Comparing two textbooks for machine learning

Question

I am a Ph.D student in Electrical Engineering. I am going to study the field of machine learning and I found some textbooks to study this field.

1) Probabilistic Graphical Models: Principles and Techniques by Koller

2) Bayesian Reasoning and Machine Learning by Barber

3) Machine Learning: A Probabilistic Perspective by Murphy

I know statistics a little bit because I studied Bayesian learning for my master degree in EE and I took some math courses such as stochastic processes and probability. But, if a book is too comprehensive and succinct, I have no ability to following the book. I would like to study machine learning in detail in terms of statistical learning. In this case, which book is good for me? Thanks.

Answer 1

I recommend Introduction to Statistical Learning. I think it hits the sweet spot between theory and practice.

Answer 2

I highly recommend Kevin Murphy's book. It covers all major areas of machine learning with sufficient theory and algorithm implementations available at: https://github.com/probml/pmtk3

In addition, I recommend experimenting with scikit-learn library: http://scikit-learn.org/stable/ the user guide covers many important algorithms in machine learning and it's written in Python.