise when it comes to the machine learning then the final project that I would suggest you to do would be uh from the unsupervised learning to Showcase another area of exper and here you can for instance use data to your customers into good better and best customers based on their transaction history the amount of uh money that they are spending in the store so uh in this case you can for instance use uh K means uh DB scan hierarchal clustering and then you can evaluate your uh clustering algoritms and then select the one that performs the best so you will then in this case cover yet another area of machine learning which would be super important to Showcase that you can not only handle recommender systems or supervised learning but also unsupervised learning and the reason why I suggest you to uh cover all these different areas and complete this four different projects is because in this way you will be covering different expertise and areas of machine learning so you will be also putting projects on your uh resume that are covering different s s of algorithms different sorts of uh Matrix and approaches and it will showcase that you actually know a lot from machine learning now if you want to go beyond the basic or medium level and you want to be considered for medium or Advanced machine learning uh levels and positions you also need to know bit more advanced which means that you need to complete with more advanced projects for instance if you want to apply for generative AI related or large language models related positions I would suggest you to complete a project where you are building a very basic uh large language model and specifically the pre-training process which is the most difficult one so in this case uh for instance you can build a baby GPT and I'll put a here link that you can follow where I'm building a baby GPT a basic pre-trained GPT algorithm where uh I am using a text Data uh publicly available data in order to uh uh process data in the same way like GPT is doing and the encoded part of the Transformers in this way you will showcase to your um hiring managers that you understand this architecture behind Transformers architecture behind the um uh large language models and d gpts and you understand how you can use pych in Python in order to do this Advanced NLP and generative AI task and finally let's now talk about the common career path and the business titles that you can expect from a career in machine learning so assuming that you have gained all the skills uh that are must for breaking into machine learning there are different sorts of business titles that you can apply in order to get into machine learning so when it comes to machine learning uh you can uh get into machine learning uh and there are different fields that are covered as part of this so uh first we have the general machine learning researcher machine learning researcher is basically doing a research so training testing evaluating different machine learning algorithms they are usually people who come from academic background but it doesn't mean that you cannot get into machine learning research without getting a degree in statistics mathematics or in um uh machine learning specifically not at all so uh if you have this um desire and this passion for reading doing research uh and you don't mind reading uh research papers then machine learning researcher job would be a good fit for you so machine learning combined with research then sets you uh for the machine learning researcher role then we have the machine learning engineer so machine learning engineer is the engineering version of the machine learning uh expertise which means that we are combining machine learning skills with the engineering skills such as productionizing pipelines or in robust pipeline scalability of the model considering all these different aspects of the model not only from the performance side when it comes to the quality of the algorithm but also the uh scalability of it and when putting it in front of many users so when it comes to combining engineering with machine learning then you get machine learning engineering so if you are someone who is a software engineer and you want to get into machine learning then machine learning engineering would be the best fit for you so for machine learning engineering you not only need to have all these different skills that I already mentioned but you also need to have this good grasp of uh uh scalability of algorithms the uh uh data structures and algorithms type of um skill set uh the uh complexity of the moral uh also system design so this one uh converges more towards and similar to the software engineering position combined with machine learning rather than your pure machine learning or AI role then we have the AI research versus AI engineering position so uh the uh AI research position is similar to The Machine learning uh research position and the AI engineer position is similar to The Machine learning engineer position with only single difference when it comes to machine learning we are specifically talking about this traditional machine learning so linear regression logistic regression and also uh random Forest exy boost begging and when it comes to AI research and AI engineer position here we are tackling more the advanced machine learning so here we are talking about deep learning models such as RNN lstms grus CNN or Computer visional Applications and we are also talking about uh generative AI models large language models so uh we are talking about um the Transformers the implementation of Transformers the gpts T5 all these different algorithms that are from uh more advanced uh AI topics rather than traditional machine learning uh for those you will then be applying for AI research and AI engineering positions and finally you have these different sorts of observations niches from AI for instance NLP research and NLP engineer or even data science positions for which you will need to know machine learning and knowing machine learning will set you apart for the source of positions so also the business titles such as data science or technical data science positions