Understanding Machine Learning

The writing in this book is very cohesive.

I have read many of the main books on machine learning. This is hands down the best. Rather than a laundry list of techniques, the book starts with a concise and clear introduction to statistical machine learning and then consistently connects those concepts to the main ML algorithms. Each chapter is 10 pages or so of crisp math and lean prose. A brief summary at the beginning of each chapter gives a clear sense of what will be accomplished in it, and attention to notation makes sure that mathematics supports understanding rather than getting in the way. This is definitely not a "how to" book, but rather a "what and why" book, focused on understanding principles and connections between them. I read the book cover to cover, and I was left with a sense of machine learning as a coherent discipline, and a solid feel for the main concepts.

I bought it since I wanted to refresh my knowledge on machine learning (I am a CS graduate, took the ML course about 15 years ago...). I finished one third of it by now and enjoy it very much.What I especially like about this book is that it gives a good theoretical background, before jumping into the algorithms.When getting to the algorithms the author show how to use the theoretical tools to analyze them, which is great !Also, the theoretical part was enough for me to further read and understand more recent theoretical ML research papers.That is a great feeling ! I wholeheartedly recommend this great book for graduates.

First, let me just say I regret purchasing the kindle version, as it is difficult to read the math symbols on the kindle, and even somewhat difficult to read them on the kindle for mac app on a big screen. Zoomed in leaves the symbols the same size (it appears as though they're images), with the surrounding text large. Perhaps this is a problem on most math texts, but I was disappointed.I'm enjoying the book. It reads like a textbook that one might find at a university, and has exercises and notes for the order you'd go through it while teaching a class. I find it well-written and for the most part, easy to digest--a bit heavy on the math for what I was looking for, but you can skim over it for the ideas.

Bought the hardcover copy. Reading through a page, one can see the words printed on the other side, as well as the words on the page after, which is unpleasant.On the other hand, the content is well structured and easy to follow.

Paperback book sell only at South Asia edition and shipped to California, USA. Zoom the picture to see edition details on bottom right corner.Is amazon authorized to sell this Edition to USA customers.Do not know any difference in edition content

This is an excellent introduction to machine learning which fills an important gap in the literatureby introducing students to formal broad conceptual frameworks for understanding, comparing, analyzing,and designing large classes of popular machine learning algorithms. These frameworks are explicitly presentedas mathematical theorems but the authors are careful about explaining the underlying assumptions of key theorems andinterpreting the conclusions of such theorems. Richard M. Golden.

Ideal book for learning theory of machine learning, in order to get a deeper understanding of practical algorithms. Clear mathematical presentation, covers every subject that I come over in articles and want to understand better, good exercises.