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

Key Takeaways

Hey everyone, welcome to this video on machine learning using Python course. Let me ask you something interesting. You type a question into Chart GBT and within seconds it gives you a detailed answer. You upload a photo to an AI tool and suddenly it recognizes object face or even generates a completely new image. You open Spotify and somehow it already knows exactly what song you want to hear next. So what's really happening behind the scenes? Now these systems are not just running simple programs. They are learning patterns from massive amounts of data. This ability for computers to learn from data and improve over time is what we call machine learning. And today one of the most powerful tools used to build these intelligent system is Python. Python has become the backbone of modern AI development powering everything from recommendation system, chat bots, fraud detection, self-driving technology and companies like Google, Netflix, Amazon, OpenAI, they rely heavily on machine learning models built using Python to process huge data sets and make intelligent predictions. But here's where things get really interesting. Machine learning is not just for big tech companies anymore. Today, anyone with the right knowledge can build models that can predict house prices, detect email spams, recognizes images, analyze customer behavior, or even build their own AI powered applications. And that's exactly what this course is going to help you understand. So before we begin, let's quickly look at what we'll be covering. First, we'll understand what machine learning actually is and how machines learn patterns from data. Second, we'll explore why Python has become the most popular programming languages for machine learning. Next, we'll learn about different types of machine learning techniques and algorithms used to solve real world problems. Next, we'll understand how data is prepared and processed before training a machine learning model. And finally, we will see how machine learning models are built, trained, and evaluated using Python. Also, if you are interested in mastering the future of technology, then the professional certificate course in generative AI and machine learning is the perfect opportunity for you. This is offered in collaboration with ENIC Academy, IT Kpur and it's an 11 month live online interactive program which provides you hands-on expertise on cutting edge areas like GI machine learning tools like chat GBT, DAIL to hugging face. You'll be gaining practical experience through 15 plus projects, integrated labs, life master classes delivered by esteemed IT Kpur faculty alongside earning a prestigious certificate from IIT Kpur. You will receive official Microsoft badges for Azure AI courses and career support through Simply's job assist program. So what are you waiting for? Hurry up and enroll now. The course link is mentioned below. Now before we get started, here's a quick quiz question for you. Which programming language powers many of today's most popular AI tools and machine learning applications? Your options are Python, MSWord, Photoshop or Excel. Let me know your answers in the comment section below. So without any further ado, let's get started. >> I'll be your mentor or trainer for this course of uh machine learning and I hope we have this uh wonderful interactive exciting journey. All right. So let's let's begin with this journey and try to understand what is machine learning and why is there so much buzz around it. Let me first uh let me go through what all we would be covering in this particular course and then we begin the discussion. So if we talk about a learning path you know the first uh you know uh topic that we will be covering today is introduction to machine learning which focuses on the basics of machine learning. Second is supervised learning regression and application which focuses on supervised learning with an emphasis on understanding and implementing different types of regression models. Third is supervised learning classification applications like basically we're trying to cover that what are the different learning techniques available in the machine learning. So first technique that we would be covering up is supervised learning. Under supervised learning we would be covering up regression as well as classification. Then we would be move on to the ensemble learning method which focuses on advanced ensemble methods to enhance the performance and robustness of the models. Then we would be moving on to unsupervised learning and finally recommener system along with the application. What is machine learning and why is there so much of buzz? Why are you here to learn machine learning? Let's we let me put this in another way. So basically we want the machine to get trained with our data. We want the machine to learn from the data so that it can predict data. What kind of data that we want to predict? Why do we want to have want these predictions? Right? Because now are we living in the digital technology where data is all around us. Even this you know uh you know session is data right. When all the material that has been sent to you is data, anything on the news which is coming is data. Anything doing for entertainment is data. E-commerce is data. You know your work profile is data because we are living in huge amount of data. Data is all around us. Do you think is there any escape from data now? No, not now. uh when I used to take sessions 5 years you know and 7 years uh before you know uh the scenario was little trying to adapt but I don't think so there is now any survival without data can you survive without this data not moving on social media and you know one day you know as it says you know if the internet stops do you think your life also stops your your mobile phone is lost everything is lost Isn't it? The data has become a lifeline you know and now we see several applications which are working or the concepts that are being bas you know based on data. Now it automatic translation. Translation has not become difficult difficult. If you want to convert something from English to German, German to Spanish, any language translation