Deep Learning Full Course 2026 | Deep Learning Tutorial for Beginners | Deep Learning | Simplilearn

Key Takeaways

[music] Hey there. Have you ever wondered how Instagram knows which filters you love? Or how Netflix always seems to suggest the perfect movie? That's all thanks to the deep learning. Deep learning helps computers recognize patterns, make predictions, and even understand things like images, text, and speech. It's everywhere today, and industries from e-commerce to healthcare are using it to build smarter systems. And guess what? The demand for deep learning experts is skyrocketing. Don't worry if it sounds complicated. We are here to break down it for you step by step. Whether you're just starting or looking to level up, this video is designed to make deep learning easy and fun, we'll begin by explaining what deep learning is and clear up the differences between machine learning, deep learning, and artificial intelligence. Then we will dive into neural networks and walk through a hands-on tutorial using Python and TensorFlow. We will cover some math basics, explore RNNs and CNN's and show you how to use hugging face. Plus, we will get you ready for deep learning interview questions. By the end, you will be all set to dive into the world of deep learning. Let's get started. >> Got 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. 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 domain specific things like do you want to detect an anomaly? That's anomaly detection. This is actually very popular right now. For instance, you want to detect money withdrawal anomalies. You want to know when someone's making a withdrawal that might not be their own account. We've actually brought this up because this is really big right now. If you're predicting the stock whether to buy stock or not, you want to be able to know if what's going on in the stock market is an anomaly, use a different prediction model because something else is going on. You got to pull out new information in there or is this just the norm? I'm going to get my normal return on my money invested. So being able to detect anomalies is very big in data science these days. Another question that comes up which is on what we call untrained data is do you want to discover structure in unexplored data and that's called clustering. For instance, finding groups of customers with similar behavior given a large database of customer data containing their demographics and past buying records. And in this case, we might notice that anybody who's wearing certain set of shoes goes shopping at certain stores or whatever it is. are 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 a 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 quiz time and um we'll be posting the answers in the 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 if they're 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 with 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 SimplyLearn and they'll follow up with you on it. Back to our regular content. And 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 jungle 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 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 demographics 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. 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 a little candle, not a giant fire pit like it looks like here. When the baby comes out and the new state is the baby is sad and crying because they got burned on the fire. And then maybe they take another action. The baby's called the agent because it's the one taking the actions. And in this case, they didn't go into the fire. They went a different direction. And now the baby's happy and laughing and playing. Reinforcement learning is very easy to understand because that's how as humans that's one of the ways we learn. We learn whether it is, you know, you burn yourself on the stove, don't do that anymore. Don't touch the stove. In the big picture, being able to have machine learning program or an AI be able to do this is huge because now we're starting to learn how to learn. That's a big jump in the world of computer and machine learning. And we're going to go back and just kind of go back over supervised versus unsupervised learning. Understanding this is huge because this is going to come up in any project you're working on. We have in supervised learning, we have labeled data. We have direct feedback. So someone's already gone in there and said, "Yes, that's a triangle. No, that's not a triangle." And then you 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 = 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 km. We have a second bicyclist who's going twice the speed or 20 m/s. And you can guess if he's going twice the speed and time is a constant, then he's going to go twice the distance. And that's easy to compute. 