I would like to quote the famous saying by the ex-CEO of Apple, Steve Jobs. He says, you need to start with the customer experience and then work backwards toward the technology, not the other way around, right? So you find what the problem is with the customer. And then you work towards the technology that can solve it, right? Not like you have a technology and then you find a use case to implement the technology in that. So this is an interesting fact. The market analysis says that acquiring a new customer is like five times expensive, more expensive than just maintaining your current, your customers, okay?  Not, you don't make them go for your competitors. So about Verizon. So Verizon is basically one of the largest telecom providers in US. So we provide personal and residential network services. Personal as in, we provide networks for your mobile phones and then residential as in we provide home internets and then your internet TV and then your home phones. So basically this is the number of active users we have as of now. 146 million users who are like having various kind of services that we have available and out of these we have four lakh people who are actively using mobile apps with us to maintain their accounts like paying their bills, now switching their mobile plans or getting new services, stuff like that. So that is a pretty big number for us. And seeing the 146 million user base, we really get a lot and lot of customer calls every day. And most of these calls are very minor queries regarding, let's say, I just wanted to activate my SIM card, I'm not able to find it. So there are some findability issues in the website or we don't place it right there where they are looking for, right? And then some other issues would be like Verizon has launched a new plan, data plan and then I'm not able to figure out how to do that. So these are very minor issues that doesn't need a customer service call. Why because every customer service call that we receive costs Verizon around $15. So I mean a lot of customer call citizens per day, we receive around three lakh calls and every call would actually cost us around $15 because that is at all free. So doing some mathematics that is so much of money for us. So every day Verizon spends around $4.5 million just for your answering your calls and your queries through mobile phones. So this is what we wanted to do actually. So we wanted to increase the customer satisfaction and decrease the customer call volume. So this is our intent and the whole exercise that we wanted to bring it out. So as a part of this, we cannot compromise on 24 bar seven customer service that we are providing now. Why because internet is so mandatory in US because people would have opted for a work from home or they would have been watching their favorite cereals with their internet TVs and then if your modem or your router goes off, then our credibility or reliability is gone totally. So we cannot compromise on this. But we still wanted to maintain our customer satisfaction and decrease the call volume. So that was really a challenging task for us. So that is when we found out this would be a suitable technology to adapt and implement in our system so that we would be able to actually solve these two problems parallely. There are lots of flavors in AI. So basically the flavor that the first one is the narrow AI or the weak AI that the industry is following. Which is basically working with the request and response manner. So you get a request and you actually have a response for that that is already there and scripted and then you actually show it to the customer. So that is the first flavor. The second one being strong AI which has a consciousness and mind, which actually has a little bit of thinking that is not available in any of our industries or in the science that is going on. Followed by the third one is the general AI. General AI basically is as intelligent as being a human being. Which is very far from the future now. And then the last one is the super intelligence, which would actually surpass your human brains. So now what we have adapted is the weak AI, the first one where we get a request from the user and then we give a response for them. One part of this weak AI are the NLP, which is the natural language processing, which actually helps us to understand what the user is typing or what the user is trying to search for. And then we process their language into a machine learning and then we give out the information. Using these two technologies, we implemented these text-based chatbots. This was one of the things that actually fantasized us because the market value of chatbots are around 700 million as of 2016. And we also had a fact that 80% of the businesses are going to have chatbots by 2020 for their business purposes. So all these facts merged in together so that we were really confident about solving our problems with the text-based chatbots. So we implemented these text-based chatbots both in our mobile app and in our website. So this is how it looks like in the mobile app. So we had a search icon with a plus, saying that it's going to be an enhanced search for you. And then this is a chatbot that will be available in our desktop websites. So whenever the user opens the search, it actually opens a chatbot. And then the user can search whatever they want, they can converse with the chatbot and it'll actually give you information that you're looking at. So I'll be showing you examples about how it looks like. So basically, we get around 15,000 in-app searches every day from our users. And then 45% of these searches are basically goal oriented. Let's say if a user comes to our app and searches for something