The Hundred-Page Machine Learning Book Paperback – 1 January 2019

Author has done a wonderful job explaining a vast subject in just 100+ pages.

The best book to learn ML concepts in a easy way. I loved this book & I also highly recommend this book

The book does not contains table of contentsIs my copy faulted ?!The book content is awesome but.

If anyone is starting their career in domain of machine learning then this book is your bible or Geeta for marching ahead. Every subject is explained in layman term. Must buy

Suited perfectly for the purpose mentioned by author. Introduces the vocabulary for people new to machine learning and is a good refresher and placeholder for people who already are working in the area. I would imagine the book will be hard to read unless the reader is very comfortable with mathematics at undergraduate level.

Very useful, concise and practical from a well known practitioner. Am sure every aspiring and experienced Data Scientist will lap it up.

If you are new to ML most of the thing will go above your head. I recommend go through other resources first related to Maths, intro to Ml. Then go deep into each topic mentioned in this book.

I've gone through different books, papers and courses on ML and Artificial intelligence, and I've found that a majority of them are either overwhelmingly dense, or disappointingly shallow, with very few of them hitting any sort of sweet spot.This book is one of the few exceptions.Despite being short, it manages to cover a lot of ground without sacrificing a fair treatment of the basics. There's an exceptionally good balance between math and concepts in my opinion, and it's all explained in very simple terms, without ever feeling pretentious or cryptic.I think the author did an outstanding job distilling a great deal of useful information into 100-ish pages while avoiding making this a dense read. It's actually the only book I've been able to breeze through while still getting a lot of useful insights in the process.In fact, even though I already had some experience on the field when I read this book, I found that the way some concepts and topics are presented provided me with a new way of approaching them or thinking about them that further deepened my understanding of those topics, and allowed me to explore them in ways I had not done before.I wish this book existed when I started learning ML. It would have made a lot of thing clearer from the start.All in all; This book is a fantastic resource that serves as a perfect introduction to the topic for beginners, and a good "refresher" and a source of invaluable tips and insights for the more experienced ML practitioners.

O conteúdo realmente superou minhas expectativas. Em pouco mais de 100 páginas, o livro consegue trazer os principais conteúdos que qualquer pessoa que trabalha ou deseja trabalhar com Machine Learning deve saber. Apesar da complexidade dos temas, o autor é bastante didático e objetivo. O leitor que não tem conhecimento em disciplinas do ciclo básico da maioria dos cursos de exatas, como Cálculo, Álgebra Linear, Geometria Analítica ou Estatística, pode sentir um pouco de dificuldade para compreender alguns temas, mas isso não deve ser impeditivo para adquirir o livro. Na medida do possível, o autor tenta explicar um pouco desses pré-requisitos em cada assunto. Mas mais importante do que isso, certamente o leitor pode se motivar em aprender sobre essas disciplinas.

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!

Un libro tosto come il ferro, necessario come l’acqua e l’aria. Chiaro, conciso, preciso. Mena durissimo.Io ve l’ho detto.

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.