The Hundred-Page Machine Learning Book

The advantage of short books like this is that if they are well written the author has to think carefully about what to write and how to write it. That's certainly been done here. After a crash course in what ML is and some mathematical notation, a few popular ML algorithms are introduced, before Burkov takes a look at what a learning algorithm fundamentally does: optimising a particular function (normally by minimising a loss function). Other parts of the book go into ML practice, deep learning, practical problems and solutions, and tips and tricks for situations you might run into (e.g. handling multiple outputs). Unsupervised learning, word embeddings and ranking and recommendation systems are discussed. The book's conclusion talks about other areas to learn about which weren't present. The book is dense in parts, no doubt about it. Burkov lays down all the mathematical formulae but also explains things pretty well and touches on the intuition behind key ideas, along with useful pictures and diagrams. That is one of the things I liked the most: it is rigorous, concise, but not unclear. Another thing I really liked is that it touches on very practical problem discussed less frequently elsewhere (e.g. imbalanced datasets) and interesting approaches you won't find in more traditional resources (like one and zero shot learning). In contrast to what some other reviewers on the back of book say, I'd say that this book is probably not the best one for absolute beginners. It would be much more useful when you know what ML is and have done a project or two, at least. To sum up, if you want an information packed ML book that has both theory and useful practical tips, read this.

This is not the book you get for sample code and immediate applications, but it is a fantastic resource to learn more of the theory behind machine learning methods. You will improve your use of models by learning the background in this book.

This books price is a shame. Aside from that the content is good for the most part. Sadly it doesnt explain back propagation which would have been nice and theres no gaussian section which seemed odd. The best part about this book for me is its one of the few that actually explains the notation properly. I find that this subject appears a lot more difficult because of the dense notation which many books go out of their way not to define. This one does a good job of making sure you understand what all the letters and subscripts mean, and for that I was very happy

The book is extremely comprehensive with the knowledge, but it's more than enough to know the basics, better take this one, than much longer but empty in context books.

This is an excellent brief but in-depth introduction to the subject for complete beginners who have a mathematical background. In the first 6 pages it explains from very basic principles to producing a complete machine learning model using one technique. It then explains other techniques, including multi-level neural networks. It is a remarkably easy read considering the level of detail it goes into. I found it an excellent first book on the subject.

This book is one of the best books I have read on machine learning. It’s beautifully written with concise and clear explanations. The author does an amazing job in only communicating the necessary on such a broad and deep project. I got the hard copy and it’s a pleasure to have. Thank you

Difficult to believe but this book describes a variety of machine learning concepts and algorithms in just 136 pages. Of course it lacks of applied machine learning paradigms but there are plenty of books out there to improve your practical skills - e.g. Hands On Machine Learning with Scikit-Learn, Keras & Tensorflow. If you are a beginner on the field this book looks challenging but after you grasp the key concepts you will know how thinks work! On the other hand experienced data scientist and machine learning engineers can refresh their knowledge or even self-improve. Lastly, I really enjoyed QR codes which provide additional material which is constantly up to date.

I've bought a few books lately on machine learning, some with bigger price tags, more pages and a lot less information but with grand titles about "Artificial Intelligence". I learned more about machine learning in pages 1 to 5 than I have in two dozen in Russell & Norvig. The author does an excellent job with a difficult subject. He even explains the mathematical notation in chapter 2 that will bring a great deal of clarity to those who have neither studied mathematics, statistic or computer science - like me. The world needs more books like this.

I am a materials engineer and this book helped me a lot to quickly understand the concepts of machine learning with a very basic knowledge. I am very grateful to have come across this book. While I was working on my Master's thesis on a topic related to computer vision, the book was very accessible thanks to its clear explanations and helped me to quickly get into my topic. It also proved to be directly applicable to my professional work. I would recommend this book to anyone who wants to learn more about machine learning and also to professionals in the field who want a reference book. Thank you Andriy for this great book!

Es uno de los mejores libros que he visto a nivel principiante. Es importante que el objetivo del libro no es que tengas horas experiencia práctica al terminar de leerlo, sino dar un "panorama general" del Machine Learning, cosa que el autor hace de forma magistral.

So succinct and doesn't skip the math on anything. An intro to ML but has something for everyone to learn. Great to keep on the shelf at home or work for reference

I'd say no one book or course is adequate for mastering Machine Learning, but this book is really helpful! It may not cover all aspects in great detail, but it does touch all the important points and with admirable clarity. The book is like a structured learning guide, based on which we can get a baseline understanding, and then go elsewhere to pick up more details as needed. I use it in conjunction with half a dozen other machine learning books and online courses. I love this book!

If I was going to make a list of essential books in this domain it would include Deep Learning (Goodfellow et alia), AIMA (Norvig et alia), ISL/ESL (James et alia), and then work through Fast.ai on the side to get your hands dirty. Now here's the newcomer, highly recommended by the other authors in the above list: The Hundred-Page Machine Learning Book (Burkov) - basic math refresher and overview of the field, brilliant and new. Burkov has a growing interactive website and community, is actively on reddit doing AMAs, and is continuously allowing his source material to evolve, as it should in this field! Top notch resource. He links to more advanced resources in the different topics he introduces for the student who wishes to excel. I have a degree in mathematics, and I recommend this book to interested readers with any level of prior knowledge.