Machine Learning With Python Full Course 2026 | Python Machine Learning For Beginners | Simplilearn

Hey everyone, welcome to this machine learning using Python course by simply learn. In this course, we will start with the basics of machine learning, how it fits under artificial intelligence and how it is different from deep learning. We'll understand how machines learn from data, find patterns, and make predictions. You'll then explore the major types of machine learning including supervised and unsupervised learning. After that, we'll move into important concepts like regression and classifications when models predict values such as sales or house prices and categories such as spam or not spam, fraud or not fraud, and yes or no. You'll also learn key algorithms like linear regression, logistic regression, n basis, k nearest neighbor, decision trees, random forest support, vector machines, xg boost, and also cat boost. Along the way, you'll also cover data prep-processing, feature engineering, model evaluation, cross validation, regularization, hyperparameter tuning, and ensemble learning. So, you can understand how to build better and more reliable machine learning models. By the end of this course, you will know how machine learning models are built, trained, tested, improved, and applied using Python. So, let's get started and learn how to turn raw data into smart predictions with machine learning using Python. If you're interested in mastering the future of technology, the professional certificate course in generative AI and machine learning is the perfect opportunity for you. This 11-month live and interactive program provides hands-on expertise in cuttingedge areas like generative AI, machine learning and tools such as chat, Dalai E2 and hugging face. You'll gain practical expertise through 15 plus project integrated labs and live master classes delivered by esteemed IIT Kpur facedated labs and live master classes alongside earning a prestigious certificate from IT Kpur. You'll receive an official Microsoft badge for Azure AI courses and career support to simply learn job assistant program. Hurry up and enroll now. Find the course link in the description box below and in the pin comments. Before we started, here's quick quiz for you to answer. Which type of machine learning is used when the model learns from data? The answer was learning, so learning, reinforcement learning, deep learning. Your answers below. Let's get started. What you think is a subset of artificial intelligence, right? That's uh basically um machines learning from data in order to uh make decisions essentially. Um so this was a big departure from the rules system were explicitly programmed to make decisions. So just think of an example like a really big kind of if this then that then that then that and and else if this this this right so bunch of rules that had to be pre-programmed in order to um come out with some final answer. Uh with machine learning it's the exact opposite of that. we're actually training something from examples from existing data um in order to predict something or um make some type of decision. Uh and so we're going to learn about the various ways we can do machine learning. But if you guys remember we um talked about some of this like the differences and the uh basically rules-based approaches to learning from data approach. Um and in included in that is going to be uh complex unstructured data. So things like images, text, audio. What handles those really well is uh deep learning which we will get to in the course after this. But uh those are certainly in there as learning from data even complex data. So we had this picture uh and I think this is kind of around where we left off last time was uh just distinguishing between those three terms. We see artificial intelligence, deep learning and machine learning kind of used interchangeably, but this is really how they fit in. Artificial intelligence is kind of a broad anything mimicking human intelligence. Um which doesn't have to be learning from data but uh machine learning is part of that. And then um one way to accomplish machine learning is to use neural nets which is the focus of uh deep learning. Um and so deep learning has been has found a lot of success especially recently with uh those complex data types like images, speech, text, right? So deep learning used all over the place. Even in um modern like generative AI, we see deep learning used quite a bit. Um it really anything that's using neural nets is uh going to be deep learning. Um and again we'll focus on that later but we're going to be mainly focused on machine learning for this course. Primarily machine learning that does not use neural networks. Okay. So just models that are not necessarily neural networks be our focus. So in machine learning we had an example of a game uh essentially um learning what decisions to make uh based on the uh kind of current um state of the board. This could be a um you know machine learning example that uh learns from many previous examples. So a lot of data around these games are used to train these um kind of robots that can play these games and play them at a very high level. Um so there's been a lot of successes actually in machine learning and deep learning um around uh playing games like chess or go um using machine learning algorithms. So pretty cool. All right. All right, so I think this is where we ended. Last time we said there's a bunch of different use cases for machine learning. So um recommendation system is going to be a big one and we will actually study that uh in one of our final lessons of this course. Um chat bots like generative AI doing sentiment analysis. Chat bots we'll study later but those are certainly an application of learning from data in order to uh generate responses to text