is right there. You know, we speak to the machine and we get the translation. Virtual personal assistants are there. image recognition, email spam filtering that's that's actually comes under the domain of machine learning that it you know based on the algorithm or the pattern or the text or the words which are there it is able to filter out whether the mail is a spam or not. So what do you think would be the criteria? Generally the males with spam are saying that you have a lottery system other system or there is a bonus. So that becomes your email spam filtering. Then we have the text and the speech recognition. Medical diagnosis, online fraud detection is there you know where we want to detect uh the how is the online fraud happening. Web search uh search and recommendation engines and of course what is going to be the traffic prediction not only the traffic prediction on the roads but it also relates to the traffic going onto a particular website whether that website is going to get crashed or not. Data becoming now difficult to handle because it's digital data. You know understanding data everything numerically is difficult. You know earlier the data was this much it could be handled. Now it's becomes like this much and this much and this much and it's increasing. So we need certain algorithm we need technologies which can help to analyze so that we can improve our performance. But on if you if you look at on the overall scenario still there is a lot of confusion about the AI the machine learning and the deep learning. Yeah. What is the you know the subset or the issues like what is the artificial what is artificial intelligence machine learning and deep learning. Are you able to distinguish between the three? So this is one of the initial chess you know developed by the computers. IBMD blue chess program developed in 1997. When was it developed? It was developed in 1997 by IBM. And this particular program was strong enough to defeat the uh world chess champion at that particular point. Uh his name is Gary Kasparov. Okay. He was capable of defeating the world chess champion. Getting my point right and then we have this IBM Watson under machine learning. So AI is the bigger branch. All right. Artificial intelligence is the bigger branch right under which we have the machine learning subset right which we are going to study. In machine learning basically it works on statistical algorithm. That is why we say that before learning machine learning it is important to have a good concepts and knowledge of statistics that helps to understand. So based on statistical foundation, statistical algorithm uh you know the machine is capable of doing Google search algorithm, Amazon recommendation and email spam filtering. How like we just saw that it is capable of filtering the email. And then finally we have the deep learning which is also under machine learning in which we have the alpho the natural speech recognition and the level four automated driving system. The scenario has changed and we are into the AI revolution. So the bigger branch is still AI. We are seeing we are the you know witnessing this revolution in front of us. Under AI we have the machine learning under machine learning we have the deep learning and under deep gen AI task. Right? So when we talk about the gen AI task generation fine-tuning we have the agents automation and the virtual assistants. And this genai is now capable of even doing lot of text generation that we see we ask a lot of things to the chat GPT other uh models like Gemini copilot also we want to image uh generate images video generation all these things are being possible AI that we are talking at the moment is only and only related to the software through software we are able to or give intelligence answers. But what is basically the difference between a traditional programming and a machine learning programming? So let me explain you with this particular con uh example. So in traditional programming again we have this data right that this is my data which is 1 2 3 and 4 right and over here we have machine learning algorithm where we have 1 2 3 and four okay the data has not changed I'm giving you a very simple uh you know example now if we talk about the program initially the program and we talk about C, Pascal, Forotron, even C++ and Java and even Python they are capable of logic. Now if I want to distinguish that what are the numbers what is the logic behind that these uh you know numbers are even or odd. So simply we understand the logic that if I if I talk about uh Python I percentage 2 is equal equal to zero that means if the remainder divided by two is zero then the number it is odd right else it is odd getting my point. Yes or no? This point is getting clear. So now even if a number 10, it would automatically give me that this is going to be an even number. And if I give the number 57, it is going to be odd. But now machine learning how things happen. I give the output along with it. I say that one is odd, two is even, three is odd and four is even. Got it? Now the computer based on certain statistical concepts algorithm will try to detect that when I feed the number 10 over that it is going to be even. Can it predict me as odd also? Yes, the prediction can be wrong also. Clear? But if the algorithm has to be good enough that it is predicting that 10 is even and 57 is odd. So coming on to the concept the first kind of learning is known as supervised learning. What is it known as? Supervised learning. that if this is my known data, this is already now images that this is my input and IO feed the output. When I feed the in as well as output to the machine, it becomes my labeled data, right? That means this is an image of an apple. I feed it into the machine and when I feed this apple it says predicts that is this apple or not and it predicts it's an apple but it can predict wrong also. So might have seen sometimes the chart GPT also predicts gives wrong answer the image uh development jibli and all all give wrong answers. It's the biggest fear you know that these if the if a wrong or a incapable data is uh set to uh them then it can give false and nonsensical and fabricated information also. So