like I wanted to trade in my old iPhone 6, right? So that is a potential business for us which we cannot lose. So if we give him the right result, he would actually trade in his old phone for a new one with us. So and then we get some revenue out of it. So we cannot compromise on these kind of searches. And 45% of the search analysis that we had done, we had researched on the searches that the customers had done previously. We're all goal oriented and they were made to get a decision out of it. So we finally told our customers not to search anymore but to converse with us. So the difference of searching and conversing is that when you search, you just use keywords. But when you converse, your context is very specific. So we get a lot of information about the intent and the context of how the user is trying to approach to us and how the user is going to get benefited out of it. So basically, let's say the user is trying to search for, so if the user is trying to search for his data balance, he just need to say, how much data do I have? And then the chatbot will actually take him to the module where his data balances are shown. And if you wanted to change your plan, you just say, so when you have to change a plan, you just need to say what plans can I change? And then it will take you to the most recommended plan for your kind of usage. And if you wanted to buy a Galaxy S, you just type it and then it takes you to your product description page. So basically, we are getting rid of a lot of search results, a lot of links that the user needs to go through. We are taking them directly to the context of what they are trying to search for. And these bots, they don't only work on chat, they are also working as an intelligent agent that actually will be like trying to find out what you're going to search and then they'll give you the right information up front. So whenever you open your app, you'll be getting the information that you're already looking for. Let's say you're running out of data, it obviously shows you how to get more data or how to get ways to get more data or extend your data limits. So that you don't run an overage costs for your bills. And then if your bill is ready, we obviously show it up front. So when you open our app, your bill is already ready and it's ready for your payment. So this gets intelligent as you are like using it more and more. And then the information on the front page of your app will start showing you more and more contextual information as per your usage. So when we wrote out this, there was a tremendous response. So one of our users, Angela Smith, from in Google Play, she has mentioned that nine out of ten times, she gets the right answer at the right time when she opens the app. And then she has been extremely helpful for the app with the app. And then she loves the app and she feels great about the app. So now that we have kind of found out a way to help people. But that is a very small amount for using mobile apps. But our user base of 146 million people is really vast and we really wanted to cover the rest of the people too. So when we again dig deep into the user patterns and how people use the mobile phones, there was an interesting thing that every individual in the US, they'll be using around two hours and 25 minutes on average to spend on mobile apps on their mobile. So and then complimenting to that, six out of ten apps they use frequently are for messaging. So and then one more fact that was complimenting was 50.6% of people were actually feeling comfortable to talk to a business over a messaging app than in a phone call. So all these facts were really merging to a center point where we wanted to find something, some way where we can reach the people using these messaging apps. And then we found out that Messenger has already had a chat bot and then they have a huge user base of 1.7 million as of April 2017, which actually would help us to merge the gap between our user base and reaching out to the users using the mobile app. So this, we are also rolling out the chat bots in Messenger app. So anytime you open your Messenger app, you will be getting notifications of if your bill is ready, if your data is out, and then you can chat with them, you can change your plans, you can trade in your old devices and what not. So all these things, implementing chat bots in all these three stages, we actually improved our customer experience to a greater extent and reduced our findability issues too. So following with this, the next step for us would be like working on the voice church. So the fact says that by 2020, 80% of your searches is going to be through voice. So we are trying to merge with Siri, I just want to update my plan. It actually would talk to our app and then give you the right information to upgrade your plan. And then same goes for Alexa. So Alexa, what is the trading value of my iPhone? It'll actually talk to our app and say, hey, your trading value for your iPhone is going to be $100, do you want to trade in? And then you go with that. So the next way of reaching people faster and easier is the voice searches that we are trying to work on now. And I would like to end with the code by Jeff Bezos, CEO of Amazon. He says, what's dangerous? It's not a wall. So as a telecom company, we are trying hard to transform from a telecommunication-based network provider into a technology company. Our recent acquisitions, if you would know, are Yahoo and telematics who are into searches and intelligent cars and stuff like that. So our transformation is hard and real, so we are all working on it. So I think this example and this use case would help you all to transform more into our businesses and