The Hundred-Page Machine Learning Book (The Hundred-Page Books)

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 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

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

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 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 all pure dust gold, it will help you to understand everything you need to know about ML

For the most part, I liked the short and concise explanations. They were so concise I found my self reading and rereading sentences simply because there was so much information condensed into them. I disliked the treatment of backpropagation, which was almost non-existent and the explanation of convolutional neural networks was difficult to follow -despite the fact that I know how these networks work. Overall I feel that this is a good book to read if you have already had a healthy introduction to machine learning from other sources but there is no getting away from the fact that it is a little too short. The price also is a little high for such a slim book. There are some dreadful books about machine learning doing the rounds at the moment. This book is not one of them.

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.

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!

Volevo una buona ma veloce introduzione al mondo del Machine Learning, per cominciare ad applicarlo al mio lavoro quotidiano. Credo che questo libro sia perfetto per questo scopo. È veramente sintetico ma accurato. Vengono presentate principalmente le tecniche "classiche" di Machine Learning, quelle più importanti e utilizzate, insieme a una serie di "best practices" di riconosciuto successo. Gli algoritmi più moderni, che vengono sviluppati di anno in anno, non sono presenti, ma troverete una buona collezione di algoritmi fondamentali, che vi permetterà anche di comprendere gli sviluppi più recenti di questo settore. Scrivo questa relazione circa un anno dopo l'acquisto del libro. Sto utilizzando il Machine Learning abbastanza spesso nel mio lavoro (anche se non mi ritengo un professionista), e utilizzo ancora questo libro come riferimento, per rinfrescare una formula o un concetto.