Machine Learning Fundamentals New Edition

This is not an introductory book. This is the book you should read after an introductory book like An Introduction to Statistical Learning by Gareth James, et al. The chapters are short but focused and well organized. The author brings out the core of the material in the most succinct and clearest way. I particularly liked the sections on Gaussian mixture models and processes. I know the HMM's are not as popular as they used to be but the section on HMM is excellent.

This is an amazing book with very easy-to-follow mathematical derivations and with a concise style of material presentation. There are lecture slides for educators on the web as well.

I have read quite a few books on ML including those by Murphy, Diesenroth, Theodoridis. This book serves as an Intermediate to Advanced transition in ML. It is in some ways better than "Intro to Statistical Learning" due to more organized approach to certain topics like Bias Variance tradeoff. Better color images to explain the concepts. These are $90 well spent. The Author is an expert and if it was my call I would definitely enroll in his graduate class given the chance to do so.