prompts, right? Um spam filtering that's a good example like classifying an email as spam or not spam. Um that that gets trained from examples and uh learning from data such as previous emails. Um social media posts analysis is another kind of text data um use case but you uh can do a lot with that text like you can predict the sentiment um you can predict uh the category of what what the post is talking about um those kind of things all can be done with machine learning and many other use cases not on this list that we will uh cover you know as we as as we go further. Okay, so this is where we kind of left off. Um, so what's doing all the hard work here is uh machine learning algorithms. So these are things that will um these are things that will learn from the data. So they are uh they they are basically um algorithms or sets of rules that uh or mathematical rules I should say not formal rules like in the in the sense of a rule system but mathematical um formulas and mathematical uh rules essentially that help us learn from the data. So they correlate the data to some type of outcome. So some type of prediction uh whether that's going to be as we will see whether that could be like a number like we're predicting a price or demand or sales um or it could be a category like is this transaction fraud or not fraud or what's the probability that this is fraud um so we have different kinds of predictions we can make with machine learning um but uh we will study the kind of the differences of those coming up. Um, but machine learning algorithms are really what power they're kind of the models, right? They're the models that help power uh machine learning to actually learn from data. So, we're going to spend a lot of time in this course studying those algorithms like the different models that we can build and what their differences are, what their strengths are, what their weaknesses are. We'll we'll learn a lot about those. Okay. So I guess you can imagine like everything is so data dependent, right? Um we're learning from data. So uh it makes sense that the quality of data really really matters here in determining how strong the model can be. Um so you see this graph here charting kind of the um high quality data um versus just uh any old data but a decent enough quantity of it. Um you can see that performance and the performance is measured by some evaluation metric. Um, so think of it as uh something like an accuracy. Like if we were predicting fraud or not fraud, how accurate can our model get at actually detecting fraud, um, it gets better and better and better the graph shows that the higher quality of data that we have. So there's kind of that there's a there's a saying in machine learning um, called garbage in garbage out. What that means is if you have poor data, even the best model in the world, poor data is not going to result in having a good model that can be accurate and perform well. Um, so it needs to be high quality, meaning um there needs to be a decent amount of it and it needs to be labeled appropriately as we will will talk about. Um, and it needs to not have any, you know, significant outliers. needs to be clean, not have those missing values, all of those things. Um, you can you have a good chance at deriving good predictions from higher quality data as this kind of shows. Okay. So, one thing we're going to learn um as we go along is quantity matters as well. So, not only quality, but a decent amount of it. And um we're going to learn those kind of rules of thumb like how much data do I need for certain algorithms. Um one thing that we will see is that uh the the basic machine learning models that we'll study don't need as much as a neural network would. It you know neural networks are going to require a lot more um than a basic machine learning model learning model. So uh that's something we will see as we go along. But uh this is something we'll talk about and discuss with each model that we study is kind of how much data do we actually need to produce a high quality model. Okay, any questions uh so far? Okay, let's talk about the different types of machine learning that we're going to discuss. Pime, there's going to be two primary ones that we will study in this course and then a couple others that'll be a little bit more advanced that we won't get to but worth knowing about. Um, so there's going to be four total that we'll study or talk about and they'll be on this list here, which is um supervised learning and unsupervised learning. Now, I'd say the majority of our focus will probably be on supervised learning, and we'll talk about what that means, but we'll also cover unsupervised learning as well. And so, we'll look at the most popular techniques in each of these types of machine learning. Um, and then we'll talk about these two, but not really study them because they're more advanced topics. Um, that that will be beyond the scope of what we'll do. But uh these are going to be um different styles of machine learning that are going to be characterized by um what kinds of predictions they make, what kind of data they need and require. Um and uh what kind of outcomes they're actually producing. Um, so let's let's get into each of these, but uh the the one that we'll probably spend the majority of our time on is going to be supervised learning, but we will study unsupervised learning as well. We'll study both. And we're going to talk about we're going to define both of those um coming up. And again, these will be a little bit more advanced topics that we won't spend too much time on. Um, but but we'll discuss their relevancy in machine learning um and give a good definition to it. All right. Let's start with supervised learning. Now this is going to be uh a term that really refers to using examples. So using labeled examples. So here we