Machine Learning Full Course 2026 | Machine Learning Tutorial For Beginners | Simplilearn

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Hey everyone, welcome to machine learning full course by Simply Learn. If you have ever been amazed at how Google finishes your sentence, how Netflix suggests your next favorite show, or how chatbots seem to understand you, well, that's machine learning doing its magic. And here's the exciting part. By 2025, the magic will be more than just having skills across industries. From healthcare and finance to marketing and gaming, companies everywhere are on the hunt for people who can build smart systems, work with real world data and create AIdriven solutions. And yes, the career opportunities and the pay is pretty awesome. In India, machine learning professionals earn around 10 lakh to 25 lakh peranom. And in the US, salaries can easily cross up to $120,000 plus. Now, this course isn't just a theory and slides. It's a full learning journey built for beginners and future experts. We will begin by understanding what machine learning really is and walk you through part one of your ML tutorial. From there, we'll get into some powerful tools and concept. You get a beginner friendly intro for large language models and dive into rec neural networks. We'll also tackle important hands-on topics like KN&N, which is K nearest neighbors, reinforcement learning, and how to evaluate models using the confusion matrix. And because real skills come with real projects, we will also walk you through hands-on ML projects and also prepare you for job success with a roundup of the most important machine learning interview questions. So, grab your notebook now or open that Google Collab and let's get started because your journey into the world of AI starts right now. Also just a quick information guys before we comment. If you're interested in launching a high growth career in artificial intelligence and machine learning then this program might be the best thing you will ever come across. The professional certificate in AI and machine learning offered in pura university online in collaboration with simply learn and IBM isn't just another course. It's a complete career transforming experience. Ranked one online EIML certification by Career Karma. This program is designed to help you master the most in- demand skills in AI automation, chat, GPT, Genai, LLMs, deep learning, agentic frameworks and so much more. So whether you're starting or looking out to upskill, you'll get hands-on with 15 plus real world projects, explore tools like hugging face, tensorflow, mid journey and even build LLM based application. So guys, hurry up and enroll now and you can find a link in the description box below. >> What is machine learning? Hello, my name is Richard Kersner. I'm with the SimplyLearn team. That's www.simplearn.com. Get certified, get ahead. Today we're covering what is machine learning? What's in it for you? We're going to cover the basics of machine learning. What is machine learning? Artificial intelligence versus machine learning versus deep learning. How does machine learning work? Types of machine learning. Machine learning prerequisites. Applications of machine learning. Here we have our um it looks a little bit like Frankenstein, our Frankenstein looking robot. Today, let me tell you what is machine learning. Machine learning works on the development of computer programs that can access data and use it to automatically learn and improve from experience. Watch a robot builder construct a house in two days. This was back in July 29th, 2016. So, that's pretty impressive, this amount of time to continue to grow in his development. And it's smart enough to leave spaces in the brick work for wiring and plumbing and can even cut and shape bricks to size. Amazon Echo relies on machine learning. And with more data, it becomes more accurate. Play your favorite music, order pizza from Domino's, voice control your home, request rides from Uber. Have you ever wondered the difference between AI, machine learning, and deep learning? Artificial intelligence, a technique which enables machines to mimic human behavior. This is really important because this is how we are able to gauge how well our computations or what we're working on works is the fact that we're mimicking human behavior. We're using this to replace human work and make it more efficient and make it more streamlined and more accurate. And so the center of artificial intelligence is the big picture of all this put together. IBM deep blue chess electronic game characters. Those are just a couple examples of artificial intelligence. Machine learning a technique which uses statistical methods enabling machines to learn from their past data. So this means if you have your input from last time and you have your answer, you use that to help prove the next guess it makes for the correct answer. IBM Watson, Google search algorithm, email spam filters. These are all part of machine learning. And then deep learning, which is a subset of machine learning, composing algorithms that allow a model to train itself and perform tasks. Alph Go, natural speech recognition. These are a couple examples. Deep learning is associated with tools like neural networks where it's kind of a black box. As it learns, it changes all these things that are as a human we'd have a very hard time tracking. and it's able to come up with an answer from that. Now let's see how machine learning works. First we start with training the data. Once we've trained the data the train we go into the machine learning algorithm which then puts the data into a processing which then goes down to machine another machine learning algorithm. And then we take new data because you have to test whatever you did to make sure it works correctly and we put that into the same algorithm. Once we do that, we check our prediction. We check our results. And from the prediction, if we've set aside some training data and we find out it didn't do a good job predicting it and it gets a thumbs down, as you see, then we go back to the beginning and we retrain the algorithm. And a lot of times it's not just about getting the wrong answer. It's about continually trying to get a better