36 * 2, you get 72 kilometers. And so if you had the question of how fast would somebody going three times that speed or 30 m/s is, you can easily compute the distance in our head. We can do that without needing a computer, but we want to do this from 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 3 * 10 m/s that this is roughly 102 km distance that this third bicus has traveled. One of the key definitions on here is positive relationship. So the slope of the line is positive. As distance increase, so does speed increase. Let's take a look at our second example where we put distance is a constant. So we have speed equals 10 m/s. They have a certain distance to go and it takes him 100 seconds to travel that distance. And we have our second bicyclist who's still doing 20 m/s. Since he's going twice the speed, we can guess he'll cover the distance in about half the time, 50 seconds. And of course, you could probably guess on the third one, 100 divided by 30 since he's going three times the speed. You can easily guess that this is 33.3333 seconds time. We put that into a linear regression model or a graph. If the distance is assumed to be constant, let's see the relationship between speed and time. And as time goes up, the amount of speed to go that same distance goes down. So now your m equals a minus v slope of the line. As the speed increases, time decreases. Hence, the variable has a negative relationship. Again, there's our definition. positive relationship and negative relationship dependent on the slope of the line and with a simple formula like this um and even a significant amount of data. Let's uh see what the mathematical implementation of linear regression and we'll take this data. So suppose we have this data set where we have xyx= 1 2 3 4 5 standard series and the y value is 3 22 43. When we take that and we go ahead and plot these points on a graph, you can see there's kind of a nice scattering and you could probably eyeball a line through the middle of it. But we're going to calculate that exact line for linear regression. And the first thing we do is we come up here and we have the mean of Xi. And remember mean is basically the average. So we added five plus 4 plus 3 plus 2 plus 1 and divide by five. And that simply comes out as three. And then we'll do the same for y. We'll go ahead and add up all those numbers and divide by five. And we end up with a mean value of y of i equals 2.8 where the x i references it's an average or means value. And the yi also equals a means value of y. And when we plot that, you'll see that we can put in the y= 2.8 and the x= 3 in there on our graph. We kind of gave it a little different color so you could sort it out with the dash lines on it. And it's important to note that when we do the linear regression, the linear regression model should go through that dot. Now, let's find our regression equation to find the best fit line. Remember, we go ahead and take our y= mx plus c. So, we're looking for m and c. So, to find this equation for our data, we need to find our slope of m and our coefficient of c. And we have y = mx + c where m equals the sum of x - x average * y - y average or y means and x means over the sum of x - x means squared. That's how we get the slope of the value of the line. And we can easily do that by creating some columns here. We have xy. Computers are really good about iterating through data. And so we can easily compute this and fill in a graph of data. And in our graph you can easily see that if we have our x value of 1 and if you remember the x i or the means value is 3. 1 - 3 equals a -2 and 2 - 3 = a -1 so on and so forth. And we can easily fill in the column of x - x i y - yi. And then from those we can compute x - x i^ 2 and x - x i * y - yi. And you can guess it that the next step is to go ahead and sum the different columns for the answers we need. So we get a total of 10 for our x - x i^2 and a total of 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'd be the next step is we need to know where it crosses the y ais. And if you remember, I mentioned earlier that the linear regression line has to pass through the means value, the one that we showed earlier. We can just flip back up there to that graph. And you can see right here, there's our means value, which is 3, x= 3, and y= 2.8. And since we know that value, we can simply plug that into our formula. Y =2x + c. So we plug that in, we get 2.8 8 =2 * 3 + c. And you can just solve for c. So now we know that our coefficient equals 2.2. And once we have all that, we can go ahead and plot our regression line. y =2 * x + 2.2. And then from this equation, we can compute new values. So let's predict the values of y using x= 1 2 3 4 5 and plot the points. Remember the 1 2 3 4 5 was our original x values. So now we're going to see what y thinks they are, not what they actually are. And we plug those in, we get y of designated with y of p. You can see that x= 1= 2.4, x= 2= 2.6, and so on and so on. So we have our y predicted 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 squared 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 equals MX + C. But you can take that out to X, Z, Y, J, Q, 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 be 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 [snorts] suppose we draw our tree like this. We're going to have our humidity. And then we have our normal, which is 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 [snorts] 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. So, 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 squar of p + n minus n / p + n * the log squar of n of p + 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 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 5 days we did play golf and 9 days we did not play golf. And so our I equals, if you had those together, 9 + 5 is 14. And so our I equals 5 over 14 and 9 over 14. That's our P&N values that we plug into that formula. And you can go with the 5 over 14als.36. 9 over 14= 64. And when you do the whole equation, you get the minus.36 log<unk>^2 of.36 -.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. 