this is the f fear that we are now moving ahead right AJ Char hardik but if we talk about system if we talk about the traditional systems expert systems expert systems were working that we have a user we are trying to give the query and then try to get the output out of it right if this is my query and this is how I get the output out of it and then I try to infer what is it but inference is not coming from the data but this data it's coming from data but this data is created from an expert now what do I mean by that let me explain this to you expert can be a cardiologist a lawyer in different domains right a cardiologist a lawyer somebody from in the finance domain maybe for 30 years 30 plus years of experience and weated if else knowledge you know inference knowledge that whether you are capable of getting a loan or not and if I have a cardiologist some information has been fed that maybe your BP rating is this much or your um terms if that is matching to the inference engine that's how it will give the output so what happens for example if you are a user and you enter to the user interface. Maybe all your uh you know blood reports, your BPS and your test reports, all things are given as the user interface right and then we do the inference engine and based on this knowledge base we get the output right. So what has been replaced now rather than knowledge base it is completely based on the original data and things have become complicated on images on you know nonstructured data. So uh do we understand supervised learning? Now can I say more the data more my system becomes intelligent is only capable of recognizing hexagon triangle and square and if I add a shape of a circle maybe a parallelogram maybe a rectangle it's capable of analyzing that. So that is where you know the systems are getting modeled because the data is becoming huge day by day right. So this is supervised learning. So under supervised learning we have the label data. What is label data? We have the input as well as the output where we try to train the model. After the model has been trained based on the test data we try to do the prediction whether it is a square or a triangle. Clear. We will initially start with supervised learning that we have the input the output. We will try the model. So where will we do the model? That means now the data the 70% of the data will be used for training and 20 to 30 uh you know percent will be used for testing. Then it will predict the output. Okay. And as I've been telling you, there is a very strong relationship between machine learning as well as statistics part of it. Right? So, statistics is a field of mathematics. But when it is combined with computer science, you know, the machine learning, it becomes statistics and machine learning. The idea of statistics is that it helps us to draw inferences, relationship between variables. Whereas machine learning gives optimization, prediction, accuracy etc. Right? And then we have prior assumptions about the data. Some knowledge about the population usually required. This is none. Dimensionality of the data usually applied to the low uh dimensional data and knowledge overlap. There's no ML knowledge required when we study statistics. But in machine learning, some statistics knowledge is usually needed as it is becomes the foundation for few algorithms. So if you do not know much about statistics, there is nothing to worry. It's not very difficult. Definitely concepts of probability would be required. So I would just require request you the learners to get familiar with the concepts of probability conditional probability basian theorem and probability distribution. This is what I expect from you all. Got it? Right. And to make the picture a little more clear, you know, the boundaries are not very crisp now because there is data everywhere. But to make the picture a little more clear that you know when we start with initially Python course you know we are doing visualization exploratory data analysis maths and statistics and when we try to overlap do overlap between AI machine learning deep learning we get this data science. So data science becomes the foundation for AI ML and deep learning. If I technically ask you what is learning and now you might have been hearing this word agent you know it could be a human agent it could be a robotic agent it could be an AI agent this so if we talk about a little more technical definition of learning basically we are trying to improve the behavior based on the experience when we say you are a very learner thing you experience that means means uh different types of knowledge. The range of behaviors is expanded. The agent can do more. Right? The range of behavior is expanded and agent can do more. The accuracy on the task is improved. The agent can do things better. And the speed is improved. The agent can do things faster. So what is the idea of learning? If you see this means that DS okay so if we talk about learning from the machine as well as from the human point of view this definition is valid that it is the ability to improve our behavior based on our experience right it's not always about acquiring new skills that's one of the thing that range of behavior that you are you know driving you know swimming you know stitching you know cooking of course the range is increased but also So accuracy by doing the thing again and again doing learning increases my accuracy as well as speed. And if we now see technically in machine learning the different types of learning techniques are supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. So all these of actually learning come from the fact that how we as humans also learn. So do you also agree only agree through training and testing? Training and testing happens when we are going to school we are trained and then we are tested in universities, colleges or even this session. Is there any other way we learn also? Do we learn through observations? Do we learn through our mistakes? Do we do we learn? There are different kinds of learning also possible. And exactly the same thing we also try to replicate in our machines also. So one of the learning method is observation. So one uh basic difference