The Different Types of Machine Learning Explained

Key Takeaways

hello everybody and welcome back so in this video what I'm gonna be doing is sharing with you my top four machine learning techniques that you need to know now there's so many different ways of doing machine learning and artificial intelligence but these are kind of the four main methods and you need to understand what these methods are so you can appropriately pick the right one for whatever project you're working on it's one thing to go through tutorials and to watch other people apply specific algorithms to specific projects but to actually be able to pick which algorithm that you're gonna use for your own project is its own skill and hopefully this video will help you do that so without further ado we're gonna have a quick word from our sponsor and then we'll get right into it before we get started I would like to thank simply learn for sponsoring this video and giving you all 10% off their data science master's course by enrolling with the code data with Tim this course will give you hands-on exposure to key technologies including our Python tableau Hadoop and spark and you'll receive world-class training by an industry leader on the most in-demand data science and machine learning skills since this course is co-developed with IBM you'll receive perks such as twelve hundred US dollars worth of IBM cloud credits access to IBM cloud platforms such as IBM Watson and digital badges from IBM you can check it out at the link in the description so I want to make it clear distinction just to start the video here on what I mean by machine learning a lot of people confuse artificial intelligence and machine learning and to be honest with you the internet does not make this easy to kind of distinguish between a lot of people will label their stuff as machine learning when really you know it's a different artificial intelligence strain or they'll say AI and it's not Mitch if people get really confused so let's clear it up right now machine learning is a strain of artificial intelligence artificial intelligence is kind of the top tier and everything kind of trickles down from there where machine learning is its own kind of topic within artificial intelligence you can have other things like evolutionary algorithms or you could have different kind of AI algorithms that do specific things that are in their own little bubble machine learning is one of its own bubbles that strains from artificial intelligence now the topics I'm gonna be talking about here in the methods are kind of strains off of machine learning so the different types of machine learning they're not necessarily algorithms there's algorithms that are involved with these methods but they're kind of their own strain and different algorithms will fit into that so with that being said let's talk about the first algorithm which is reinforcement learning now reinforcement learning is a very popular method for training AIS to play video games and that's kind of the example I'm gonna use to explain how this works now reinforcement learning is learning by trial and error and essentially what that means is our AI starts off being extremely stupid having no idea what to do it's seen no prior data before and doesn't know what the level looks like it doesn't know anything and it starts taking random actions so this is where I'm gonna introduce to us three concepts an agent and environment and a reward now our agent is gonna be our artificial intelligence and in this case when we're talking about a game it would be the player of the game or whatever AI you're trying to train our environment is gonna be if we're talking about a game maybe the level in which this agent is playing but in other situations your environment is kind of what you're exploring and what you're trying to optimize or do the best in and then we have a reward and the reward is what our agent is trying to maximize the agent is going to be navigating the environment and based on the actions it takes it's going to be given a reward that reward will come you know from the programmer they will determine what reward the agent gets at what specific scenario for doing what specific action and essentially the goal of the agent is maximize this roar so it starts navigating the level kind of figuring out what it needs to do in navigating the environment and as it does this it starts to realize based on the actions it takes which actions maximize the reward so if we're talking about maybe a Mario game for example Mario will start off maybe running left so going out of the level and it will be given a negative reward and told don't do that stop doing that so the next time this happens it's gonna go the other direction because it says well I know left is bad that minimizes my reward so where can I go to maximize it so don't start going right and then maybe it hits an obstacle it's gonna back okay so when I get to that point rather than hitting the obstacle which gives me a negative reward let's go around the obstacle let's do something that gives me a positive reward and this is the basic premise of reinforcement learning and now on to our next topic which is supervised machine learning now a lot of people to classify this as kind of the most basic and most boring form of machine learning and I would agree but that also makes it one of the most useful now a lot of people that get into machine learning think that you need to use very advanced MA to use neural networks and crazy evolutionary things to solve problems when in reality a lot of the problems that you have can actually be solved with supervised machine learning models now what is supervised machine learning well you start in supervised machine learning with some previous knowledge or some previous data that is step one you're not going to be doing supervised machine learning unless you have some data to work with and what you're going to be doing is essentially trying to come up with some function that can map the input of your data to the output of your data the reason you want to do this is because you have all this previous data and you want to come up with some kind of correlations between them so you want to say hey maybe if a student has high grades that would relate with them studying for a long amount of hours so what you're gonna do is say okay we handled this previous data we're gonna feed it into this machine learning algorithm and it's gonna come up with this function and your goal is with this function that when you pass it some input data that it's never seen before so some data that you're trying to make a prediction for it can give you an accurate prediction for that specific value so that's why you need a lot of data usually to make this work well so what you're gonna do is feed you know the input of this data into the model it's gonna look at it it's gonna spit out some output it's gonna compare that output to what the output should be and then say okay I need to make a tweak here or tune here to make this function a little bit better and this is why it's called a supervised machine learning algorithm because you can almost think of it as like a teacher teaching the model what to do and how to get better where it starts not being very good but as it sees more and more data the teachers like know do that do that you need to fix that tweak that it gets better better better better until you reach a point where you're satisfied with the accuracy of your model and that is supervised machine learning and the next topic to talk about is unsupervised machine learning so in