say labeled data to help our model train. In other words, help our model be able to predict guided by specific input output pairs. So supervised really refers to the fact that we have answers. We have examples, we have answers with those and we use that collection of data to build our model off of so that we can predict um those kinds of things like a price, like a category, like a spam not spam in this in this slide. like we would be predicting if this shape is a square, a triangle or a circle. Um but but when we build a model for that, we have data that has an answer attached to it. Right? We've talked about this before a little bit with labels. So there's a guide there that can guide us towards building our model. there's an actual every every example has an answer and that answer is really critical to help build our model off of. So, um that's it's almost like you have um a you have a bunch of exercises in let's say like a math textbook. You have a bunch of exercises and you have the answers and that way you can kind of check your work. you think about model training um that is the really a lot of that process of model training as we are going to discover is um basically checking our work against these answers in our data in our training data. Okay. So supervised learning is any type of machine learning that involves learning from labeled data in order to predict outcomes. Okay, predict outcomes like now the the outcomes can be numerical. They can be like a price, temperature, demand, sales, revenue. They can be numerical but they can also be categorical. So they can be like spam not spam, fraud, not fraud, cancer, not cancer. Um dog, cat, giraffe, those kind of categories. Um we could predict those. It's some type of outcome. Okay, some type of outcome. The key is we're using labeled examples to guide our model building. That's why it's called supervised learning. So we know in our data we know what the inputs are. Of course, those are going to be think of the inputs as like all of our columns and then we have a special label column that represents the output we're trying to predict. So if you think about that housing price data, the label could be the price. And that's something we would build a model to predict, but we have answers for all of our examples in our rows. We have answers to help guide our model building. They help tweak our model because we know the answer ahead of time. So they're they're really good examples to build our model off of. Okay. So that's that's supervised learning. Uh in this example, is circle not in the prediction because it's not part of the test data even though it's in the labeled data? Um no, it just not necessarily. It just means that like we learn against all of these examples that have these answers and then when we observe new examples um we can try to predict what those would be based on what we've seen before. So I if there was a you know it's just a coincidence we only have two two examples in our test data like we could have a circle here in which case we would predict circle that's fine or at least we would hope our model would predict circle right that's what we're hoping may or may not get it right um but it's it's only not there because we only like we're just assuming that we only have two examples we're testing against but in reality we would probably do a lot more than two. It's just it's just a coincidence really. In reality, we would test against a lot more data. And we're actually going to see why we would do that. Like why would we train our model and then kind of use additional data um to to evaluate it? It's actually really important that we do that step to get a sense of how good our model is before we take it out in the real world. So if we apply our model that we build on our label data to um this kind of set of test data that we haven't been exposed to before. It helps give us a sense of how good is our model. So it's test is usually used for evaluation. So that's something that's something we'll study. How do we train? Uh it depends on the model. Um so training will be a sense uh will be an algorithm that will um basically update the model according to the data these labeled examples. Um every model is going to be different in exactly how it trains. So we're going to we're going to talk about that when we get to the individual models that we'll study. But uh loosely speaking, they're going to use the data to adjust itself. Like imagine adjust like tuning a bunch of knobs. Um, like the best example I can give you is we I think I did this one last week where you have kind of a function that predicts the price and let's say it has um weights like weight one with feature one, weight two with feature two, weight three with feature three. So imagine we had three input features and we we built an answer according to that. Essentially what we would do to train the model is adjust these um in order to get this correct based on our our labeled examples. Okay. So that's something we're going to learn about coming up shortly when we when we actually dive into model. Every model is going to be slightly different in how it trains, but at a high level it's going to use the training data with those examples, right? the labeled examples to help guide the formula essentially to adjust to generate the proper kind of model here. The these things are going to be adjusted according to the data in order to produce the correct output. So think about these as knobs that will turn. uh which type of machine learning is used? Uh probably supervised um which is what we're talking about now. So probably supervised because most people want to um build some type of model to predict something. Uh so yeah, I'd say I'd say supervise. Yes, we're we are definitely going to learn how to train. Yeah, we'll see. We'll do the