as a designer. Thank you for this wonderful opportunity, yep. Any questions, suggestions for us? We have time for questions, one or two we can take care of. So I wanted to ask you, you had these benchmarks in the beginning and then in the end you said you improved customer satisfaction, decrease number of searches for issues. So can you share with us like the degree of improvements? I guess you measure them somehow like, was it a 200% improvement or like? Yeah, so the customer satisfaction index was considerably improved. Like let's say we had around 70% of people satisfied and then when we implemented all these in all these various platforms, it was around like 10 to 15% improvement. Because the app and the desktop users were a little bit low. And then when we had the chatbots in Messenger, that was tremendous. We were able to reach a lot of audience. So that also increased our credibility and reliability on our brand value mode. Okay, so we started out with data analysis of all the chats that I mean the searches that the user has done. So from the website and the app, so we used to take key words of it and then we used to categorize how the user is actually searching for what information, how he navigates and finds the information. And what and how much time he takes it. Let's say he wanted to trade in his old phone, he just goes and types in trade in. And then he has a long list of search results from our website, which has a lot of articles and then he has a lot of videos to watch. And then we're not even sure if he has already tried in it. And that was a potential search that was missed. So by analyzing all these keywords, we found there is a trend that the user searches and the most searched and least searched. And then we had chatbot scripts written for the most searched keywords. Let's say trade in my iPhone and then we obviously know their account information, what iPhone is linked. They don't even know, they're not even necessary to specify the model number or the color of the storage, we already know that. So you just say trade in my phone, we know that it's an iPhone 6, 32GB, we give them the right answer by the chatbot. So that is how we built our chatbots.


Last night, I was trying to think of a word that would encompass people that made the plunge and started their own business and run a float centre. I was thinking for a while what word would sum it up best. The word that I think sums it up best is brave. The reason I say brave is because it's not for the faint-hearted. Ac ar ychydig yn dweud. Felly mae'n ddweud am gwrthododd. Felly mae'n gorfod o'r busnes i gyd ac yn iawn o'r trawr. Felly, rwy'n mynd i 1993, y llwys yn dweud y flote-centru yn Llyfrgell called Floatworks. Felly mae'r erioed y pre-internet. Felly rwy'n fwyaf'r gwybod mewn gwybod. Felly rwy'n fwyaf'r flote-collectif. We really learnt by all our own mistakes, but we knew we were doing something right, so we opened a two tank float centre and we used aquatonics float tanks. They were a local company and they also had a float centre in London on the other side in West London. Typically getting people in float tanks is not the easiest thing to do. We really really put a lot of effort into it to get people through the door. We were open from ten to ten every single day. We had days where we didn't have a single booking in our diary. We were keen to get a third tank at our centre and at the time we were looking around the globe for any other manufacturers and we came across the Australian float tank, the Apollo. We worked with this guy for a little while and we wanted to see if we could sell it as a product. For the leisure industry. This is a picture of me and my partner Tim exhibiting at the leisure industry week. Even back then there was a huge amount of interest, but there wasn't a lot of people that were as brave as us. Still looking around for more equipment to sell. We went off to Singapore and we dealt with this guy, Gillies Buchan, who was a real character. The engineering started to get really good and we sold a few of these tanks. Again it was a little bit of a logistical nightmare company in Singapore. Time differences, phone calls in those areas weren't easy to make communication. But we sold a few and we learnt a little bit more about float tanks. So in 2000 we had to leave our three tank float centre in London Bridge and move around the corner. We moved the float works to a bigger location and we met up with Colin Stanwell Smith from Floaterway. He was an engineer getting into the float tank business. We had the first six floataways at our centre in London Bridge. We opened with six tanks, two members of staff. Now we've got something that looks a little bit more desirable. People showed a little bit more interest and the business was going well. We added a seventh float tank, a seventh float away. It was challenging. It wasn't quite the optimum thing that we were looking for but it served the purpose and those tanks were there for the best part of 15 years. The first float tank that I came across that had a engine, a separate filtration unit, was this float tank. It's called the Ova and I first came across the designer and producer of this at the 1997 Rest Conference. Very interesting gentleman called Jürgen Taprik. Some of you may have met him. A little bio of him, here he is. He's a very established photographer and you may or may not have seen this Johnny Walker advert for the iconic whiskey brand. Jürgen was our next float tank purchase. This was our eighth float tank at the Floatworks. It's a really, really good product and I learnt a lot from this and I thought to myself, this is definitely the way to go. It's easier to operate, easier to