answer. So you'll see the first time you might be like, "Oh, this is not the answer I want." Depending on what domain you're working in, whether it's medical, economical, business, stocks, whatever. You try out your model and if it's not giving you a good answer, you retrain it. If you think you can get a better answer, you retrain it and you keep doing that until you get the best answer you can. Let's see the types of machine learning. Supervised learning, unsupervised learning. There's a number of ways to divide up machine learning and how it works. These are two main categories you can divide it into. Supervised learning. We have a known amount of data. So in this case, we have a bunch of apples. We have a machine learning algorithm. It goes through the process. It goes through and trains the model based on that known data. And then once you've trained your model on the known stuff, you can then put an unknown data in there and you get a new response. And of course, in this particular one, it's an apple. So it's trying to figure out whether it's an apple or another fruit. There are many different algorithms you can use for computing this information for doing this supervised training. Just to list the some of the top ones that are currently being used, and by no means not there's more than just this. So by no means this isn't the complete list. There's polomial regression, there's a random forest, there's linear regression, there's logistic regression, there's decision trees, there's K nearest neighbors, and there's naive bays. Like I said, this is just a short list of some of the many tools that are out there nowadays. And if you have supervised learning, then we should also look at unsupervised learning. Unsupervised learning. So we have unknown data. In this case, you can see we have a bunch of fruit and we might not have labeled it. We don't know. We've never had anybody look at it and say this is what this is and we take that data and we put it through the machine learning algorithm and then that goes through the processing and then the trained model and what the trained model says hey can I see a pattern here and from that pattern it divides it up into a response in this case apples and pears you can see some of these things look just like the other and it tries to put them all together so that you get similar things in similar groups and again we have a nice list of algorithms here and this is not uh the only algorithms used for this. So don't limit yourself to this just these. These are just some of the primary ones used today. And of course we have the K means clustering, singular value decomposition, fuzzy means, partial lease squares, app priori, hierarchal clustering, principal component analysis, machine learning prerequisites, computer science fundamentals and programming. So any of the machine learning out today, you have to know some basic scripting or programming. Intermediate statistical knowledge. You have to understand a little bit about probabilities. If A is current, how likely is B going to happen? If there's clouds overhead, how likely is it going to rain? Linear algebra and intermediate calculus. The linear algebra is very important because you have to understand basically drawing a line through the data points and what that means. That's the most fundamental linear regression models. You draw a line through all your data and you use that line to compute new values. Intermediate calculus means you need to have a little bit of understanding of what a differential equation is. You really don't need to be an expert because the computer does all the heavy lifting for you. But it's important to know the terms when they come up. Unless you're doing some advanced programming on the actual models themselves. And data wrangling and cleaning. I would say this might be the biggest one in here is you have to start getting a grip on how to clean up your data. There's a saying is bad data in, bad data out. good data in, you're more likely to have good data out. Some applications of machine learning, instance segmentation, object detection, instant segmentation. You can see here where they use machine learning to go in there and find where the different cats are and the different objects are in the picture. And then in segmentation, it actually cuts them out. Kind of a fun one, especially if you have a Google Pixel phone and you can do little animation objects on top of your ongoing pictures you're taking or movies. Number plate detection. You can see here where we have a car and it comes in there and it finds a number plate on the car. Once it's done that, it can then do automatic translation. Automatic translation is we pick up some symbols in this case on a machine and it does machine translation so that you can know what it's saying even if you don't speak that language. So to summary, we covered the basics of machine learning. What is machine learning? We talked a little bit about the process or the workflow of machine learning. We've looked at two different divisions of machine learning, supervised and unsupervised. We went over the prerequisites you should have going into machine learning that you should have the basic fundamentals or a little bit of computer science and programming or scripting skills. You should know some basic linear algebra and maybe some little bit of calculus and differential equations as part of the calculus. You should have some basic intermediate statistical knowledge of what that means and what those terminology means. And you should have an idea of what data wrangling and cleaning is. How do you take your data coming in, making sure you don't have missing values, make sure that you're switching float values, so they're processed correctly, integer values versus something that's categorically like yes, true, yes, no, true, false. And then we looked at a couple applications of machine learning. Of course, there are so many applications out in today's market. It's one of the biggest growing markets out there. This is just a very brief summary of some of the things that are going on. Hello and welcome to machine learning tutorial part one. This is part one of a machine learning series put on by SimplyLearn. My name is Richard Kersner. I'm