between supervised learning and unsupervised learning. What do we mean by supervised and unsupervised learning? Supervised learning says that this is my data right are my apples right and I tell them that this is my image these are apples I 70% of my data is used for training and 30% of the data is used for testing label data is given in absolutely correct mega label data means supervised ized learning and then the model predicts me that it is an apple and what is unsupervised learning? Have I have do I have a label data? No, I have not told that this is an apple or this is a banana or this is a peach. But the model or the algorithm, sorry guys, is capable enough to distinguish between the three of them that this is my apple, this is my peach and this is my banana. Right? So initially we will you know build concepts on this supervised as well as unsupervised learning. The machine is given huge sets of data that are not labeled as inputs to analyze. The machine needs to figure out the output its own where it identifies the patterns. And the two types of algorithm which come under unsupervised learning are association and clustering and K means for clustering problems and a priority algorithm for association rule learning problems. Right? And then we have supervised learning. The input is in the form of raw data that is labeled. The machine is already fed with the required feature set to classify inputs divided into two types of problems. Regression and classification. And then we will try to understand different regression algorithm. This is the first stage that we are going to work on. Clear? So now let's understand what is selfsupervised learning. You know here we have the input data. Is it labeled? No. This is not labeled data. But we have partial label data that this is an orange and this is a banana and its quantity is less. And here both the types of data are mixed machine learning model and this is my unlabelled data to predict the output. So it's an apple. So this is my input data, right? This is my partial data. And when I combine them together, prepare the model, I get the output, clear and reinforcement learning. The most effective way of learning from which we as humans learn a lot. That means from our mistakes, from our feedbacks, from our punishments, from our rewards. Right? So if this is the input given to the machine and the machine predicts that it is a mango and I give a feedback wrong, it's an apple, it notes it down. And now when I feed the apple again to the machine, it says that it is an apple. So take making the overall picture a quick recap that basically you know if we talk about machines there are three types of learnings we have the supervised learning where we have the input and output and this supervised learning is capable of calculating error that is output minus the input uh uh you know and what is the because why are we capable of calculating error in supervised learning because we have the actual output put over here, right? And based on the actual output, is my machine predicting the correct result that if it is an apple, is it actually telling me an apple or not? Or is it telling me this is a cherry? So I can calculate my error. So the biggest advantage or the simplest way to learn is supervised learning where we are capable of calculating the errors. Right? Here we have unsupervised learning where we do not know the output. It's just trying to do the clustering and association between the objects. And the other type is reinforcement learning that is not going to be part of this journey that's generally taken into deep learning concepts that where the machine learns from its you know uh punishments and rewards right and of course that we are capable of calculating error. Again I'm telling you in this particular course we are going to try to cover supervised and unsupervised learning uh you know concepts algorithms in detail. Now moving ahead to supervised learning there are two types of supervised learning. We have regression and then we have classifification. We have regression as well as classification. Now what is the difference between the two? Please try to understand. Under supervised learning, if the output, it's all about output. If the output is numerical then this is known as regression and if the output is categorical then it is known as classification. Getting my point learners? What we are trying to achieve that what is going to be the temperature tomorrow. So if it it gives me that tomorrow it is going to be 84° F or any other value then this kind of algorithm is regression. But if I want to predict whether the temperature is going to be cold or hot this is known as categorical data. Clear? So the regression works on numeric and classification works on variable and categoric. Two types of numerical data. One is discrete and other one is continuous. Do we understand that? And if we talk about categorical data, do we understand nominal and ordinal data? This is what is covered in data science class. So basically you know data types are divided as qualitative and quantitative very very important when you um divide the data as qualitative it's categorical order data is something you know like rating ranking they come under order feedbacks of uh like movies nominal is that there is no order as such the color of the eyes or nationality and quantitative is numerical values continuous which can be divided such as distance, salary, price and something which cannot be uh divided is example cats etc. So now I hope the concept of regression and classification is clear to everybody right. So I making your concept more clear that regression is something when the task of predicting a continuous uh you know quantity that I want to predict the price of the house that price of the house in 2014 was this much then in 2024 it is this much right and then what will happen what will be the price in 2034 Clear? So since price is a numerical quantity, it comes under regression and classification as I told you that if I want to separate whether the male is as spam or not, that comes under a classification problem. Clear? So now we can we will begin our journey in this machine learning through supervised