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 GPT, Dalai E2 and hugging face. You'll gain practical expertise through 15 plus project integrated labs and live master classes delivered by esteemed IT Kuracated labs and live master classes. Alongside earning a prestigious certificate from IIT Kpur, you'll receive an official Microsoft bash 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 for you to answer. Which type of machine learning is used when the model learns from day? The answer was learning, so learning, reinforcement learning, deep learning. Your answers below. Let's get started. What you think is which 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. We last time we said there's a bunch of different use cases for machine learning. So, um recommendation systems 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. it 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. 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. Prim, 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 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. 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 by model 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, supervis is 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 the 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. 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 waste 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 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 unlabelled 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 in 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 have 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, let's say we had that in there. So we have So if we have we could have it in our labels then predict it. Yeah. Yeah. Don't get worried. Don't worry about this data. The test data is just saying here's a new here's a shape it okay that's a square triangle and we could have as many of those examples as we want in our test right the test data can be whatever it whatever it wants but yeah if if we label data are basically talking about this just mean what are categories that are data so in So the labels are relative to our data. What labels, excuse me, what labels uh do we have? We only have part of those. No. So supervised is not going to 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 these guys are similar similar similar cluster. It's not going to make a prediction. That's what supervised learning does. Clustering. Yes. Yes. 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. This cluster one this is cluster two this three a number meaning a 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 but in traditional fiction sense 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 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 there's no groups ahead of time like we don't have we don't say this image belong to this this image belong we 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 group together based on similar qualities clustering will find those exist but there's no labels that say like this person should be in this group, person should be in this. 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 lay. Right. So we had unsurprised which uses the labels unsurprises no label to 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 label data but most of your data is actually unlabelled data um and you try to get some use out of that label data in order to um build out of it and so uh it uses the um it uses label to um generally provide some guidance on you have learning is you use your label data to kind of predict what should be unable and then you can go from there. So you can create artificial labels on this data and then you can use all learning approach. So this basically refers to the fact that you start out with most of your data labels. You do have some examples and what you do is basically shuffle those labels into data set and then some labels and then now everything. Okay. So it falls between supervised uh and it's kind of rare most actually just going to um prefer to start with usually most rare um it could like if provide artificial lab offer uh that's those are the features I still use the core features of the data. they just don't have any like labels in the sense label like you should think of a label as something we're trying to predict whether it's a like we've been kind of predicting and so uh we have the result out answer that we're trying to that's label so unsupervised we don't have any labels we have gender age in square footage okay supervised because there unlimited of it unl supervised fashion for a model down the road try to take um you know maybe we try some labels based on we have some some labelled data here we have most of our data is unlike youth and um adults um and we try our and we try to artificial unl you can use it because then everything is point and then we can just go ahead and do supervised learning from there supervis um if we just start with labels we won't try to drive artificial labels usually just start with labels so one example in the world is Google photos which um when you picture and uh labels based on previous uh images in your library so tags or labels on those uh genally we take that picture it's kind of unable unless you go in and specifically write some tags and some labels but um if you do that you can still uh make you create one of those based on the other you have um okay last one in terms we have high learning um it's interact but that's what are we learning we are learning what actions to take in the internet um and we do that by reinforcing positives that 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 calling eventually we're going to do it and they get a reward for some of the positive movements that lead them to power call um they might tough feedback um so they learn from those um so this comment um essentially it's a lot with um again taking actions usually taking action something changes um then you kind of observe some feedback so think about where you're trying to figure out what move you should make or what um trying to navigate a maze like for left action it can take also like self It's not slow on things for me or something. Uh yeah. So real example game reward like if you wins in game or if you like capture keys and checkers or chest like you lose a game or lose one of your keys like um task rewards for um moving in the right direction um towards the exit and having a room. Um you penalize in the wall um you would get a reward for moving usually oh like usually function state. So the word it could be like um like depending could be like or even let's say amazing in the right direction we get smaller intermediate rewards like this should be a plus move forward this is a plus five plus 10 if you're in the wrong direction fiveus 10 and collect those reward you try this out many many times you basically simulate len many time like where I should go is based on what I've learned in the past. Okay, what should I make from space? If I'm here, if I'm here, should I go? You can know that experience on the reward