with the SimplyLearn team. That's www.simplearn.com. Get certified, get ahead. What's in it for you today? Well, we'll start off with a brief explanation of why machine learning and what is machine learning. And then we'll get into a few of the types of machine learning. machine learning algorithms, linear regression, decision trees, support vector machine, and finally, we'll do a use case where we're going to classify whether a recipe is of a cupcake or a muffin using the SVM or the support vector machine. Sounds like a delicious way to explore machine learning. So, why machine learning? Why do we even care about having these computers come up and be able to do all these new things for us? Well, because machines can now drive your car for you. still very in the infant stage but it's just exploding as we see with uh Google's Whimo and then Uberhead their program which unfortunately crashed. They know that this is huge. This is going to be the huge industry to change our whole transportation infrastructure. Machine learning is now used to detect over 50 eye diseases. Do you know how amazing that is to have a computer that double checks for the doctor for things they might miss? That's just huge in the health industry. Pretty soon they actually do already have that with in some areas where maybe not for eyes but for other diseases where they're using the camera on your phone to help pre-diagnose before you go in and see the doctor. And because the machine can now unlock your phone with your face. I mean, that's just cool having it being able to identify your face or your voice and be able to turn stuff on and off for you depending on where you're at and what you need. Talk about an ultimate automation our world we live in. And as we dig in deeper, we have a nice example of Facebook. As you can see here, they have the Facebook post with Halloween. Comment yes if you want it. Order here. Nobody likes spam posts on Facebook that annoy them into interacting with likes, shares, comments, and other actions. I remember the original ones were all if you don't click on here, you will have bad luck or some kind of fear factor. Well, this is a huge thing in a social media when people are getting spammed. And so this tactic known as engagement bait takes advantage of Facebook's newsfeed algorithm by choosing engagement in order to get the greater reach. To eliminate engagement bait, the company reviewed and categorized hundreds of thousands of posts to train a machine learning model that detects different types of engagement bait. So in this case, we have we're using Facebook, but this is of course across all the different social media. they have different tools are building and the Facebook scroll gif will be replaced kind of like a virus coming in there it notices that there's a certain setup with Facebook and it's able to replace it and they have like vote baiting react baiting share baiting they have all these different these are kind of general titles but there certainly are a lot of way of baiting you to go in there and click on something so they fed all this this data was fed into the machine and then they have the new post the new post comes up that takes over part of the Facebook setup up and that's what you're looking at. You're looking at this new post that's replaced like a virus has replaced that. So what Facebook did to eliminate this is they start scanning for keywords and phrases like this and checks the click-through rate. So it starts looking for people who are clicking through it without even looking at it or clicking through it and it's not something that normally would be clicked through. Once Facebook has scanned for these keywords and phrases, it is now able to identify the spam coming in and this makes your life easier. So you're not getting spammed. It's not like walking through an airport and in a lot of countries you have like hundreds of people trying to sell you time share. Come join us. Sign up for this. Eliminates that annoyingness. So now you can just enjoy your Facebook and your cat pictures. Or maybe it's your family pictures. Mine is family. Certainly people like their cat pictures too. Another good example is Google's Deep Mind project Alph Go. A computer program that plays a board game Go has defeated the world's number one Go player. And I hope I say his name right. Kijiji the ultimate go challenge game of three of three was on May 27th 2017 so it was just last year that this happened and what makes this so important is that you know go is just is a game so it's not like you're driving a car or something in our real world but they are using games to learn how to get the machine learning program to learn they want it to learn how to learn and that is a huge step a lot of this is still in its infant stage as far as development as we saw what happened with the as I referred to earlier the Uber cars. They lost their whole division because they jumped ahead too fast. So still an infant stage but boy is this like the beginning of just an amazing world that is automated in ways we can't even imagine what tomorrow's going to look like. 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 the 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 postcripting 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 domainspecific 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. and 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 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. So 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 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 just 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 predict an 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 label 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 equals 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 a safety gear on. Thank goodness. Speed equals 10 meters/s. And so over a certain amount of time, his distance equals 36 kilometers. 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 km. And so if you had the question of how fast somebody is 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 the 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 three times 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 a 100 seconds to travel that distance. And we have our second bicyclist who's still doing 20 meters per second. 