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 514 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 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, "Uh oh, 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 hyper plane 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 that the new data point is MEL. One of the reasons we call it a hyper plane versus a line is that a lot of times we're not looking at just weight and height. We might be looking at 36 different features or dimensions. And so when we cut it with a hyper plane, it's more of a three-dimensional cut in the data. Multi-dimensional that cuts the data a certain way. and each plane continues to cut it down until we get the best fit or match. Let's understand this with the help of an example problem statement. You always start with a problem statement when you're going to put some code together. We're going to do some coding now. Classifying muffin and cupcake recipes using support vector machines. So, the cupcake versus the muffin. Let's have a look at our data set. And we have the different recipes here. We have a muffin recipe that has so much flour. I'm not sure what measurement 55 is in, but it has 55, maybe it's ounces, but it has a certain amount of flour, certain amount of milk, sugar, butter, egg, baking powder, vanilla, and salt. And so based on these measurements, we want to guess whether we're making a muffin or a cupcake. And you can see in this one, we don't have just two features. We don't just have height and weight as we did before between the male and female. In here, we have a number of features. In fact, in this, we're looking at eight different features to guess whether it's a muffin or a cupcake. What's the difference between a muffin and a cupcake? Turns out muffins have more flour, while cupcakes have more butter and sugar. So, basically, the cupcakes a little bit more of a dessert, where the muffin's a little bit more of a fancy bread. But how do we do that in Python? How do we code that to go through recipes and figure out what the recipe is? And I really just want to say cupcakes versus muffins like some big professional wrestling thing. Before we start in our cupcakes versus muffins, we are going to be working in Python. There's many versions of Python, many different editors. That is one of the strengths and weaknesses of Python is it just has so much stuff attached to it. It's one of the more popular data science programming packages you can use. In this case, we're going to go ahead and use Anaconda and Jupyter Notebook. The Anaconda Navigator has all kinds of fun tools. Once you're into the Anaconda Navigator, you can change environments. I actually have a number of environments on here. We'll be using Python 36 environment. So, this is in Python version 36. Although, it doesn't matter too much which version you use. I usually try to stay with the 3x because they're current unless you have a project that's very specifically in version 2x. 2.7 I think is usually what most people use in the version two. And then once we're in our um Jupiter notebook editor, I can go up and create a new file and we'll just jump in here. In this case, we're doing SPM muffin versus cupcake. And then let's start with our packages for data analysis. And we almost always use a couple there's a few very standard packages we use. We use import oops import numpy that's for number python. They usually denote it as np that's very comma that's very common. And then we're going to import pandas as pd and numpy deals with number arrays. There's a lot of cool things you can do with the numpy uh setup as far as multiplying all the values in an array in a numpy array data array. Pandas I can't remember if we're using it actually in this data set. I think we do as an import it makes a nice data frame. And the difference between a data frame and a numpy array is that a data frame is more like your Excel spreadsheet. You have columns, you have indexes. So you have different ways of referencing it easily viewing it. And there's additional features you can run on a data frame. And pandas kind of sits on numpy. So they you need them both in there. And then finally, we're working with the support vector machine. So from sklearn, we're going to use the sklearn model. Import svm support vector machine. And then as a data scientist, you should always try to visualize your data. Some data obviously is too complicated or doesn't make any sense to the human. But if it's possible, it's good to take a second look at it so you can actually see what you're doing. Now, for that, we're going to use two packages. We're going to import mapplot library.pipplot as plt. Again, very common. And we're going to import seabor as sns. And we'll go ahead and set the font scale in the SNS. Right in our import line, that's what this U semicolon followed by a line of data. We're going to set the SNS. And these are great because the the seabour sits on top of map plot library just like pandas sits on numpy. So it adds a lot more features and uses and control. We're obviously not going to get into mapplot library and seabour. It' be its own tutorial. We're really just focusing on the SVM, the support vector machine from sklearn. And since we're in Jupiter notebook, uh we have to add a special line in here for our mattplot library. And that's your percentage sign or amber sign mattplot library in line. Now, if you're doing this in just a straight code project, a lot of times I use like Notepad++ and I'll run it from there. You don't have to have that