The Hundred-Page Machine Learning Book Book Review



100-page machine-learning book by Andre Birkhoff or in other words the start here and continue here of machine learning at least that's what I'm calling it now it's just over 100 pages about 130 or so so the title is a little bit of a lie but that's all right I mean you could read it in the space of a day if you wanted to but it took me a little while longer than that you see I like to read books slowly ingest them write down notes see I've covered the front pages here with with my favorite page numbers I mean basically the whole book right and then every page is annotated with with my own notes if the author's written something and I want to understand a little bit more I'd like to write it down in my own words so we'll keep this video nice and sure and answer any questions that that you might have before getting one of these should you read it well the TLDR of this video or TL DW is that if you're in a data science or machine learning experienced or beginner you should get this book put it on your coffee table after you've read it of course and then when your friends come over and they ask you what's machine learning are the robots really going to take over well you can answer them and tell them what machine learning is because you've read the book who's the book for I mentioned in one of my previous videos that I was reading this book and someone messaged me asking what are the prerequisites to read it well I read it from the perspective of a machine learning engineer so I've been putting machine learning techniques into practice for for over a year before I even started reading it and I still learned a lot so I think experienced or beginner you're still going to gain something further from this and you can read it online for free before even buying it because Burke ah've has released it on a read first slide later principle but I think despite that you should still just get one right because there's nothing quite like holding a physical book especially such a totem or machine learning but yeah if you're a complete beginner and you read some things and you don't understand them don't worry I'm the same I've been practicing machine learning for the past year and a half and I read some things in this I still didn't understand them but that's the beautiful thing about having a book like this is that you can go back and reference something and look it up okay oh yeah I completely forgot what normalization was and you can go back and find normalization is the process of converting an actual range of values which are numerical feature can take into a standard range of values typically the interval negative 1 to 1 or 0 to 1 now you might ask yourself what could it possibly contain in only 100 or so pages well what works right what works in machine learning it starts off with a different type of learning supervised unsupervised semi-supervised and reinforcement learning and gives you a conceptual idea of what each of them are that's a supervised learning is when you have data and labels probably the most common type of machine learning unsupervised learning is when you just have a whole bunch of data and no labels semi-supervised learning is when you have some data with labels or you can learn the labels from the data but you don't explicitly have a bunch of labels and reinforcement learning like having a computer program play chess and then rewarding it if it wins the game once you build up an intuition of each of the four main types of learning then you can start to assess what problems require what type of a machine learning solution then you've got notations and definitions so one of the biggest questions people ask me of how to get into machine learning is its how much math right and if you see machine learning like a paper or a blog post or something like that it can have a lot of these math symbols Greek symbols you haven't seen since high school or maybe you've never seen at all and that kind of stuff like what you don't know that's scary so chapter 2 is probably one of my favorite chapters is because it clears up this type of notation right so it'll go through an equation like one of my favorites which is capital PI notation now I'm taking this straight from the book so you can see here that symbol there is capital Pi and essentially what it means is product off so let's say you had a number of things ten things and n things because people like to use em for number of things and ten things let's say numbers one through to ten now if you had capital PI X I equals x plus I 1 I 2 I 3 if you see that capital PI symbol it means the product of the things all the way up to n so if we had 10 things 1 times 2 times 3 times 4 etc all the way up to 10 which equals 3.6 million or something do you see examples of these different symbols explained all throughout chapter 2 and what that does is it sets you up right it sets you up with the math notation that you need to know for the next chapters which explains some of the most common and useful machine learning algorithms things like linear regression logistic regression support vector machines k-means clustering the list goes on right but most importantly you'll learn about what type of algorithm should be used for what type of problem like what kind of algorithm should you use for a classification problem what exactly is a classification problem now I've taken this definition straight from the book classification is a problem of automatically assigning a label to an unlabeled example spam detection is a famous example of classification you'll find these kind of definitions sprawled throughout the book so if there's some kind of common key term that's used in machine learning it'll often have a one to two-sentence definition okay so you've read through the fundamental algorithms then the book dives into the machine learning paradigm taking the world by storm deep learning and neural networks and this is the kind of machine learning that you'll often hear referred to as artificial intelligence or AI but after reading through the previous chapters in the book you'll start to realize that as much as normal works in deep learning as valuable as they are and as much as their artificial intelligence you'll realize that they're also a clever combination of the math functions you've read throughout the previous chapter you'll also see how a machine learning engineer or data scientist works with basic practices and advanced practices now what do I mean by this all right now you'll see in the book why a data scientist or machine learning engineer doesn't usually invent a new algorithm instead their job is to figure out how some of the best algorithms can be applied to problems right like finding information out of data but what you'll also see is that much of the work rather than then applying the algorithm right is making sure that the data is in a format that the algorithm can be applied to what have you had missing data or what if one of your labels had more samples than the other okay in balance classes now what if your models doing too well okay overfitting or not well enough underfeeding now what if you got 99.99% accuracy is that the right metric to use what about the precision and recall or the area under the curve don't worry the book has you covered with these finally don't forget the online wiki the book is scattered by each chapter let me follow on for you the book is scattered throughout well these you scattered throughout no these QR codes are scattered throughout the book so what you can do is grab your mobile phone scan the QR code and if there's something like more reading or code examples or something like that if you want to dive deeper so basically the QR codes are for those who it wants an extra curriculum all right so it sounds like it's an amazing resource and it actually is but what doesn't it contain everything in machine learning right those books are a thousand plus pages far more scarier and you can't really just put them in your backpack and take them wherever you go you can use this one as a foundation and then if you want to dive deeper on something maybe one single topic right then you can grab one of those thousand page books and use the extra nine hundred pages to really dive deep I really wish I read books like this more often right so two years ago I started learning machine learning created my own AI master's degree and this is on it now it would have been on there from the beginning if it was out but remember we're learning anything it's always going to be difficult right learning by definition is hard have a bias towards doing so that means read something in here if you want to try to learn it apply it if it doesn't work that's okay reread it try and apply it that's that's how I learn best that's how a majority of people learn best right put things into practice all the links you need including an article version of this video will be in the description so check it out get yourself a copy of the book do yourself a favor right read it end to end take notes to start here and continue here off machine learning as always keep learning keep creating I'll see you next video

13 thoughts on “The Hundred-Page Machine Learning Book Book Review”

  1. I’m a recent CS/Math grad that has just watched youtube videos on ML & read parts of a couple online books, but I’ve never directly implemented ML myself so still consider myself very beginner with understanding it, so when you asked ‘What is normalization’ I kind of said it in my head and was happy when I was right haha

  2. Thank you a lot about it. Could you share us some useful github about machine learning from beginner to advance. Thank you

  3. Awesome as always. Your explanation of a what a data scientist is perfect. I’m happy that the book covers all those topics.

  4. Great video as always. I have been reading this book for while and just in time I found your video 😄

  5. Great review, Daniel, and the article as well. You have made a gigantic work with this review, including the written blog version: https://mrdbourke.com/blog/the-hundred-page-machine-learning-book-review

  6. I've seen this book promoted in several machine learning YT channels. This book is getting popular very quickly.

  7. Have an iMac arriving tomorrow. Looking forward to coding on something other than a 13" laptop screen lol.

    Picking this book up too.

  8. make sure the video is over 10 minutes so you can get an extra advert in and I can give you money just by watching your great content!

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