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 / 30 since he's going three times the speed. You can easily guess that this is 33.33 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 with 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 5 + 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 dashed 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 one and if you remember the x i or the means value is three 1 minus 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 two 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' 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 equ= 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 values of what we think it's going to be when we plug those numbers in. And when we plot the predicted values along with the actual values, we can see the difference. And this is one of the things that's very important with linear regression in any of these models is to understand the error. And so we can calculate the error on all of our different values. And you can see over here we plotted um X and Y and Y predict. And we draw a little line so you can sort of see what the error looks like there between the different points. So our goal is to reduce this error. We want to minimize that error value on our linear regression model. Minimizing the distance. There are lots of ways to minimize the distance between the line and the data points like sum of squared errors, sum of absolute errors, root mean square error, etc. We keep moving this line through the data points to make sure the best fit line has the least square distance between the data points and the regression line. So to recap with a very simple linear regression model, we first figure out the formula of our line through the middle and then we slowly adjust the line to minimize the error. Keep in mind this is a very simple formula. The math gets even though the math is very much the same, it gets much more complex as we add in different dimensions. So this is only two dimensions y = mx plus c. But you can take that out to x, z, y, j, all the different features in there and they can plot a linear regression model on all of those using the different formulas to minimize the error. Let's go ahead and take a look at decision trees. A very different way to solve problems in the linear regression model. Decision tree is a treeshaped algorithm used to determine a course of action. Each branch of a tree represents a possible decision, occurrence, or reaction. We have data which tells us if it is a good day to play golf. And if we were to open this data up in a general spreadsheet, you can see we have the outlook whether it's rainy, overcast, sunny, temperature, hot, mild, cool, humidity, windy, and did I like to play golf that day? Yes or no. So, we're taking a census and certainly I wouldn't want a computer telling me when I should go play golf or not. But you could imagine if you got up in the night before you're trying to plan your day and it comes up and says tomorrow would be a good day for golf for you in the morning and not a good day in the afternoon or something like that. This becomes very beneficial and we see this in a lot of applications coming out now where it gives you suggestions and lets you know what what would uh fit the match for you for the next day or the next purchase or the next uh whatever you know next mail out in this case is tomorrow a good day for playing golf based on the weather coming in. And so we come up and let's uh determine if you should play golf when the day is sunny and windy. So we found out the forecast tomorrow is going to be sunny and windy. And suppose we draw our tree like this. We're going to have our humidity. And then we have our normal, which is uh if it's if you have a normal humidity, you're going to go play golf. And if the humidity is really high, then we look at the outlook. And if the outlook is sunny, overcast, or rainy, it's going to change what you choose to do. So if you know that it's a very high humidity and it's sunny, you're probably not going to play golf cuz you're going to be out there miserable, fighting off the mosquitoes that are out joining you to play golf with you. Maybe if it's rainy, you probably don't want to play in the rain. But if it's slightly overcast and you get just the right shadow, that's a good day to play golf and be outside out on the green. Now, in this example, you can probably make your own tree pretty easily because it's a very simple set of data going in. But the question is, how do you know what to split? Where do you split your data? What if this is much more complicated data where it's not something that you would particularly understand like studying cancer? They take about 36 measurements of the cancerous cells and then each one of those measurements represents how bulbous it is, how extended it is, how sharp the edges are, something that as a human we would have no understanding of. So how do we decide how to split that data up? And is that the right decision tree? But so that's a question that's going to come up. Is this the right decision tree? For that we should calculate entropy and information gain. Two important vocabulary words there are the entropy and the information gain. Entropy. Entropy is a measure of randomness or impurity in the data set. Entropy should be low. So we want the chaos to be as low as possible. We don't want to look at it and be confused by the images or what's going on there with mixed data. And the information gain, it is a measure of decrease in entropy after the data set is split. Also known as entropy reduction. information gain should be high. So we want our information that we get out of the split to be as high as possible. Let's take a look at entropy from the mathematical side. In this case, we're going to denote entropy as I of P of and N where P is the probability that you're going to play a game of golf and N is the probability where you're not going to play the game of golf. Now, you don't really have to memorize these formulas. There's a few of them out there depending on what you're working with. But it's important to note that this is where this formula is coming from. So when you see it, you're not lost when you're running your programming, unless you're building your own decision tree code in the back. And we simply have a log 2 of P over P + N minus N / P + N * the log squar of N of P plus N. But let's break that down and see what actually looks like when we're computing that from the computer script side. Entropy of a target class of the data set is the whole entropy. So we have entropy play golf. And when we look at this, if we go back to the data, you can simply count how many yeses and no in our complete data set for playing golf days. In our complete set, we find we have five days we did play golf and nine days we did not play golf. And so our I equals, if you add those together, 9 + 5 is 14. And so our I equals 5 over 14 and 9 over 14. That's our PNN values that we plug into that formula. And you can go 5 over 14 =.36. 