line in there because it'll just pop up as its own window on your computer depending on how your computer's set up because we're running this in the Jupyter notebook as a browser setup. This tells it to display all of our graphics right below on the page. So that's what that line is for. Remember the first time I ran this, I didn't know that and I had to go look that up years ago. It's quite a headache. So mattplot library inline is just because we're running this on the web setup and we can go ahead and run this. make sure all our modules are in. They're all imported, which is great. If you don't have them import, you'll need to go ahead and pip. Use the pip or however you do it. There's a lot of other install packages out there, although pip is the most common. And you have to make sure these are all installed on your Python setup. The next step, of course, is we got to look at the data. You can't run a model for predicting data if you don't have actual data. So, to do that, let me go ahead and open this up and take a look. And we have our uh cupcakes versus muffins. and it's a CSV file or CSV meaning that it's commaepparated variable and it's going to open it up in a nice uh spreadsheet for me. And you can see up here we have the type we have muffin muffin muffin cupcake cupcake cupcake and then it's broken up into flour, milk, sugar, butter, egg, baking powder, vanilla and salt. So we can do is we can go ahead and look at this data also in our Python. Let us create a variable recipes equals we're going to use our pandas module read CSV. Remember is a commaepparated variable and the file name happened to be cupcakes versus muffins. Oops, I got double brackets there. Do it this way. There we go. cupcakes versus muffins. Because the program I loaded or the the place I saved this particular Python program is in the same folder, we can get by with just the file name. But remember, if you're storing it in a different location, you have to also put down the full path on there. And then because we're in pandas, we're going to go ahead and you can actually in line you can do this, but let me do the full print. You can just type in recipes.head head in the Jupyter notebook. But if you're running in code in a different script, you'd need to go ahead and type out the whole print recipes. And Pandanda's nose is that's going to do the first five lines of data. And if we flip back on over to the spreadsheet where we opened up our CSV file, uh you can see where it starts on line two. This one calls it zero. And then 2 3 4 5 6 is going to match. Go and close that out because we don't need that anymore. And it always starts at zero. And these are it automatically indexes it since we didn't tell it to use an index in here. So that's the index number for the left hand side. And it automatically took the top row as labels. So pandas using it to read a CSV is just really slick and fast. One of the reasons we love our pandas, not just because they're cute and cuddly teddy bears. And let's go ahead and plot our data. And I'm not going to plot all of it. I'm just going to plot the uh sugar and flour. Now, obviously, you can see where they get really complicated if we have tons of different features. And so, you'll break them up and maybe look at just two of them at a time to see how they connect. And to plot them, we're going to go ahead and use Seabor. So, that's our SNS. And the command for that is SNS.LM plot. And then the two different variables I'm going to plot is flour and sugar. Data equals recipes. The hue equals type. And this is a lot of fun because it knows that this is pandas coming in. So this is one of the powerful things about pandas mixed with seaborn and doing graphing. And then we're going to use a pallet set one. There's a lot of different sets in there. You can go look them up for seabour. or do a regular fit regular equals false. So, we're not really trying to fit anything. And it's a scatter KWS. A lot of these settings you can look up in Seabor. Half of these you could probably leave off when you run them. Somebody played with this and found out that these were the best settings for doing a Seabor plot. And let's go ahead and run that. And because it does it in line, it just puts it right on the page. And you can see right here that just based on sugar and flour alone, there's a definite split. And we use these models because you can actually look at it and say, "Hey, if I drew a line right between the middle of the blue dots and the red dots, we'd be able to do an SVM and and a hyper plane right there in the middle. Then the next step is to format or pre-process our data. And we're going to break that up into two parts. We need a type label. And remember, we're going to decide whether it's a muffin or a cupcake. Well, a computer doesn't know muffin or cupcake. It knows zero and one. So, what we're going to do is we're going to create a type label. And from this we'll create a numpy array np where and this is where we can do some logic. We take our recipes from our panda and wherever type equals muffin it's going to be zero. And then if it doesn't equal muffin which is cupcakes it's going to be one. So we create our type label. This is the answer. So when we're doing our training model remember we have to have a a training data. This is what we're going to train it with is that it's zero or one. it's a muffin or it's not. And then we're going to create our recipe features. And if you remember correctly from right up here, the first column is type. So we really