code. Um, I'll tell you about how it's done. Yeah, we're definitely going to learn it. But what I was saying is it's kind of on a model bymodel basis. So, I want to wait till we get into the individual models, then we'll talk about how they're trained. But yeah, we'll we'll learn how to do that. But yeah, supervisor used all over the place. Even even for uh generative models, they use supervised learning because um like an LLM is going to use labeled examples in order to train, right? In order to train how to generate responses according to prompts. Um it needs to learn against a lot of text examples. So that supervised learning is what um results in that model, right? Learning from those labeled examples. It is yeah image image uh a lot of um yeah a lot of image processing is supervised like object detection. So the YOLO model is an object detection model. Yes. Um because it has to be trained right it has to be trained on uh it has to be trained on images with labels such as this is what object is in this image. This is the box around the object. Um, yes. So if if it's if it ever uses label data to train and build the model, it is supervised. So YOLO is definitely supervised and we actually we will we will cover the YOLO model later on in our deep learning course. We talk about object detection. So we'll still we'll study that. But yeah, it's supervised. Okay. So on this slide we have some common supervised learning algorithms that are we will study. So all of these we will study and understand what they do and how they work. But just giving you some to name them. Linear regression is kind of the one I just drew out which is the um this is the prototypical like easiest to understand model that is kind of the um exactly like this where we have a weight times a feature um a weight times a feature and then a weight times a feature and on and on and on. You can have as many as you want. um that is a linear regression. And so that is um that's a supervised model because we need this value here and we need all of our inputs in order to um actually train this model and generate all those weights um that that is uh that uses um labeled examples to help tune all those knobs. Um same with all these other models. So, we're going to talk about decision trees. We're going to talk about logistic regression and and SVMs, which are support vector machines. We'll talk about all of those, but they're all examples of supervised uh supervised Okay, we'll talk about all of these. They're all supervised because they all require labeled examples in order to train them and and then subsequently use them. Okay. Okay. So what are some use case examples? So for for instance in uh supervised learning we may be predicting temperature based on yearly temperature trends. So we would have that yearly data as our um as our labeled examples and those would supervise the learning of a model that predicts temperature. Um, same thing with predicting crop yield based on um, seasonal crop quality changes. So maybe we have a bunch of features relating to crop quality. We could predict crop yield. Um, we would just need historical examples with those labels, right? What the crop yield is for each time period. Let's say we would just need those uh, supervised examples and we could easily build a model off of it. Um uh this this last one sorting waste based on known waste items and their corresponding waste types. Um that's kind of like spam. It's like filtering basically like a spam filtering. Um so think of it like the the shapes example. We sorting things into squares, circles, triangles. Um, same kind of idea here where we have a bunch of examples on what those um what those waste items should uh should belong to, like what wastist bins they would go to, for example. Um, and those could be labeled and therefore then we could um understand what category of waste they belong to. Um, same thing with spam. something is fraud or not fraud, spam or not spam, cancer or not cancer. All of those are going to be supervised learning examples because they're going to require in order to train them, they're going to require data that has those labels. Okay? So, anything that has labels is going to be supervised learning. So again, this is where we will spend probably the the majority of our time is doing supervised learning problems, ones that we have labeled data. We're building a model and we're going to predict those those uh labels essentially. Okay, before we go to unsupervised, any questions about uh supervised All right. So supervised requires labels in order to have an example to go off of to build your model. And that's because you're predicting those kind of outcomes like spam or not spam, cancer or not cancer. Now unsupervised learning is completely different. It's the opposite. So unsupervised learning is where we do not use labels whatsoever. So we're not using any labels at all. So it's it c it can be completely unlabeled or even if it's labeled we're not using labels in any way but um we primarily would say it's unlabeled data. We have no guidance because we're not using the labels in any way. We have no guidance to um predict anything. But that's because we're not really predicting anything in unsupervised learning. Generally what we're doing is looking for some structure or pattern. Okay. With unsupervised learning, we're looking for some structure or pattern. So, um, one type of example that's very very popular is going to be this second one, which is, um, identification identification of user groups based on similarities or commonalities. Now, this is going to be a problem basically known as clustering, and it's a problem we will study quite a bit. there's going to turn out to be lots of different algorithms that can accomplish clustering. So what clustering attempts to do is basically