learning. First we will try to complete algorithms which are like simple linear regression, multiple linear regression, polomial, support vector, decision tree, random forest. We are not covering neural network. All the others will be covered. Similarly in classification we will cover logistic K nearest neighbor support vector machine nave bias decision tree random forest. Again neural networks are not covered. Yeah. So with this uh we come to the end of the introduction and if you will now look at your uh slide the ebooks that is lesson number two. Now we can begin with lesson number two. So just I've prepared the foundation for that. So let me show you the lesson number two. All right. So we are now starting with this. I hope now you would be able to locate this particular file in your LMS in your material. Please look at that. So analyze the distinctions and applications of machine learning, deep learning, artificial intelligence through the real world examples of various technical applications. Differentiate among various machine learning models and explore each model learns from data to predict outcomes. Explore Python libraries for effective data manipulation, visualization, implementation and machine learning algorithm. So the business scenario says that ABC is an e-commerce company which is struggling with a surge in fraudulent transactions on its website. The manual review process for transaction has caused delays in order processing and led to negative customer experience. To address this, ABC will use machine learning algorithms to detect realtime fraudinal transactions. So machine learning algorithms are capable for detecting the fraud transaction and these algorithms will be integrated into the company's transaction processing systems to flag suspicious transactions and prevent fraud. Additionally, the company will use these algorithm to predict the customer behavior on the past purchase history thereby improving the recommendation engine's performance. Now, everybody is using mixture. Everybody wants the best you know the more you learn the best output you get. And when I say best output, you want the prediction to be highly accurate. So when your report goes to a machine learning or a AI machine, it it has to give accurate result that whether you have whether you are you know that predicting that you know it should be accurate enough to predict that you know yes you know your you are you know uh cap you have a tendency to have cancer or not you know so no so not only one technique will be used it will try to use mixture of techniques to get the best results. Got it? Now, so now are we clear? What is machine learning? So, machine learning is a subset of AI that assist systems to learn and improve automatically from the experience without being explicitly programmed. Arthur Samuel coined the term machine learning in 1959. It enables programs to learn automatically making computers more intelligent without human intervention. But human feedback is extremely extremely important because see ultimately machines are not genius. We have to tell them that this is you know a an acceptable result or not. So who is known as the father of machine learning? It's Arthur Samuel and he coined the term machine learning in 1959. Father of AI that's John Mcathi in 1956. Yes, John Mcathi in 1956. Are we now more clear what is the difference between the traditional approach where we had the data and where we were programming explicitly the output right and the machine learning approach based on the algorithm based on the data the patterns it understands it predicts the output that is why statistical techniques algorithms are the foundation for machine learning it automatically learns the features and reduces the need of manual featuring clear. It handles complex and unstructured data such as images, text, audio without requiring extensive pre-processing. Performance improves with more data and learning iterations enhancing accuracy and generalization. Right? And now do we understand this ven diagram also that machine learning, deep learning, AI are often used interchangeably. So AI encompass encompasses simulation of human intelligence in machines. So self-driving cars are you know AI all the robotics come under the category of AI but machine learning Amazon Alexa where we are giving it specific instructions and it gives us output and when I talk about deep learning deep learning involves neural network which is going to be your next stage after uh you know machine learning to understand how neural network uh you know work. How are they capable of understanding complex pattern recognition such as recognizing patterns in images, speech, text etc. So where are what are the examples of machine learning in a chess game between a computer and a person? Why do you think that why is this chess game always coming into the picture? Why do you think is the chess game always coming into the picture? Well, when we talk about human beings, a person who plays uh chess well is said to be intelligent. It's an intelligent game. Agreed? Is said to be intelligent. And even if the results go wrong, there's no harm. There are no catastrophic results. You know, even if the person is winning and says the machine is winning, it is not that harmful. Yeah. So that is why you know that that is was one of the way where AI exploded in you know intelligent gaming systems theoreance solving that is why chess alph you know read or see when we talk about a little history of AI. So in a chess game uh between a computer and a person, the computer uses AI to analyze the game, predict moves, decide its decision. AI uses machine learning to figure out the opponent is a beginner, intermediate, and an advanced level. How the AI uses machine learning to identify whether you are a beginner, intermediate or an advanced player? By predicting our moves, you know, based on our moves, it will immediately judge, you know, immediately judge that you are a beginner or a intermediate or an advanced level, right? And you might be playing a lot of games where there is AI and other you know graphics games over there, you know, especially the young generation, right? Well, I don't, but you