don't need the type column because that's our muffin or cupcake. And in pandas, we can easily sort that out. We take our value recipes columns. That's a pandas function built into pandas. values converting them to values. So it's just the column titles going across the top and we don't want the first one. So what we do is since it always starts at zero, we want one colon till the end. And then we want to go ahead and make this a list. And this converts it to a list of strings. And then we can go ahead and just take a look and see what we're looking at for the features. Make sure it looks right. Go ahead and run that. And I forgot the S on recipes. So, we'll go ahead and add the S in there and then run that. And we can see we have flour, milk, sugar, butter, egg, baking powder, vanilla, and salt. And that matches what we have up here where we printed out everything but the type. So, we have our features and we have our label. Now, the recipe features is just the titles of the columns. We actually need the ingredients. And at this point, we have a couple options. One, we could run it over all the ingredients. And when you're doing this, usually you do, but for our example, we want to limit it so you can easily see what's going on because if we did all the ingredients, we have, you know, that's what, um, seven, eight different hyperplanes that would be built into it. We only want to look at one so you can see what the SVM is doing. And so we'll take our recipes and we'll do just flour and sugar. Again, you can replace that with your recipe features and do all of them, but we're going to do just flour and sugar. And we're going to convert that to values. We don't need to make a list out of it because it's not string values. These are actual values on there. And we can go ahead and just print ingredients. And you can see what that looks like. Uh, and so we have just the nanoflower and sugar, just the two sets of plots. And just for fun, let's go ahead and take this over here and take our recipe features. And so if we decided to use all the recipe features, you'll see that it makes a nice column of different data. So it just strips out all the labels and everything. We just have just the values. But because we want to be able to view this easily in a plot later on, we'll go ahead and take that and just do flour and sugar. And we'll run that. And you'll see it's just the two columns. So the next step is to go ahead and fit our model. We'll go ahead and just call it model. And it's a SVM. We're using a package called SVC. In this case, we're going to go ahead and set the kernel equals linear. So, it's using a specific setup on there. And if we go to the reference on their website for the SVM, you'll see that there's about there's eight of them here. Three of them are for regression. Three are for classification. The SVC, support vector classification, is probably one of the most commonly used. And then there's also one for detecting outliers and another one that has to do with something a little bit more specific on the model. But SBC and SVR are the two most commonly used standing for support vector classifier and support vector regression. Remember regression is an actual value, a float value or whatever you're trying to work on. And SBC is a classifier. So it's a yes, no, true, false. But for this we want to know 01 muffin cupcake. We go ahead and create our model. And once we have our model created, we're going to do model.fit. And this is very common, especially in the sklearn. All their models are followed with the fit command. And what we put into the fit, what we're training with it is we're putting in the ingredients, which in this case we limited to just flour and sugar, and the type label. Is it a muffin or cupcake? Now, in more complicated data science series, you'd want to split into, we won't get into that today, where you split it into training data and test data. And they even do something where they split it into thirds, where a third is used for where you switch between which one's training and test. There's all kinds of things go into that. It gets very complicated when you get to the higher end. Not overly complicated, just an extra step, which we're not going to do today because this is a very simple set of data. And let's go ahead and run this. And now we have our model fit. And uh I got an error here. So let me fix that real quick. It's capital SPC. It turns out I did it lowercase. Support vector classifier. There we go. Let's go ahead and run that. And you'll see it comes up with all this information that it prints out automatically. These are the defaults of the model. You notice that we changed the kernel to linear. And there's our kernel linear on the printout. And there's other different settings you can mess with. We're going to just leave that alone for right now. For this, we don't really need to mess with any of those. So, next we're going to dig a little bit into our newly trained model. And we're going to do this so we can show you on a graph. And let's go ahead and get the separating. and we're going to say uh we're going to use a W for our variable on here and we're going to do model coefficient_0. So what the heck is that? Again, we're digging into the model. So we've already got a prediction and a train. This is a math behind it that we're looking at right now. And so the w is going to represent two different coefficients. And if you remember, we had y = mx + c. So these coefficients are connected to that but in two-dimensional it's a