supervised machine learning what we had was some input data and he actually had the answer to that input Deb so we had for example all this information about a student and maybe we're trying to predict their final grade we actually had a bunch of previous students final grades so when we ran through the model we could tell the model when it got something wrong hey you were you know one grade point off from what this grade actually should be based on your current state so let's tune you a bit let's fix you a bit and let's get better unsupervised machine learning we don't actually have the answers to our data we have a ton of different data and we actually don't know what kind of the answer is for what we're looking for the problem itself is slightly undefined and what we're trying to actually do with unsupervised machine learning is have the computer or the algorithm come up with some correlations between our data that we can't see ourselves so maybe we're trying to predict you know something with supervised machine learning we want to figure out what a grades gonna be we want to cluster this information together we want to classify something and we actually know the correct answer whereas here we want to come up with things about our data that we don't know so this is where we talk about something called clustering now I think I made a mistake and I mentioned clustering with supervised machine learning that is not correct clustering typically is gonna be an unsupervised machine learning in a common algorithm in unsupervised machine learning is k-means clustering where maybe we feed a bunch of data points so we only have input data we feed a ton of data points to our model or algorithm and what it does is actually clusters and figures out which data points are related together so maybe we're trying to figure out what you know information will relate someone to be a part of a specific ethnic group or something like that we're looking at some big data well we don't know how many groups we might have in our data how they might be we don't want to specify those so we just pass this information into the computer it does some of this magic stuff with the unsupervised machine learning algorithm and spits back to us a bunch of different clusters of this group and when we pass in a new data point that we haven't seen before well what it does is say okay you are a part of this cluster or you are a part of this group so unsupervised machine learning algorithms are typically used to try to figure out stuff about the data we're trying to determine different groups or different kind of pieces of information and some abstract correlations that we maybe can't see ourself as a programmer or as someone that's analyzing the information a final reminder that if you're interested in becoming an expert in data science to consider the simply learned data science masters course it's comprised of six full courses including our programming data science with Python machine learning tableau desktop qualified associate training Big Data Hadoop spark developer and data science capstone and has three electives such as IBM Watson for chatbots machine learning with our data science with sass now during this course should work on 15 plus real-life projects and learned 30 plus in-demand skills with lifetime access to all of the course content this course is listed at $1,300 which is as low as 115 dollars a month and you can use the discount code data with Tim for a limited time at 10% discount I would personally recommend this course and encourage you guys to take advantage of this offer and now to talk about deep learning now deep learning is by far one of the most kind of looked up to and just like advanced parts of machine learning now a lot of people that get into machine learning just immediately jump into deep learning they see all these neural network things on YouTube they see neural network neural network neural network and they start just learning it and doing it and this was great and you can do this but deep learning is really not applicable to a lot of different tasks or maybe it is but it's way overkill and you could solve a lot of the same tasks that people are doing with deep learning and neural networks which is basic supervised machine learning algorithms or reinforcement learning are simple you know very basic things to setup and to work with so what is deep learning well I've kind of said it already but it's pretty much just working within their own networks now the reason we call this deep is because a neural network has more than one layer so whereas in our other algorithms we kind of just had an input and an output and we had a function that kind of classified or did something with that with a neural network we have layers so we have an input layer we have some hidden layers in the middle we have an output layer and our data actually gets passed through all of these different layers to pick out specific high-level features of it now this is why neural networks are used a lot for classification tasks related to things that have patterns in them so for example doing object detection or image recognition a classic example is going to be with the amnestied data set what essentially you're hoping a neural network is gonna do when you train it on a data set like an amnesty data set which essentially is a bunch of handwritten digits and you want to figure out if there's zeros or if they're ones 2 through 9 is you're gonna pass all the pixels of this image in and then the network's actually gonna look at these pixels try to pick out like lines and features and curves and edges of it and and what the number is based on seeing all those different features that it's kind of created and and figured out so neural networks are really good at looking at higher dimensional features that you can't specifically see in the data set or that actually don't exist it will create those features for you based on the input that you're giving it but this means that it's very complicated it's very complex you don't usually understand how you're actually solving the problem when you're using a neural network because you kind of just give some input you get an output and you don't have any idea what happens in between and that's the dangerous thing when you run into an issue where your neural networks not performing correctly it's very difficult to tweak it and modify it and a lot of the times there's not much you can do because you really just don't understand what's happening in the middle so this has been kind of the four main topics in machine learning now I always like to say that I am no expert there's a lot that I still need to learn I'm a student myself and this stuff is really interesting to me and that's why I go out of my way to make videos like this and to teach other people because I want them to be able to learn the same things that I've done I find a lot of people on the internet what they do is explain things super complicated and just try to make it seem like they're very smart that's really not how I want to come off I want to explain this to you guys as simply as possible so that you can you know go out there and learn it yourself a lot of this stuff is really not that hard it's just you have to have a little bit of knowledge and know where to look to find the information so with that being said that has been the video if you guys learn something you'd like this and you want more content like this please leave a like on the video leave a comment down below and subscribe to the channel and with that being said I will see you guys