can, you know, how smartly, you know, they are capable of hiding things and they become smarter. The level changes you know as you are also becoming smart the level of the game becomes smarter right we all observe that so AI decides its next move against the oppo opponent using a complex neural network that learns various features patterns from the data right then of course many applications are there we see machine learning all around us in spam filtering spam filtering actually use Drive based classifier, social media analysis, customer service, chat bots, now a lot of available online recommendation. We all see that sentiment analysis. How does the sentiment analysis happen? Anybody who has an idea based on the words and especially the emojis. Is it a smiley? Is it a angry face? Is it a sad face? Crying face. All these things are taken into account. So these processes allow computers to learn patterns from the data, make predictions, predict outcomes, classify target feature and improve performance. So that's what uh we want machine learning that they help to predict the outcome, what is going to be the price of the house or any other thing after 10 years down the line. classify target features based on the similarities and improve the overall performance of the system. And we have seen is there a very very strong relationship between the data and the output. Yes, the amount of data if it is more of course the output or the quality or the prediction also increases where the red line is uh the quantity and if if it is high quality data we get better results better insights and better predictions of the output. Of course maintaining the quality, authenticity and removing the errors. All these points have to be taken into account when we are talking about the data and the machine learning algorithm. Clear? Right. So when we talk about types of machine learning, ML can be divided into four main categories each characterized by its capacity to predict the our conditions or identify the patterns to produce outcomes such as what is supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. Now I think the distinction is clear. Supervised learning, we've done this. What are the three main points under supervised learning? First is the label data. What do we mean by label data? It will have the input as well as the output. Second point is splitting of the data into training and testing. Right? Generally training happens on most of the data and testing of on the rest of the data. And third is calculation of the error because in supervised learning we know the actual output also and whether it is predicting it right or wrong. Clear? And some commonly known uh you know uh supervised learning algorithms are linear regression, decision trees, logistic regression, support vector machines etc. So some examples of supervised learning are predicting temperature rise based on the yearly temperature trends. Predicting why supervised learning again predicting temperature rise it comes under regression because it's a numerical problem right numerical output predicting crop yield based on the seasonal crop quality changes. Again regression sorting waste based on the known waste items corresponding to the waste type. This types comes under classification or filtering. So under supervised learning calculation of error is also going to be an important aspect of machine learning because we have to be very clear that the output of machine learning will always not be 100% correct. Clear? Now this example are these example making the picture more clear. And if we talk about unsupervised learning very much used to you know uh identify different parts of the object image segmentation for object detection. Identific identification of user groups based on commonalities. Identification of anomalies over geographical landscapes based on the data patterns. The unlabelled data set is provided to an unsupervised learning algorithm to discover hidden patterns and to recognize their relationship. So it's not that unsupervised learning is not important. It is equally important to analyze different features, different relationships in the data. Right? Rather this is the algorithm which helps us to discover the hidden patterns and discover the relationships. Clear? And now coming on to the unsupervised learning example. It automatically groups the images based on the similarity that this is unlabelled data. Based on that it is capable of distinguishing the middleage people, old age people, the young, the infants, the teenage etc. Got it. And what is semi-supervised learning? As I've already told you, it uses a combination of small amount of label data. Sometimes, you know, the data is that's one of the constraints that we see in data is not completely labeled. A large amount of unlabelled data is used for training. Like supervised learning, it aims to learn from a function that can accurately predict the output variable from the input variable. It uses the unlabelled input to assist the learning process by collecting more information improving model generalization. So it falls between supervised and unsupervised learning. So suppose this is my raw data and I have partial that this is adults and these are kids then the machine automatically distinguishes between babies, teens and tween and also distinguishes between the senior citizens, youth and adults. Got it? So it automatically learns the correct uh you know uh groupings of the kids uh or the different people into teens uh tween and babies and the adult ones into into these category clear and another example of semi-supervised learning which we see it practically that was your question Mega that Google photos is popular example of semi-supervised learn learning that when a picture is taken it gets stored in the Google cloud platform and from slowly the Google tracks you know whose picture it is at what place it was taken so in various instances uploaders label images despite Google's lack of knowledge regarding image names its algorithm can identifying by analyzing visual features and shapes and colors and it does that it does a lot of lot for me it is able to it's able to identify my friends my family in which location I