Introduction to Machine Learning

Key Takeaways

foreign tools like chat GPT exist and all of a sudden a lot of you want to dive into machine learning and artificial intelligence now I'm not here to validate that decision or to tell you that's the best possible path to go down but I am here to provide you a road map and make it as easy and stress-free as possible for you to dive into this world I myself learned quite a bit about machine learning and AI back when I was in university this was about three years ago I actually have a ton of videos on this channel related to machine learning and artificial intelligence and you can check those out for the link in the description I'll pop some of them up on the screen so I have a really good sense of what it takes to dive into this field what kind of algorithms you should be learning the level of math you need to know what programming language you should learn libraries and the next more advanced steps that you can take if you really are getting serious about that so with that said let's dive in and let me share with you how to learn machine learning and artificial intelligence in 2023 so let's begin here by walking through the steps that's involved in training a machine learning model or really developing some kind of artificial intelligence you're going to realize very quickly here that very little of what you're going to do as a machine learning engineer or as someone who's interested in artificial intelligence is actually building machine learning models or artificial intelligence a lot of this has to do with data pre-processing and also things like deploying testing and validating your model so keep that in mind this is not as glorious and as glamorous as a field as you might make it out to be and there's a lot of very frustrating time consuming and difficult work that goes into actually being successful in training machine learning models and creating artificial intelligence anyways let me walk you through the steps so step one in any machine learning application or project is to define the problem what is the problem what are you trying to solve what are your goals and objectives and how is machine learning going to help you do that now step two is to pick the data that you're going to be using for your model so data collection where are you getting this data from how are you storing it and how are you going to decide what data you actually need the next step is going to be data cleaning oftentimes when you collect data especially if you're getting it from the internet some kind of Open Source you're going to need to clean this data and get rid of a bunch of information that's unnecessary too much data can confuse a machine learning model too little obviously could be not enough to train it and you have to be careful how you're cleaning this data and putting it in a format such that it's usable for your model the next step is going to be data selection so now you should have this massive amount of data depending on the problem obviously they're going to be training a model on now you need to actually select the specific pieces of data that you want to use to train the model not all of your data is going to be relevant maybe there's some filters you need to apply there's a lot of stuff that could go on in this step next is going to be your model architecture or selecting a model oftentimes you don't need to reinvent the wheel you don't need to create your own custom model you simply need to pick an existing one and then train it using your data so once you pick your model now you're going to train the model so you're going to feed all of your cleaned and processed data into that model and then you're going to test and evaluate your model and make sure that it's actually working as you expect once you do that you're going to repeat this step countless times until you get a model that has an accuracy that you are happy with and then if you're going in a production environment you are going to deploy this model so the reason I wanted to share these steps with you is just so you have a better picture of what's actually involved and what you're going to be doing if you get into this field most of your work is not going to be training a model coming up with a model architecture and doing all of that fun stuff you might see in YouTube videos or hear about In Articles a lot of it is going to have to do with data collecting data processing data and then testing and evaluating your model and continuing to repeat that process until you hit a point that you are satisfied with so after listening to those steps some of you might be a little bit concerned that your computer may not be powerful enough to actually execute those steps and train kind of more advanced deep learning models now if that's the case you don't have to worry because I'm actually giving away an RTX 4080 this is courtesy of Nvidia which I've teamed up with for this video to anyone who enters the giveaway now to enter the giveaway you simply need to attend the GTC this is the global technology conference hosted by Nvidia this is from March 20th to 23rd you can register for free from the link in the description and if you attend one of the events at GTC and then you fill out the Google form that I have in the description I will enter you to win this RTX 4080 I will ship it anywhere in the world to you completely for free in addition to this Nvidia has a ton of awesome resources related to deep learning machine learning and artificial intelligence a bunch of them are free and you can check them out from the link in the description or if you actually want to access some of their paid courses I'm going to be giving