 What's going on everybody? Welcome back to another video. Today, we're going to be taking a look at the machine learning specialization on Coursera. Andrew Ng needs no introduction, but if you have not heard of him, Andrew Ng is the co-founder of Coursera, the very platform that we're looking on today. He was also the co-founder and the head of Google Brain. So this guy knows what he's talking about and he is extremely, extremely intelligent. I mentioned Andrew Ng because he is the creator of this machine learning course, but he created it back in 2012. But this one that we're going to be looking at today is a revamped, updated, and more enhanced version of the one that he originally created. And we're going to be diving into the courses so you can see what you're actually going to get if you decide to take this course, but from everything that I've seen is absolutely phenomenal. One of the biggest upgrades that they made from the original course is that they changed the programming language that they used in it. Originally, they were using a programming language called Octave and now they're using Python. So if you are a Python fanatic like myself, that is a huge, huge upgrade. Now I'm a data analyst and my audience is mostly data analysts as well. So you may be wondering why we're even looking at machine learning specializations because that's more data science. Well, one of the biggest things that I usually tell people is that you may not use machine learning as a data analyst, but you're going to be working with people who do use it. And so understanding how it works, understanding the terminology behind it can really help you go a long way. And it really helped me out about two years ago when I was on the data science team and they were talking about models and all these different things that they were doing. And I felt like I actually knew what they were talking about and I could kind of keep up with them. It was really beneficial to at least know the basics of machine learning. Now, before we jump onto my screen and start taking a look at this specialization, I want to give a huge shout out to the sponsor of this video. And that is Coursera. Coursera is one of the best platforms just about anything data related. So if you want to learn SQL all the way to machine learning, you can do that on Coursera. Or if you want to become a data analyst, a data scientist, or a data engineer, you can do all of your learning right here on Coursera. It has been one of my favorite platforms to learn on for the past five years since I became a data analyst and I still love it today. Thank you again to Coursera for sponsoring this video. And without further ado, let's jump on my screen and take a look. As you can see on my screen, this is the machine learning specialization. It is brought to you by deeplearning.ai and Stanford, both things that are associated with Andrew Ng. Now, there are a few other instructors on here, but Andrew is the one who's actually doing almost every single one of the videos, goes through all the math, walks or everything. And I mentioned him specifically because he's just such a huge name in machine learning and artificial intelligence. He's just incredibly popular. And, you know, the original course that he did back in 2012 was like a staple in the machine learning community. I mean, just about everybody who got started walked through it. And it's just because he's so smart and he talks about it so well. So if you haven't taken his original one, this new one is just enhanced. It just goes above and beyond without original one did. So if you haven't taken it, then this is like the time to take it. And I'm going to give you a spoiler. It's amazing. And so with that being said, let's jump into it. So it just came out like a couple weeks ago from when I'm recording this, like the middle of June is when it came out. And you're going to learn a ton of stuff in here. Right down here, you're going to build machine learning models with NumPy and Scikit Learn, building, training. You work mostly with supervised learning. Andrew goes a little bit into the unsupervised learning as well. But he'll even tell you he primarily is looking at linear and logistic regression, those type of machine learning models. So that this is like, I would say the nuts and bolts of what you're going to learn. He teaches you about the best practices for machine learning development, how to train neural networks using TensorFlow and TensorFlow is fantastic. You've never used it. And he goes really in depth into all these things, like none of these are just like brushed off or like the basics. He goes like really in depth in almost all these things, as well as recommendation systems and how they work and the math behind them and everything. It's really fascinating. Now, one thing I want to mention before we get into like the actual course is that he goes into a lot of math. If you don't know the math behind this stuff, that is okay. In fact, it's pretty common for most people who take this to not understand the math behind it. And he talks about that in one of his videos, he's like, Hey, listen, the math behind this is really important, but it doesn't mean you have to go and be able to do the math, just maybe understand the some of the concepts. And it's true, a lot of this math is really advanced. And so, again, that's why it's like, if