say um we have data that's like this and then data over here and then data over here. Let's just group these together. So like this should be one group, this should be one group and this should be one group. And we can find those structures and say okay this is group one, this is group two and this is group three. one, two, three. And we can basically build what we would call clusters of data um based on how close together the points are kind of located in these kind of cluster zones like these boxes I've drawn. Okay. Now, that doesn't require any label to do, which is really fascinating. So, unsupervised, you don't need any label at all to accomplish the algorithm. Um, so clustering is one good example. Um finding outliers or anomalies is another. So we don't necessarily have any label of what is an outlier or what is an anomaly. We are deriving that from the features alone. There's no guidance. There's no label um to doing like outlier detection or anomaly detection. Okay. So that's another good example. One that's not listed on here um but is also really important that we will study is something known as dimensionality reduction. So dim reduction and what that what this focuses on is basically compressing the data set a bit. So we take our data and basically compress it um so that but we do it in such a way that we retain as much information as we can. This is a very like smart compression. And what it does is it lowers the dimension. Um dimension, think of the dimension as like number of columns. Number of columns. So imagine we had 100 columns in a data frame. What we could do is actually reduce that down to 10. So like 10% of that. So we reduce it down to 10. And um but those 10 are it's not like we chopped out um 90 other columns. We um smartly kind of compressed all that information into these 10 new columns um that are compressed versions of the hundred that we used to have. Um so dimensionality reduction is is another unsupervised technique. It requires no guidance, no label to do, but is um a really useful technique to reduce the size of your data if you're doing things with it. Um so this is another one that we will we'll study how to do it and basically more details behind it, what the algorithms are. Um we'll so probably those two in unsupervised will spend the most amount of time on clustering and dimensionality uh and supervised if some data is present but we didn't label it means example we had circle triangle square in the training data we add pentagon but we didn't label that in that case uh yeah so every um in supervised learning, every row, think about it as like every row in our data frame needs to have a label uh associated to it. It needs to have a a column that represents the label. So if we've never seen Pentagon before, I can't use that as a label. So, it has to the Pentagon has to exist in the data if I'm going to be able to predict it, right? So, it can't predict, right? If we've never seen it before, we have no examples to go off. We have no guidance. So, how could we predict that? Right? We can't predict it. if it's if it's in there. So if if we have labels of Pentagon, let's say, then yeah, we could put Pentagon. We could remove. Remove what? The Pentagon. We wouldn't remove that. No, let me go back to that page. Wouldn't remove it. Um, it's just if it's not in our labels, we're not going to be able to predict it. So, Pentagon's a good example here. Uh, pentagon is not one of our labels. So, it currently is not in our data set as one of the labels. We only triangle, circle, or square. We don't. So, I would never be able to predict. I'll never do that if I haven't seen examples of it before. Okay. But let's say we had that in there. So we have so if we have we can have example of it in our labels then predict it. Yeah. Yeah. Don't worry. Don't worry about this data. Data is just saying here's a new here's a okay that's a square triangle. And we could have as many of those examples as we want in our test. What's this? Right. The test data can be whatever it whatever it wants. But yeah, if if we label data are basically the talking about this just mean what are categories that are data. So in this so the labels are relative to our data saying what labels excuse me what labels uh do we have not part of those lab no so is not going to be that's the difference with unsupervised They're not going to make a prediction like this. Um so unsupervised is not going to make a prediction like um basically say they're similar similar cluster. It's not going to make a prediction. That's what supervised learning does. Clustering. Yes. Yes. Labels require labels. So the other thing unsupervised might do is it might say labels it might say that this is an outlier um that's something that uh unsupervised could do. Um it it yeah cluster in the sense that um it would basically assign a number to it. Cluster one. This is cluster two. This is three assign a number prediction label in traditional sense of a label. It does provide like a numerical index for the cluster because what we want to know is like okay this guy has the cluster of one. This guy belongs to cluster one. This guy belongs to cluster one. This guy belongs to cluster two. This guy belongs to cluster two. Some index of what cluster he belongs to. So time but in traditional fiction sense no you're doing things like identifying clusters doing dimension these are all like structure pattern oriented things they're not predictions of a label okay they're not which is what you see in supervised learning Okay. So an example would be that we take the data we can group together uh images into um which would be clusters there's no these would be groups of like we don't have we don't say that this image belong to this this image belong we derive that um so like a good example is um customer groups