away five discount codes which will give you free access to those courses again to enter to win those discount codes simply attend the GTC completely free fill out the Google form and then I will pick five of you and send you those codes thanks to Nvidia for teaming up with me for this video again they have a ton of awesome resources a ton of cool stuff they're sharing at the GTC and if you're interested in AI AI machine learning and technology in general you should definitely attend it's completely free it's virtual go and check out their itinerary a ton of really cool stuff going on alright so now I'm going to get a bit more serious and structured and share with you kind of step by step what you need to do if you want to get into machine learning and AI so step one is going to be to learn some kind of programming language that you can do machine learning and AI in now the language I typically recommend here is python obviously python is a very well-rounded language you can use it for a bunch of stuff not just machine learning and Ai and it's worth learning even if you're not getting into this field but it's also the top choice for performing machine learning and artificial intelligence there's all kinds of libraries that use Python that make it really easy to deploy or not deploy but to create deep learning models or machine learning models and it's what I would recommend you learn first you do not need to be a Python Master but I would say you need to be competent in the language you need to understand things like for Loops classes objects and gets you kind of an intermediate programming level if you wanted to do that you could check out my course programmingexpert.io from the link in the description Additionally you may learn something like rust this is not a language I know or that I've looked at so I'm not going to recommend that and there's some other more specific machine learning languages you can look at kind of related to data science but again I'm not going to recommend those I'm going to recommend python to pretty much everyone here so step one Learn Python get decent at it you don't need to be an expert so moving on the next thing on my roadmap here is going to be to learn some math or to have some kind of foundational math background now I typically recommend that people that are diving into machine learning and Ai and actually want to understand what's going on like they want to look at the math they want to see what's behind just this basic algorithm they want to have some intuition on what's going on and what's happening should have a understanding of linear algebra basic calculus like something you'd take in grade 12 or first year university and then probably understand things like matrices vectors multi-dimensional math and just be generally speaking okay at reading like mathematical notation and comfortable kind of digging into it a little bit I'm not telling you you have to be a math whiz I am not a math genius myself but I did go through two years of math courses in University these were the computer science math classes and that helped me tremendously when I was learning machine learn so if you don't know the math it's fine you can still continue here but I will warn you it's going to be much more difficult to really grasp what's going on and I'd highly recommend you don't skip this if you have access to these kind of math courses alright so the next thing I'm going to recommend here is that you get familiar with some of the popular modules and libraries related to machine learning in Python I'm going to list them out you don't necessarily need to learn all of them but you should know what each of them is the goal of each library and if you're learning something like numpy which is the first library that I'm going to recommend I'd suggest that you spend a bit of time to understand some of the core functions and methods in that module because they're used quite a bit in different machine learning tutorials and not necessarily explained or kind of detailed as much as they should be so anyways the first module I'm going to rec command is numpy if you learn only one module this is the one that I would learn then you can look at something like scikit learn tensorflow Pi torch and then pandas pandas is more of a data science Library used to kind of import data manipulate data and oftentimes you're going to see this used in different machine learning tutorials again you don't have to learn all of these different libraries but you should be familiar with what they are maybe the difference between something like Pi torch and tensorflow and when you may want to use kind of each Library some tutorials will just tell you use a specific Library if I were you I'd want to have a bit more information on what that is and why you're using it before you dive right into that alright so finally my next step is to actually learn some machine learning algorithms I told you at the beginning of the video machine learning is not as glamorous as it seems there's a lot of other stuff you need to learn and understand to be good at it and that's why only at this point I'm actually telling you okay get into some machine learning algorithms and learn those now what I always suggest is that you learn these kind of foundational it's very straightforward and basic machine learning algorithms before you dive into stuff like deep learning neural networks convolutional neural networks Q learning reinforcement learning all of that stuff that you guys probably get really excited about so I'm going to share with you a few different algorithms that I would learn and I would really focus on learning the math Behind These because it's