that I've seen in the past when I've moved out, right? Yeah, it's a great question. Um, how does so every so in the chess every space is a state. So, it's not where all their pieces are stable. Um so way there's a way to quantify essentially what's the value of how they moving up around the rest of the state to sacrifice. Um but we learn experience that okay the best learning time is to say okay if I use Christ in the world all these pieces are treated in this way the best way to get the most reward in the long run but we know the most action for me. So you learn how to take actions and like move right and left move down like you know turn actions but there's a calculation there that you learn what state and state probably my favorite and if enough people sign up I really fascinating So you have like that's by the car of the human is known as the agent it's interacting with the internet by taking action to see that's action do is learn the best action um and still so you have to uh you have to learn what what leads to experiencing so there's a lot of kind of simulation or letting the robot try something a lot of things but that's actually so I said robing and this is the ideation then that's warning timer whatever happens to a lot of recommendations being warn how long it's more rewarding things but uh have you remember so one of the most recent bought some best generics in chess as a word game. So games to generate like my translations because all during this course of course useful here in main reason most prepared to do something visualize before like Um T is sometimes useful for certain uh processing. Um so there but by far most of course is going to be second here. um and your team using a max learn has um that we use to help um incredibly learn that you know how about that is that is really learning so that come from so okay so just that But it's learning will be our gosp. Which choice he makes the best and not just use mission say a is the best choice there. Okay, we ask which example illustrates to enhance customer experience in an e-commerce company. So to do fraud transactions so they'll get prediction probably that unhistics um that that might be unsurprised by way. Okay. Is deep learning for machine learning and artificial intelligence. So what's unique about deep learning? So all right let's go to three. So now you want to open that. So open the 3.1 book. Um there's two of them. We'll see how far one today probably will. Um you guys for the school you guys had it. Okay. All right. So we're going to start by talking about uh supervising um supervised and underrise after you values learn here which is learn 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 algorithms and that's just within this notebook um 3.2 going to get into uh regression um would be very interesting models learning from label data. So we have our data set you train model and our lab the goal is to learn of course model and all do remember columns and the lab and price that are two types of learning do like that versus and it's known as classification because classify examples as belonging to one another. So two main types um for many reasons generally we don't care that is continous here. We're trying to predict the temperature tomorrow. Okay. And this category, right? Classification because we are two categories just saying that would be decision or cold versus right. So that Russian value this Right. So it doesn't need to be and it's qualifications. Um so there's classification reject um finance all the time with things like loan approvals you can say here a lot of classifications um underly into a pool of so many words right control levels. umation is kind of driving where you are learning and it's of all right so to the actual models they are different um and they're actually different but at a level they're all learning the label they have lab And what is amazer all trying to learn they're all trying to learn. So here's a list of all start we're going to start with and work through comput far so good. to see why we created is a any discussion around regression next. We're going to start with linear regression and see um how we what that model is doing. Um would you kind of see the idea of it? So it should be somewhat familiar. Um and then walk to what's called a big um because we want to know that for here um and what is do is creates this is what should be good is going to be Yes. Why is this line? And the argument is the line is that distance. So we want this distance to be um this distance to be as minimum as we need to adjust this as small as possible is sales before So number is generally known as factor allation. part of linear regression. So you train here that's how we find all of this fitting. Okay. Um and you can do a soldier feature. So feature that's exactly in this feature exactly that simple. So this line will be y= a 0 plus And so sometime is and we have one model table $10. How much money and how many sales do you have on that point? Then the sales will be 700 700 units. Okay, it has and then we have one and this could be is fun. It's learned from learned from one feature. No, we're going to expand this to case we have more than feature so more broadly instead we have multiple multiples features um so this is what I'm talking about um so this can feature just like um and this is the um this model if all should have features X that's clearly the same idea trying to go really Let me turn back and find the best learners. that really um I find the sales. So um to see how it's fit which first of all is all those first column and our label is the second column and our first collector first is our features from labels because we need to standard X and Y present labels. is that we are responsible by separate consist. Why is this working? this equals this over and over again is% to 30% that's why use for training okay% I'm eating and seems for us later. Oh yeah. That's the white train and vessels to being of new tablec. Is for um yes and so or you see how and um you can run on the That's a good um for every single is looking to train it. model. So using package model allter there. refuge. So stay here. Um and we're using our features. So I need to see how this is very fit. out. So this is the line through this. the line values equals question um is there was really really is a real accord of the error function. So I do a little bit minimize loss. Um, so already that means it's not using Everything First is importance on the physical. If you have any in order to um not Because why do that? Yeah. obviously turbos and exactly Amen. I'm sorry. in your face. It is high. But it's not very clear. Then you do your own That's probably the doesn't need to destroy. So my first you have James want to make this sure. Please officer There's never How many videos you have? Uh, so yeah. Never mind. So lighting. I have This is okay with different You think that is You want to pay I wondered. is not actually results. Please learn. wizard. This is Bless you. Single. procedures. accessing wish two last I'm feeling I'm ugly. 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