was and where and reinforcement learning is a type of machine learning where algorithms learn from the environment by performing actions and receiving either rewards or penalties as feedback. If the pro program finds correct solution, the interpreter rewards the algorithm. If the outcome is incorrect, the algorithm is penalized for incorrect predictions. It must I reiterate until it finds a better result. Right? So ultimately reinforcement learning involves an agent. It interacts with an environment. Learning from the rewards and states to choose from and then based on the output it gives the best action and if it is an error it learns it again. All right. So this is my input raw data based on the environment reward state and action. it's capable of detecting them separately. Okay. So the example is this type of learning is seen in YouTube recommendation where the user searches for a particular song. The program shows the list of available song. So when a user selects a specific song, the system trains itself to remember and deliver similar results for future s searches based on the user's interaction like views, shares etc. So this is the concept on which recommendation systems work. So other examples of reinforcement learning are game where players can play with bots, order correct tools, search recommendation income uh engines, self-driving cars and then the Python packages that we would be doing for machine learning. We are aware about NumPy. It's a very powerful tool for numerical Python computing. Mattplot lib for drawing data visualization pandas. So I hope you all are aware about numpy mattplot lib pandas but we will be working more on the scikitlearn uh uh you know uh file which consists of different algorithm but the pre-processing the other part is also being taken care before we feed in into the algorithm. So a quick recap. So machine learning refers to the machine's ability to learn from the data and replicate human behavior. AI includes machine learning, deep learning, each with unique capabilities for simulating intelligence. There are four main types of machine learning, supervised, unsupervised, semi-supervised, and reinforcement learning. Python packages are folders with modules that organize code for easy reuse and maintenance, improving the de development efficiency. Now clear. So now let's go in for a knowledge check. Question number one. Yes learners are you there? Which of the following best describes the machine learning? A, B, C, and D? Question number two. Which example illustrates the use of machine learning to enhance customer experience in an e-commerce company? What distinguishes between deep learning, machine learning and AI? Yes, it is a subset of ML that uses multiple layers for complex pattern recognition such as recognizing patterns in images, speech and text. Right? So with this we come to the end of the very introduction and basics of machine learning. Yeah. So what I'm looking forward is this is the overall uh picture of statistics that uh we have the learners who have already done data science are aware about it. The ones who are not aware about it. The different types of statistics that we have is descriptive. Under descriptive we have measures of central tendency and measures of variability. Under measures of central tendency we have the mean, mode and median. Under measure of variability we have the range, variance and dispersion. And here we have the inferial statistics how we infer the results. So this is the important part that we are looking at that that includes confidence interval hypothesis testing. So you can do a lot of search on inferial statistics and uh this is what I am saying that uh statistical inference constructing confidence and intervals on population hypothesis testing that is what I'm looking and what is the advantages of these particular uh program because ultimately you know probability in data science and AI play a very very major role in understanding uncertain certainty predicting outcomes how probable correct the output is even the LLM models the chart GPT is predicting on the probability okay this is the next word so if it is 70% above then let's predict the outward how do we model complex system enhancing AI and it is the statistics and the probability together which help in exploratory data analysis and give meaningful full insights and features. So as we are going through this flow we understand we are going to start with supervised learning. What is supervised learning? Before supervised learning you know uh I would like you to cover we would uh you know the cover the basic concepts of machine learning which I have prepared through my PPT and then we will move on to what they have shared. Okay. So the first thing is regression. You know what are the two main algorithms which come under supervised learning? Yeah, regression and classification. What is the difference between regression and classification? Regression happens when the output is numerical and classification happens when the output is categorical. All right. So I start with my PPT again so that it helps and it gives you strong foundation clear concepts so that we can move along with that. So if we talk about the first knowledge check what is machine learning? Correct answer it is see that it is an autonomous acquisition of knowledge through the use through the use of computer programs. Second question, what is the key difference between supervised and unsupervised learning? A and B are both correct. What's the key benefit of using deep learning for task like rec? How do I u Okay, you've written yes. Okay. What's the key benefit of using deep learning for task like recognizing images? Yeah, they can learn from complex details from data on the own. That's the idea of deep learning. That's the use of neural network. Absolutely correct. All right. So now we start with the concepts of machine learning under supervised learning which are valid for regression and classification algorithms. Whenever you will say you know machine learning these are the basic questions that will be asked right. So if I talk about uh machine learning or supervised learning what are we trying to uh you know do in supervised