not overly complicated relative to some of the other stuff in the ML and AI world and if you can understand these core algorithms you can already do quite a bit of very interesting stuff that's not overly complicated so this will help you build a bit of confidence really understand what it means to perform machine learning and a lot of your more complicated machine learning stuff will kind of be based off some of the math and foundational information and Theory you get from these algorithms so let me go through the algorithms the first one is linear regression then you have logistic regression KNN this is a form of clustering so this is K nearest neighbors then I have support Vector machines then decision trees and naive Bayes these are very very popular algorithms you can find all kinds of videos articles tutorials whatever you want on these and in fact I have an entire series on my channel that goes through most of these different algorithms I also have a massive video on the free code Camp channel it's about seven hours long that walks you through a full overview and introduction of machine learning using tensorflow so I'll link that in the description as well anyways I would go through these different algorithms understand the math behind them practice applying them using some sample data sets again there's a million resources on the internet for each of them and only after you feel comfortable with these algorithms would I move on to the next step alright so at this point in time you'll have a really solid understanding of machine learning a really good foundation some good fundamentals you know python you know some basic libraries you know some math you've learned these core algorithms and now it's time to move on to some more advanced complicated and interesting machine learning topics I'm going to share with you a few different ideas here and kind of avenues you could go down but at this point it's up to you you need to do your own research and you need to figure out what you actually want to do what interests you and what you want to learn so when it comes to machine learning algorithms you have four classifications of algorithms the first is supervised then you have unsupervised semi-supervised and reinforcement learning these are the four different classifications within each classification as multiple different algorithms and methods of machine learning and AI to give you a prime example if we talk about supervised machine learning we're looking at something like classifiers something like neural networks where we have labeled data that means we have say an image of a cat and we have it labeled as cat we have an image of a dog it's labeled as dog and we use those labels to train our machine learning model and get it to a point where it's accurate enough to say predict if an image is a cat or a duck okay so neural networks would be under supervised machine learning something you could definitely learn if you were interested in that there's all different kinds of neural networks as well you know many different kinds so you guys can have a look at those and see the different names for them next we have unsupervised learning unsupervised learning is when you have data but you don't necessarily have a classification or a label for it so an example of unsupervised learning would be something like K means clustering this is a clustering algorithm that is essentially going to group different data points or different pieces of data based on similar properties so you may be trying to look in a data set for something you don't quite know yet and figure out different clusters or groups of maybe people or information or cells or whatever it is that you're clustering all right next we have a semi-supervised learning now I'm not an expert in this so I'm not going to explain it too much essentially this is a situation in which you have a little bit of labeled data but not enough to train the entire model on so you train the model on the limited data that is labeled and then you kind of perform a complicated process where you use non-labeled data to continue to improve the accuracy of the model so that's why it's semi-supervised you have some labeled data you have some non-label data you're kind of mixing and matching and trying to you get an accurate model lastly we have reinforcement learning this is the coolest one in my opinion an example of reinforcement learning would be training an AI how to play a game essentially what you're doing is reinforcing certain actions and maybe I guess non-reinforcing I don't know what the opposite of that would be other actions where you're telling the model you did this that's good keep doing that you did this that's bad stop doing that so you're reinforcing specific behavior attempting to reach some kind of goal I actually did a reinforcement learning video where I used an evolutionary algorithm to train an AI how to play Flappy Bird again entire tutorial on this channel I will leave that in the description alright so that said I think I'm gonna wrap up the video here as a reminder I am giving away an RTX 4080 courtesy of Nvidia attend GTC where you can learn a lot more about AI machine learning and just technology in general and then fill out that Google form that I have in the description and you will be entered to win you'll also be entered to win five different discount codes that you can use to attend any Nvidia course paid or free from their website all kinds of awesome resources there if you really want to get serious about ML and AI thank you for watching the video If you enjoyed leave a like subscribe to the channel and I will see you in the next one [Music] foreign [Music]