Machine Learning: A Probabilistic Perspective

Awesome Book. This should really be the First book you read on Machine Learning. The Probabilistic Approach and Heavy Math might tire you at first but if you stay on with the materials and after few revisions, all Algorithms that are prominently used in the Industry today start to make sense readily. Understanding the Math and associating the tuning parameters to the Math allows you to better tune your models.

Awesome content

1. It's not for faint hearted people who don't understand the beauty of mathematics. 2. It covers almost all the topics of ML and a good handbook which covers Statistical Learning and Patterns Recognition. 3. Heavy book both in terms of weight and knowledge too. The book is very poetic for ML.

It looks like a good book but it is more of reference book and not a text book. Could be a good one for Phd student.

This book will always be in my shelf. This might not be the most organized book in machine learning but man this book has stuff. Along with convex optimisation and linear algebra this book gave me enough exposure to machine learning topics that I cleared research position in ml at good companies without sweat. This book is everything PRML by bishop is and more but with slightly hard to read organisation.

Very nice coverage of topics. Very nice explanation with detailed formula and fundamentals. Truly bridges the gap between classical statistics and machine learning

This is a very bad textbook. Organization of content is very illogical. There are better books out there.

Don't waste your money on this book. This book is junk. You will never learn anything from this book. Very superficial coverage of the topics and very very confusing. Better to pick up a math book instead.