you aren't going into machine learning, and you don't need to know all this math, still walking through these videos and understanding these concepts, I mean, it is phenomenal. Let's go down really quickly to the courses. There's three courses. The biggest one is the supervised machine learning regression and classification, again, that's all supervised machine learning. The second course is the advanced learning algorithms. This one's very hands on, so it's a lot of building the models, understanding how to train your models and things like that. And then we have the third course, which this one has not been released yet as of when I'm recording it, although it may be when I release this video, it may be available. But this goes into the unsupervised learning, recommenders and reinforcement learning. So, again, the bulk of what the original course and this is built on and what you are going to learn is the supervised machine learning, the logistic and linear regression. That is what this is primarily focused on. But the third course is on kind of the other things that are still popular, just not as heavily used, like unsupervised learning and recommendation systems, recommenders. So, let's go and take a look at this first course and see what is actually in there. All right, if we take a look at week one, this is basically Andrew kind of walking you through the basics. Andrew is like the ideal professor. I mean, just look at him. He's just like the happiest guy. And you can tell he just loves what he does. He's just like that professor in college that you want to take his course or his class over and over again, but you already passed it. He was such a good teacher. That's how Andrew is this entire time. He's just a fantastic teacher and he teaches it extremely well. So, in this first week, you're going to go through the supervised and unsupervised learning. Just what is it? How does it work and the math behind it? And then he's going to introduce Jupiter notebooks. Now, most of these videos are exactly like how he has them right here. So, he's talking and then he'll have some type of kind of whiteboard where he's talking over it and reading through it, asking questions. It's very interactive. And let me find one really quickly because he's going to go into a lot of the math as well. So, let me find the math really quickly. I'll kind of show you how he does that also. So, this is a good example of how he teaches the math in his course. So, he usually has some type of visualization. Then he has the equation that we're looking at. And typically the equation is looks really complex. Like this one to me, I would not understand it at all if I was just taking a look at it. But Andrew has a really unique way of just breaking things down, helping you understand it and kind of showing you usually on some type of visualization how it actually works in machine learning. So, if you're not a math person and you don't care about the math behind it, you know, you don't have to skip these sections because they're just so good. You'll feel like you understand it because Andrew just teaches it really well. But this is still week one. And so, in this one, you're learning how to train models with gradient descent and kind of understanding what gradient descent is. Along the way, you have these quizzes. All really great. I can't speak highly enough of this course. It's just absolutely phenomenal. In week two, we have multiple linear regression, which is something he mentions in the first one. But then you actually learn how to implement it and use it in week two. And we also go into gradient descent and practice. So, it kind of shows you how it's actually used and not just, you know, what it is like he did in the second week. And then week three, we have a lot of logistic regression. It's basically the entire week. This is what you cover. So, you have classification with logistic regression, cost function with logistic regression, gradient descent for logistic regression. And then at the very end, you have the problem with overfitting. Now, this one was actually, the math was the most interesting in this section to me in this first couple of weeks. Because this one, the visual is just phenomenal about how overfitting actually occurs and how to fix it using math. I thought it was just extremely interesting. So, again, if you're not a math person, you don't have to take it. But I love the math behind this overfitting stuff. It was extremely interesting. Now, let's take a look at the advanced learning algorithms course. And I think this is the one that most people are going to really enjoy, especially if you want to get more hands on. So, in this course, in the first week, we're going to be looking at neural networks, and then we'll actually be implementing it using TensorFlow. And so, in this first week, we look at neural networks intuition. So, what is neural networks? How is it used? Let's look at the math behind it. That's kind of what you're going to be looking at. And then we have neural network models. And then we have the TensorFlow implementation. This is where they actually walk you through how to set up TensorFlow, how to actually use it to create your first neural network. And right down here, we have neural network implementation in Python. Again, super happy that they're using Python. I think that's just a huge upgrade from