Top positive review
This is the easy book from Hastie, et al. on Statistical Learning (Machine Learning)
Reviewed in the United States on December 16, 2017
In 2009, Stanford Statistics professors Hastie/Tibshirani/Friedman wrote 'The Elements of Statistical Learning', a book that demands a Master's or Doctoral level knowledge of Mathematical Statistics. Years ago, as a part of earning my MS Mathematics, I passed a doctoral-level qualifying examination in Mathematical Statistics. But that was years ago and I needed a friendly refresher before reading 'Elements', which is gathering dust on my shelf.
Well, I'm lucky (and probably so are you) because in 2013 Stanford Statistics professors James/Witten/Hastie/Tibshirani wrote this simpler 'An Introduction to Statistical Learning' that requires only a Bachelor's degree in Mathematics or Statistics. If you have that math grounding, then this is a wonderful book to start your Statistical Learning. The book offers a clear application of Mathematical Statistics and the programming language R to Statistical Learning. At the end of each chapter, the authors provide 10-15 questions to test whether you've digested the material.
Only a few times have I needed to review my Hogg/Craig 'Introduction to Mathematical Statistics'. If you want an excellent book on Mathematical Statistics to prepare you for both 'Introduction to Statistical Learning' and 'The Elements of Statistical Learning', buy the 7th edition of 'Introduction to Mathematical Statistics' by Hogg/McKean/Craig, which is typically used for a year-long (2 semesters) class for 1st or 2nd year graduate students in Mathematics or Statistics. In fact, you could simply bone up on Hogg/McKean/Craig, skip 'Introduction to Statistical Learning', and go straight to the more challenging 'Elements of Statistical Learning'. I wanted to digest some Statistical Learning asap and probably so will you. Enjoy.