maintain and the customers really liked it. We worked with Jürgen for quite a while and we got the opportunity to buy his design and his business manufacturing setup. Problem with that project was that Jürgen got so emotionally attached to his float tank that in the end he gave us our money back and wouldn't let us buy his business. At this point we're a little bit stuck so we designed our own float tank, the Isopod. I think this really represents a change in the float tank industry when this came along. It's the first real float tank that ticked all the boxes in size, operation, desirability, but I think most of all what it's given our industry is a perceived value. People perceive value when they see this. They wanted to get inside it and it's given us all an opportunity to put a good price tag on what we do. I know as well as you do that it's a float at the end of the day but when we're trying to get people through the door it's very helpful. This was our ninth float tank at our float centre. Out of all our members that were floating there from 1993 everybody wanted to float in there. The Isopod was constantly booked. I think about 2002 I was thinking about inflatable float tanks for some reason. I always thought this is something that I wanted to do. I finally got round to doing it probably a few years ago but this is something that I've worked on for the last few years and the version that we've got here today is pretty on point. I've been very fortunate to work with a team at Superior to get this to market and to utilise their filtration system. If you haven't had the chance to have a look at it, it's a very durable, robust product and it's something that we've had under test at our facility now for the best part of the year. We've also got some interest in domes that go over pools. Again this sort of thing is very easy to deploy. You can use it on a temporary basis.  Fast forward to what we've been doing recently. Myself and the team at Superior were asked to put a float tank into a mobile vehicle. This is for the use by the tennis player Novak Djokovic on his tour of Europe when he's going around the European tennis tour. Key to his success. He has this in his truck with a hyperbaric chamber. He doesn't have a shower inside. He's not too bothered about that. That's something that he wields as his secret weapon. Just to conclude, keep doing what you're doing. I think I've probably experienced every possible calamity that could have happened to you in a float centre. I think that there's such a wonderful group. There's so much support available nowadays. Just keep what you're doing. I've had a great life out of it. It took me on some amazing travels and I've met some amazing people. I think we should all be proud to be in this really interesting niche industry. There's a lot of people looking in on us that would love to do what we do. Just remember that and keep doing your best. Keep supporting this industry. You're all innovators. What more can I say? Thank you very much.


What's up guys, it's Josh and we're back again with another video. Today we're gonna be talking about my own personal journey over this past winter break where I was kind of relearning and also teaching myself some new concepts in artificial intelligence and machine learning and I'll kind of go through the process of why I was doing that, how I'm doing it and give you guys some tips as well if you're interested. For those of you that don't know, my name is Josh Beasley and I'm currently a senior at Yale University studying computer science.  If any of that sounds interesting, hit that subscribe button down below and join the community. So this is kind of an interesting video and something that I've been working on a lot recently but I thought I would share with you because I feel like it's something applicable not only if you're interested in AI machine learning or if you're just interested in learning how to teach yourself something because I feel like that's a very valuable skill that I picked up over the past couple of years especially as I've gotten into college and it's something that's going to help you infinitely along the line later in life, just being able to pick up a textbook, pick up some sort of resource and teach yourself brand new content, especially content that literally is that and the entire book that I used would have been an entire college class if not more than the amount of content that you normally see in a college class. So for those of you that are unfamiliar with AI or machine learning, obviously we have the quintessential examples that we've seen in the mainstream media, Terminator, SkyNet and obviously you hear that Elon Musk is using AI for self-driving cars. If you guys aren't already aware, AI and machine learning are penetrating every realm of our lives as we speak of it, whether it's your Amazon recommendations where they're recommending something that you're just thinking about buying. And a lot of people think Google's listening to us but the scarier part is Google has so much data that they're able to accurately serve us ads on topics that we did happen to be just talking about. I mean, Netflix recommendations, everything along the lines and obviously the more physical things we see like self-driving cars but those are still a couple of years down the line. So AI is something that's already become so pervasive and a lot of people don't even know it. So in terms of like machine learning and trying to come up with the definition of it, the way that I like to think about it, I forgot who first exposed me to this methodology but basically if you think about software, they call it software 1.0 which is basically writing a