the previous course, is that a lot of people and companies are using Python for machine learning. And so, I think it just obviously shows that they're keeping up with things, they're improving and making things better, which is just better for everyone in the long run. In week two, we're still looking at neural networks. Again, this stuff is a lot more of the advanced, as the course says, it's a lot more of the advanced stuff. So, it's not as introductory like the first course where he's kind of walking through, here's what machine learning is, here's how it's used. These are like, let's dive into the math, let's look how to implement, let's start using it. It goes from beginner to advanced, I think pretty quickly, at least advanced for me. So, it's kind of like a diving head first into this stuff and just really trying to learn it as you go. So, we have these activation functions. We also have multi-class classification and neural networks and how those work together. Now, in week three, he kind of takes a step back and he's like, whoa, I know I said let's just dive in and learn it and get our hands dirty, but we kind of need to take a step back and learn a little bit more so that when you're actually using this, you don't just dive in and make a bunch of mistakes. So, here are some of the things that you should look at. So, we look at some of his advice for applying machine learning on what models to use in evaluating how, if it's good or not, we also look at bias and variance and then the machine learning development process and how to actually implement your machine learning models once you've actually developed them. And then in the very last week, you learn about decision trees and a few different variations and ways to do them. So, you have decision trees and kind of what they are. And on the surface, they're pretty simple, but they get pretty advanced. That's why it's in this advanced course. But we have the decision tree section right there. Then we kind of look at how to use them, how to implement them. And then he walks you through how to actually set it up in the code. And then at the very bottom, we have a few different options, things like multiple decision trees, sampling with replacement, random forest algorithm and XGBoost. XGBoost is all over. I see it everywhere. So, again, these are things that if you're not going to be using them, if you're not going to be implementing these things, these are really great things to just know what they are and how they work so that if you are working with somebody who's using these, you can at least understand them and work with them instead of just being confused and asking a million questions. So, the third and final course is the unsupervised learning recommenders and reinforcement learning. Again, this has not been released yet. It's not released until July 19th, which if you're watching it after July 19th, there you go. It's already out there, but I plan on releasing this at the first week of July. So, as of when I'm releasing this, this most likely will not be out yet. But we can get a sneak peek really quickly into what you're going to be learning in here. So, you're going to be learning how to use unsupervised learning techniques for unsupervised learning, including clustering and anomaly detection, building a deep reinforcement learning model, and building recommender systems with collaborative filtering approach and a content-based deep learning method. So, there you go. Again, we just don't have it yet, so I can't dive into it. But it's going to be phenomenal, and I'm going to take the whole thing. Again, I am not a machine learning expert, but it has really helped me work within my team or my old team when I was working with the data scientist to really understand this stuff. So, if you're somebody who wants to get into machine learning, 10 out of 10, 100% you need to take this course. And I don't say that for every course. Usually there's a lot of qualifiers. It's like, if this, if that, I think that if you have a pulse and you are even remotely interested in machine learning, you should take this course. It is by far hands down one of the best courses that you will ever take on machine learning. And I've taken several other ones, and I've learned a lot more about the implementation and how to actually create it and do that. But in this one, you're going to learn some of that as well. We're also going to learn a lot of the math behind it, how it actually works. And for the original course back in 2012, I mean, that thing was a staple. Every machine learning person, every data scientist had taken it because it was just phenomenal. And this just builds on it and makes it better. So, if you've gotten this far and you are interested in this at all, even in the slightest, I think you should absolutely take it again. I would not be saying that if I did not believe it. It's just phenomenal. It's one of the best courses on machine learning that you'll find out there anywhere. So with that being said, I hope that this video was helpful. I hope that you're as excited about this as I am. I think it's just a fantastic reboot and upgrade of the original course. It's just really great to see them continuing to improve and make things better over time. So thank you again. And if you liked this video, be sure to like and subscribe below. I'll see you in the next video.