so we would identify customers okay do they have similar spending how many days they shopping a week how many groups based on similar qualities clustering will find those groups that should exist um it will cover those groups bas on the idea. But there's no labels that say like this person should be in this group, person should be in this time. There's no labels of that. It's arrived during them. It's unsupervised, right? There's no unsupervised guidance. There's no guidance to it. You just layers. All right. So we had which uses the labels have unsurprised uses no labels for structure and then we have some that's kind of in between which is um what's known as semi and this is where you use a combination of a little bit of labelled data but most of your data is actually unlabeled data um and you try to get some use out of that label data in order to um build a lot of it. And so uh it uses the um it uses the label to um generally provide some guidance on usually what happens is you use your label data to kind of predict what it should be unl data and then you can go from there. So you can create artificial labels on this data and then you can use all people learning approach. So basically refers to the fact that you started out with most of your data not being labels and what you do is basically shuffle those labels into some labels and then now supervised uh and it's kind of rare most just going to um prefer to start with all usually approach most rare but um it could like if they um and provide artificial label uh and then they use that whole data set to model off of I'll go to question they uh that's those aren't labels the features I still use the core features of the data they just don't have any labels in the sense label like you should think of a label as something we're trying to predict whether it's a phrase whe it's like we kind of predicting and so uh data for every answer that we're trying to that's label so unsupervised we don't have any labels gender, age, income, square footage, listing semiup because there's a decent amount of unlimitable and predictable and use all that together in kind of a supervised fashion for a model down the road. Try to take um you know maybe we try to some based on we have some some label data here. We have most of our data is unable and we try to supply some labels to it like we know youth and um adults um and we try our and we try to stab you because then everything is low point and then we can just go ahead and do supervised learning from there supervised what we do in this course um is we just start with labels we won't try to drive artificial labels usually just start so one example in the world is Google photos which um when you picture invite uh labels based on previous uh images in your library. in tags or labels on those. Uh generally when you take that picture, it's kind of unable to unless you go in and specifically write some tags and some labels. But um if you can still uh make one of those based on the other you have um okay last one supervis it's more interact but ask what are we learning? we are learning what actions to take in the internet um and by reinforcing positive lead to a reward. Um so that's where the word reinforcement comes from is we we basically uh imagine a child that's you know walking eventually do it and they get a reward some of the positive movements that lead them to call um feedback um so they learn from those um so this is a comment um essentially they deal a lot with um again actions usually action something changes um then you kind of observe the feedback so like a breaking where you're trying to figure out what move you should make or um trying to navigate a maze to like reputation for left right actions it can take also like a self it's slow things yeah so real example game award if you win game or if you like capture a key piece and checkers or chest pen like you lose a game or lose one of your pieces like um task you get rewards for um moving in the right direction um towards the exit and having a room um you would penalize it for coming in the wall um you would get a reward for moving usually Oh, like usually function. So the word it could be like um like depend like or even let's say let's say this interest then if they make it here like 100 number and they uh if they bump this kind could be the reward if the right direction say smaller intermediate rewards like this should be a plus forward this is a plus 10 plus 15 if you're wrongus 10 and you're trying to collect the most the largest you try this out many many times you basically simulate length of this maze many times and Like it's like where I should go is based on what I've learned in the past. Okay, what move should I make space? I'm here. I'm here. Should I go? You can know that from experience. So on the reward that I've seen in the past, right? Yeah, it's a great question. Um how does different so every so in the word every space is a state. So the state piece of states are possible. Um so way there's a way to quantify essentially what's the value of my piece moving up around giving the rest of the state to sacrifice. Um but we would learn from experience that okay the best sacrifice we have to learn many times is to say okay Christ in the world all these pieces are this way the best right now the long run in the long run like we know the best action for me so you learn how to take actions and like move right there's a calculation there that you learn state and you're trying to say but actually can actually probably my favorite and if enough people sign up for um our main topics I really fascinating by the car of the human is known as the agent it's interacting with the taking that's why action do is learn that's action Um and that's actually what leads to longer. So you have to uh you have to learn what