program, giving that program input and then it's outputs your desired output, what you wanna see from the program. So if I had a program that a calculator that did math, I would input what numbers, what operation I want performed on them and they would give me the sum, the product, whatever I wanted from that. So machine learning or what they call software 2.0 is a completely different methodology. You are no longer technically writing the program. Rather you're creating a model that's flexible. It can be trained or it can be changed and you might think of the typical model as like a neural network, a support factor machine or even like linear regression, something as simple as that. But basically you're giving it input and it's giving you some random output then you take that random output, you compare it with the desired output and you use that to kind of adjust the model so you're minimizing the error. So what I just talked about is one realm of machine learning called supervised learning. There's also unsupervised learning where you don't have specific labels on the data and that normally consists of learning where it's you're clustering certain things together or using certain characteristics to kind of build associations between different elements within the data set. And then lastly there's reinforcement learning which you probably heard a lot about when it comes to like game playing and that's where you have this, you have a bunch of different states you can move to, you have a bunch of different actions you can perform in each of these states and certain actions lead to certain rewards. So you use a bunch of math and algorithms within that that I probably won't go too deep into in this video to basically calculate what actions and what states will lead to the maximum amount of reward. So that's basically a brief overview of AI and machine learning for those of you that are interested. It's something that I've taken a deep dive into recently but now I kind of wanna talk to you about why I'm actually learning machine learning. So there's two kind of main reasons. So the first main reason is my thesis is going to evolve a lot of deep learning. So when I say deep learning that's mainly the neural network supervised learning part of AI that I talked about. So basically I'm going to be using deep learning and combining that with a lot of concepts and cybersecurity. So if you guys haven't seen it yet check out my senior thesis video where I kind of talked about this but I'm gonna basically use deep learning algorithms specifically like generative adversarial networks and recurrent neural networks to train them on passwords and then have them be able to produce unique passwords in hopes of finding other passwords on these lists. So basically it's a password cracker an AI that will produce random passwords some of which will hopefully be able to crack your own. Now if I didn't explain that super well that was just right off the top of my head but definitely go check out my senior thesis video I kind of break it down into all the different steps if you're interested in that. And the second reason is it might be useful in my career in the Air Force is like a cyber warfare officer because I will be commissioning in May and I hope to become a cyber warfare officer we'll find out about that soon but basically not only are there a lot of opportunities for cyber security related work but the DOD also has this thing called the JAKE which is the Joint Artificial Intelligence Center and they have a lot of great opportunities for any service men or service women who are interested in pursuing artificial intelligence especially those that are already trained in cyber. So I hope to pursue that as well and I think that learning AI rigorously would be one of those first steps. So now I'll kind of talk about I didn't come into this completely new and I've had some prior AI experience because of my coursework. Unfortunately Yale doesn't let you declare like a certain focus like a lot of other CS majors do so I couldn't focus in AI or machine learning but I did have the opportunity to take a lot of different AI and machine learning classes. The first of which was a class I took my sophomore year called Artificial Intelligence and this class was good but it was really just a brief survey course where we covered so many different topics and really just dipped our toes in the water in these different topics and really didn't go in depth in anything. So you know we started out with search. So like A star search, depth first, breadth first, different ways of searching to find certain paths and we built that into kind of these tree algorithms. So like minimax, alpha beta pruning, Monte Carlo tree search. These are very applicable to games specifically where you can kind of build out the game tree of all the different possible moves and then use these searching algorithms to search the tree or prune off certain parts of the tree to find the optimal move that you make. And soon we moved into some computer vision topics like convolution, edge detection and then we finished off the semester with some neural networks and some reinforcement learning. So it was great but like I didn't feel like I was very competent in any single area of AI. Later, actually last semester I took two different AI classes which I talked about in my video where I reviewed my senior fall classes so I won't go too in depth into them now but one was called Computational Intelligence for Games where we looked at some AI techniques specifically relating to games which were mainly some more basic techniques like the search techniques I talked about before but mainly with a focus in reinforcement