NLP researcher NLP engineer for this all uh you will need to know machine learning and knowing machine learning will help you to break into those positions and those career paths in this lecture we will go through the basic concepts in machine learning that is needed to understand and follow conversations and solve main problems using machine learning strong understanding of machine learning Basics is an important step for anyone looking to learn more about or work with machine learning we'll be looking at the Tre Concepts in this tutorial we will Define and look into the difference between supervised and unsup rvis machine learning models then we will look into the difference between the regression and classification type of machine learning models after this we will look into the process of training machine learning models from scratch and how to evaluate them by introducing performance metrics what you can use depending on the type of machine learning model or problem you are dealing with so whether it's a supervised or unsupervised whether it's regression versus classification type of problem machine learning methods are categorized into two types depending on the existence of the label data in the training data set which is especially important in the training process so we are talking about the So-Cal dependent variable that we saw in the section of fundamental Su statistics supervised and unsupervised machine learning models are two main type of machine learning algorithms one key difference between the two is the level of supervision during the training phase supervised machine learning algorithms are Guided by the labeled examples while as supervised algorithms are not as learning model is more reliable but it also requires a larger amount of labeled data which can be timec consuming and quite expensive to obtain examples of supervised machine learning models includes regression and classification type of models on the other hand unsupervised machine learning algorithms are trained on unlabeled data the model must find p patterns and relationships in the data without the guidance of correct outputs so we no longer have a dependent variable so unsupervised ml models require training data that consists only of independent variables or the features and there is no dependent variable or a label data that can supervise the algorithm when learning from the data examples of UNS supervised models are clust string models and outlier detection techniques supervised machine learning methods are categorized into two types depending on the type of dependent variable they are predicting so we have regression type and we have classification type some key differences between regression and classification include output type so the regression algorithms predict continuous values while the classification algorithms predict categorized values some key difference between regression and classification include the output type the evaluation metrics and their appc ation so with regards to the output type regression algorithms predict continuous values while classification algorithms predict categorical values with regard to the evaluation metric different evaluation metrics are being used for regression and classification tasks for example mean square theor is commonly used to evaluate regression models while accuracy is commonly used to evaluate classification models when it comes to Applications regression and classification models are used in entirely different types of applications regression models are often used for prediction tests while classifications are used for decision-making tasks regression algorithms are used to predict the continuous value such as price or probability for example a regression model might be used to predict the price of a house based on its size location or other features examples of regression type of machine Lear learning models are linear regression fix effect regression exus regression Etc classification algorithms on the other hand are used to predict the categorical volum these algorithms take an input and classify it to one of the several predetermined categories for example a classification model might be used to classify emails as a Spam or as not a Spam or to identify the type of aner in an image examples of classification type of machine learning models are logistic regression exus classification random Forest classification let us now look into different type of performance metrics we can use in order to evaluate different type of machine learning models for aggression models common evaluation metrics includes residual sum of squared which is the RSS mean squared eror which is the msse the root mean squared error or rmsc and the mean absolute error which is the m AE this metrix measure the difference between the predicted values and the True Values with a lower value indicating a better feed for the model so let's go through these metrics one by one the first one is the RSS or the residual sum of squares this is a metrix commonly used in the setting of linear regression when we are evaluating the performance of the model in estimating the different coefficients and here the beta is a coefficient and the Yi is our dependent variable value and the Y head is the predicted value as you can see the RSS or the residual sum of square or the beta is equal to sum of all the squar of y i minus y hat across all I is equal to 1 till n where I is the index of the each r or the individual or the observation included in the data the second Matrix is the MSC or the mean squared error which is the average of the squared differences between the predicted values and the True Values so as you can see m is equal to 1 / to n and then sum across all i y IUS y head squ as you can see the RSS and the msse are quite similar in terms of their uh formulas the only difference is that we are adding a 1 / to n and then this makes it the average across all the square differences between the predicted value and the actual True Value a lower value of Ms indicates a better fit the rmsc which is the root mean squar error is the square roof of the msse so as you can see it h