what leads to experiencing this encounter. So there's a lot of kind of simulation or letting the robot try something a lot. So I said to me and this is the built-in reward. So you click watch it. um kind of reinforces the uh you can make similar things like time or watch a lot of recommendations being worn them just naturally in this case how long it's more so that is however Google might do not um pandas because all this is going to be applied during this course of course useful here in knowledge mainly most prepared to do visualizing like things um t is sometimes useful for certain supervising we use a max square learn but has um different model into it that we use to help do so incredible being that training and doing all come from it also So anyway from this okay learning network best which makes the best guesses. And of course just usition is the best choice there. Okay. Okay, we ask which example of illustrations you want to enhance customer experience in an e-commerce company. So to do fraud transactions so they'll get prediction probably behavior characteristics um that might be by anyway it's machine. Okay, deep learning for machine learning and artificial intelligence. So what's unique about deep learning? All right, let's go to three. So now we open that. So open the 3.1 book. Um there's two of them. We'll see how far the second one today. Probably will for this one. You guys have it. Okay. All right. So we're going to start by talking about uh supervising. Um we are supervised and unized after you supervised because there are two different types of learning values. I'm talking about the over here which is I'm talking about level study which are these these categories supervised learning. Um those two categories are going to be called classification regression talk about those differences and then some example outcomes and that's just within this notebook um 3.2 get into uh regression um would be very interesting models learning for label data. So we have in our train model the goal is to learn between study model and allow us to do so. Um so remember columns and the other will be the lab and learn price. So that inside there are two types of learning on the lab and depending numerical so that is known as trying to classify examples as belonging to one or another. So we have two main reason but um generally we don't care that is continuous. pattern and we're trying to predict the temperature tomorrow because okay and category right classification um because we are categories just now we're not saying but the only choice is hot right so then it before we talk about exactly as close this oneation. Right. So that means we haveations. Um so there's classification reject um finance all the time with things like loan approvals. So it says here's a lot of classifications. Um we category but under the hood actually right And factory category for control. Um is where you are and of all right so to the actual they are um and they're actually at a level they're all learning the label they have lab and what is all trying to learn they're all trying to uh learn we'll cover these guys ination. So some of the useful question and work through probably uh They're probably all these good. So um um using that next for listen to discussion around first and then two regression. We're going to start with linear regression and see um how what that model is doing. Um but you kind of see the idea. So it should be somewhat familiar. Um and then walk that idea to what's called big because we want to know what performance today here and what is is Paul's message and they are right here and believe SL is this line and the argument is that this distance from actually the is that it distance. So we want this distance to be um sorry this beible distance between as minimum distance as small as possible. everything. So, so this conversion is generally known as linear regression. So you train here that's how we find all these find of we just fit. Okay. Um and we do a future. So that's the exact blue. So this line will be yals 0 plus b1. So some weight. So in the case value and the future is learning model. So $10 and how many sales do you have of that point then say it has one 50 and green that line is It's learned from expand this case we have more than a feature right so more broadly instead we have multiple multiples features um so this is clearly talking about um so this way we just line um and this is the um model yeah if all orient that's the community It's still the same ideation. Yeah. So remember that um I find it also the sales. So um to see how it's fit first of all the first is keep all those first column which is the second first step is to always feature our characters from our triangles and those standard X and Y is that we are sleeper sales. So the view is an feature. Why is this working? And then this over and over again use fastation. So we go over here and you test for extended use% to 30% and white creating tablet use in area. Turn it. So um yes areable. And you see how do XY model split and um end up on the train to Jesus says let's do anything for someone is looking train model. So using package model on two and three. So this was only So, and our test here um and our predictions are not fit. Train it. never the Eagles. etc. This question is really according to the function. So I do a little bit on this minimize loss for the air. Um, so That means it's not well Everything is too simple. really needed. Angel is clear. And the last is completely General of the use that measure of this. So the average lesson. So now this term Just let your world do And again, all Yes. So that's why you don't need to on the safety table. You have all flavor is underneath you. I know how many under. So following your Yes. Yeah. 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