learning and then also using artificial neural networks. And my second class I took last semester was called Big Data AI in Russian Politics and that was more of a data science course so we more focused on more, we use R and we focus on more data science techniques as they apply to machine learning not like the neural network side of things. So it was more like K-means or dimensionality reduction, those sort of things. But I still was able to make a final project using a convolutional neural network in Python because my professor allowed me a lot of flexibility. So that sounds like a lot and it was but a lot of these courses didn't go as in depth as I'd liked. So I generally had a familiarity with all these different topics in AI but I really wanted to kind of do a deep dive into deep learning. So for those of you that are interested I kind of want to walk through the different I guess prerequisites that you need before you want to start machine learning because a lot of people will tell you oh you need all this math, you need to know calculus, you need to know linear algebra, you need to know statistics and yes that is true but you don't necessarily need to know all of that stuff and there are a ton of free resources on Khan Academy or a bunch of other websites to kind of pick up these more specific skills that you need depending on what area of machine learning you want to go into. So my experience, I'll personally be talking about my own personal experience as it relates to deep learning. So the first area is math. You're going to need to know some basic linear algebra because if you know anything about neural networks it's basically a big massive dot product, massive matrices using all these different weights and parameters. So it helps to know actually how those operations works. Next is calculus. So you don't actually need a calculus with the modern frameworks for actually coding neural networks like TensorFlow or PyTorch but it is helpful to understand some calculus basically because the main algorithm back propagation that's used for optimizing neural networks relies on gradient descent which is basically a partial derivative where you're minimizing the loss of the network using all the different weight parameters so you can adjust those properly. So it does help to understand some basic calculus.  So you also need to know statistics. I don't have that much of a statistics background. I haven't taken a single college level statistics class. I guess I took AP statistics in high school but that's mainly used for some of the more what you would typically lump into data science some of the techniques that don't necessarily involve deep learning you know like logistic regression, linear regression those types of things support vector machine statistics are probably a little more helpful. The next area is programming. So basically both of the big neural network frameworks right now for deep learning which one is TensorFlow and then the others PyTorch they both use Python. So you're gonna need a familiarity with the Python programming language and then also a familiarity with both of these frameworks where you use Python to build neural networks and build these models and train them. Also when it comes to data science, NumPy and then pandas are two other packages that are very helpful when it comes to wrangling your data because a huge part of deep learning is not actually building the network. You can find a lot of these networks publicly available or kind of tweak them for your own uses. A lot of it kind of becomes collecting data, labeling data and then mainly what NumPy and pandas are helpful for mangling that data into whatever form it's most optimal for your neural network because a lot of times you kind of have to normalize all these values between zero or one and kind of pick those parameters and the range that the values that those parameters can take very carefully if you want your neural network to be optimized. The next kind of knowledge that you'd want to kind of dig into is the underlying principles of machine learning. So this involves, I mean just to name a few off the top of the head but how you go about collecting your data, what a training set is, what a testing set is, how you can perform validation on machine learning models before you actually test it on the testing set and then obviously the different architectures of neural networks, how a neural network works, what is gradient descent, what is back propagation and that's where kind of the sum of the math ties in. Also, if you're interested in any computer vision or like in any types of neural networks that will recognize pictures or just take images as a data source, knowing about convolution and those different image processing techniques, I luckily was able to learn about that in one of my electro engineering classes but that also helps a lot. Like convolution, max pooling because it's not only neural network layers, you also have convolutional and pooling layers within like a convolutional neural network. So that would help as well. Kind of the next step I would take is familiarizing yourself with different models. So like I said, a convolutional neural network is one, you have the standard artificial neural network, you have recurrent neural networks which are very good with time series data, you have a bunch of different types of generative models whether it be a GAN, like a generative adversarial network which actually has two networks that have a bad line against each other or like a variational auto encoder, those all kind of fall into that category as well. And lastly, you just need practice. Like I needed, I needed to build these models, I needed to