9 over 14=64. And when you do the whole equation, you get the -.36 log roo<unk>^2 of.36 minus.64 log<unk> of 64. And we get a set value. We get 94. So we now have a full entropy value for the whole set of data that we're working with. And we want to make that entropy go down. And just like we calculated the entropy out for the whole set, we can also calculate entropy for playing golf and the outlook. Is it going to be overcast or rainy or sunny? And so we look at the entropy. We have P of sunny times E of three of two. And that just comes out how many sunny days yes and how many sunny days no over the total which is five. Don't forget to put the we'll divide that five out later on. uh equals p overcast = 4 comma 0 plus rainy = 2a 3 and then when you do the whole setup we have 5 over4 remember I said there was a total of five 5 over 14 * the i of 3 of 2 + 4 over 14 * the 4 0 and 54 over i of 23 and so we can now compute the entropy of just the part that has to do with the forecast and we get 693 similar Similarly, we can calculate the entropy of other predictors like temperature, humidity and wind. And so we look at the gain outlook. How much are we going to gain from this entropy play golf minus entropy play golf outlook? And we can take the original 0.94 for the whole set minus the entropy of just the u rainy day and temperature and we end up with a gain of.247. So this is our information gain. Remember we define entropy and we define information gain. The higher the information gain, the lower the entropy, the better. The information gain of the other three attributes can be calculated in the same way. So we have our gain for temperature equals 0.029. We have our gain for humidity equals.152. And our gain for a windy day equals 0048. And if you do a quick comparison, you'll see the.247 is the greatest gain of information. So that's the split we want. Now let's build the decision tree. So, we have the outlook. Is it going to be sunny, overcast, or rainy? That's our first split because that gives us the most information gain. And we can continue to go down the tree using the different information gains with the largest information. We can continue down the nodes of the tree where we choose the attribute with the largest information gain as the root node and then continue to split each subnode with the largest information gain that we can compute. And although it's a little bit of a tongue twister to say all that, you can see that it's a very easy to view visual model. We have our outlook. We split it three different directions. If the outlook is overcast, we're going to play. And then we can split those further down if we want. So if the over outlook is sunny, but then it's also windy. If it's uh windy, we're not going to play. If it's uh not windy, we'll play. So, we can easily build a nice decision tree to guess what we would like to do tomorrow and give us a nice recommendation for the day. So, we want to know if it's a good day to play golf when it's sunny and windy. Remember the original question that came out, tomorrow's weather report is sunny and windy. You can see by going down the tree, we go outlook sunny, outlook windy. We're not going to play golf tomorrow. So, our little smartwatch pops up and says, I'm sorry, tomorrow's not a good day for golf. It's going to be sunny and windy. And if you're a huge golf fan, you might go, "Uhoh, it's not a good day to play golf." We can go in and watch a golf game at home. So, we'll sit in front of the TV instead of being out playing golf in the wind. Now that we looked at our decision tree, let's look at the third one of our algorithms we're investigating. Support vector machine. Support vector machine is a widely used classification algorithm. The idea of support vector machine is simple. The algorithm creates a separation line which divides the classes in the best possible manner. For example, dog or cat, disease or no disease. Suppose we have a labeled sample data which tells height and weight of males and females. A new data point arrives and we want to know whether it's going to be a male or a female. So we start by drawing a line. We draw decision lines. But if we consider decision line one, then we will classify the individual as a male. And if we consider decision line two, then it'll be a female. So you can see this person kind of lies in the middle of the two groups. So it's a little confusing trying to figure out which line they should be under. We need to know which line divides the classes correctly. But how the goal is to choose a hyper plane and that is one of the key words they use when we talk about support vector machines. Choose a hyper plane with the greatest possible margin between the decision line and the nearest point within the training set. So you can see here we have our support vector. we have the two nearest points to it and we draw a line between those two points and the distance margin is the distance between the hyperplane and the nearest data point from either set. So we actually have a value and it should be equal distant between the two points that we're comparing it to. When we draw the hyperplanes we observe that line one has a maximum distance. So we observe that line one has a maximum distance margin. So we'll classify the new data point correctly. And our result on this one is going to be th