learning what is the main aim what is our main objective of supervised learning if but prediction of the data. Okay, we would call it prediction of the data future based on given data. Right? We want to predict and is this prediction always correct or it can be wrong. Can be wrong. They can be errors. Right. Right. And so they there should be a limit of accepting the errors or rejecting the result. Right. There should be some way of accepting and rejecting the results. We all understand this very in a conceptual subjective manner. Now let's try to understand it on the basis of mathematical functions. Right? So here we have the plus over here and here we have the minus over here. Right? This is my data set. This is my x-axis or and this is my yaxis. These are given as my data. Right? And what are we trying to do over here? We are going we are trying to predict the output. So what are we trying to predict the data that what is the value of this question mark? What is the value of this question mark? All right. And what is the value of this question mark? Right? Is this question mark a plus sign or a minus sign? Can you tell me what is this question mark? A plus or a minus sign? What about this? What about this? So this is my first question mark. Second question mark. Third question mark and fourth. So based on your observation, can you tell me what is this first? Do you think it is a plus or a minus sign? What does this particular data point represent? Is it a plus or a minus? Because this this particular data point is more close to the plus. So if there are chances that this is going to be plus sign more chances yes it can be negative also but we can say 70 to 75% or 90 to 95% chances are that this is going to be plus what about the fourth one? This is negative. Now what about the second and the third one? What about the second? What about the second and the third? This is going to be a little difficult to say that this is going this can be plus this can be minus based on the way the neighbors I am selecting what will be my output agreed but but if technically now if I use a straight line function ma mathematically how do we use a straight line function that y is equal to mx + c. So any all the points which lie on the left side of this line they all are known as plus they all will come under the category of plus and the data points. Now all the points which are lying on the right side of the line they will be all negative because most of the points are minus or the red points. And I say that all the points which are lying on the left side are plus and all the points which are lying on the right right side are negative. There are very few. Now this is there is only one plus sign and if you see all the points belong to the red red negative class one way I have to do something. But again the question is why this straight line that how do I decide the straight line? So one point to understand is that when data points are given that is known as a hypothesis space represented by capital H and the line is represented by small H right and it is going to be this red line which is going to divide the data points into two different classes. So there can be more than one solutions to a problem or infinite solutions. I can draw infinite straight lines. But which one to accept? The line which will be accepted is going to be one with gives me the minimum error. Right? The error word will be different for different algorithms. But I will accept that straight line which will give me the minimum error or I can say the maximum accuracy. Clear? We will calculate as I told you in supervised learning we keep the track how we can have maximum accuracy and minimum error. So what is the capital H over here? Hypothesis space is the set of all possible legal hypothesis. This is the set from which the machine learning algorithm would determine the best possible only one which would describe the target function and small h is a hypothesis function that best describes the target in supervised learning algorithm. So now technically what does supervised learning mean that the hypothesis or the small edge there can be several small edge that an algorithm would come up depends upon the data also depends upon the restrictions bias that we have imposed on the data. So next thing is we need to find out mathematical functions which give relationships between the data points. Right? So basically now the idea is that we want to find a mathematical function where you know uh we want the error to be minimum or the result to have maximum accuracy. Again I'm repeating we want to find a simple mathematical function which gives me minimum error or maximum accuracy. Clear? Thank you at Thank you for understanding. Thank you learners. So now what are the different stages that we are looking at? Now suppose data points can be of any kind. So if this is my data points over here and if I want to solve it with with a simple linear function see straight line function is a simple mathematical form uh you know calculation that y is equal to mx + c. Okay. So when y is equal to mx + c, right? And if these are my data points, now how do I calculate my error? This is going to be actual minus the predicted. It will have very high error. And this particular concept is known as underfitting. Do you think that this straight line is consistent covering all the data points? No. Right? So the simple straight line equation or function is not capable of covering all the data points that is known as underfitting. Other way is that if I draw a function which passes through all the data points so it makes it a very very highly complicated mathematical function with high degree. Right? So it makes a complicated function with highderee mathematical function that also we don't want. Why we don't want because if the data point is out of all the data points then this is going to give me my maximum error. So we want a mathematical function which is simple and which covers all my data points. So something like this exponential right and this gives me a good fit or a good balance now clear. So AJ says no. So the model even fails to predict labels what it learned right it will it will predict the labels but with