train these models, I needed to practice, you know, wrangling data. I mean, there's a lot of cool data sets that you can use already publicly available online. If you check out Kaggle.com, they have competitions for specific data sets as well, which is super helpful when you're learning. Now I will be talking about the actual resources I used. First of all, I kind of got an introduction to data science. I hadn't been super familiar with NumPy and Pandas, specifically in Python before, I was fluent in Python but hadn't used those packages extensively. So I found it very useful because I had a free data camp subscription from that political science, big data and AI class I was talking about earlier. I was able to take some of the data camp courses that related to NumPy and Pandas just to kind of brush up on those and get familiar with those. The next step I did to kind of familiarize myself with some of the bigger picture concepts within deep learning was MIT has this deep learning course that they put on every January and it's publicly available on YouTube. So I was literally, it's only like six-ish lectures. Somebody correct me if I'm wrong in the comments if they know more about it. But it's like six lectures and they go through different types of networks and I was shocked by how much they were able to condense this very complex material into just like an hour-long lecture. And the professors that teach that class are amazing and you can actually do the same projects that some of the MIT students are doing. So that was kind of a great big picture and introduction until I kind of went into the book work. Speaking of the book, I highly recommend, let me get this book for you guys. I did a lot of research on what books to choose when I was actually starting this journey. But this by far was the one that I was most highly recommended. Hands-on machine learning was scikit-learn, Keras and TensorFlow. So Keras and TensorFlow are the neural network packages that you can use and then scikit-learn is more just for general machine learning types of stuff and working with data. And this book is incredible. Like this is version two. I think version one doesn't include as much like deep learning stuff. But version two, I mean, it takes you all the way from a linear regression to a GAN or like it even has a section on reinforcement learning so you can implement some like deep Q learning and those types of things. And this book not only does it walk you through everything and have beautiful diagrams, is it super understandable, but it also has a ton of practice problems where it kind of reinforces not only the concepts but gives you different data sets and chances to implement a certain model on a data set and it gives you like a benchmark accuracy score. But also everything in the book, all the code they present is publicly available as Jupyter notebooks. Like you don't even need to buy the book to access the Jupyter notebooks. It's not really gonna make sense but you can access his notebooks online on GitHub and it's all there for you. In addition to the book and the MIT deep learning lectures, I also found some other interesting channels online specifically YouTube that were super helpful for me. One is Andrew Ng who has done a ton of deep learning work. He's kind of done big name when it comes to education and deep learning. He has like a full course era course. I didn't end up doing the course but he does have a lot of publicly available YouTube videos from that course online. So I checked out some of those and then also for some of the more statistics heavy data science topics within machine learning, within the first part of the book when I was learning scikit-learn. StatQuest is a wonderful YouTube channel that I discovered recently that walks through these concepts and makes them so easy to understand and helped kind of fill in some of the gaps in understanding that I was stuck on within the book. So this was a way longer video than I thought it would be. But basically I kind of hope I lay down what is machine learning and why I'm doing it. All the different kind of prerequisites and steps you should take if this is something that you're interested in because I know there's a lot of CS students that are watching my videos and may not have been exposed to this type of stuff but yeah, you know, take some classes at your university kind of get exposed to it. Realize it like unless your school has a specified deep learning course or a specified machine learning course it's gonna take some digging to kind of become familiar with a lot of these more complex topics but I was able to do it in about a month and I'm by no means an expert. Don't take anything I say in this video as expert knowledge. I'm still learning myself and practicing but I really hope to kind of take you guys along as I kind of implement these projects to start working on my thesis and use this kind of newfound knowledge to kind of further my experience as a CS student. So if you have any questions about this, comments, drop them in the comment section down below. I am responding to almost every single comment these days. If you enjoyed the video, drop a like. It helps me and supports the channel a lot more than you think. It really helps the algorithm and helps this video pick off. If you're new here, if this is the first video that you've seen and you're interested in this AI CS stuff, I have a ton of other CS related videos. I heard my experience as a CS student on this channel. So please check those out and hit that subscribe button so you won't miss another one. With that, I hope you guys enjoyed the video. I hope you're staying safe and healthy and I'll see you next time.