Was advised to read this by a Data Scientist friend. Found it to be incredibly helpful.Also I find that it has just enough math balanced against just enough textual explanation...something that is rare in tech books.

This is a great book for someone with a decent exposure to statistics and math. If you do not have a good math background and fundamentals in Statistics it may be difficult for you to follow 100% but you will still get to learn so much about Statistical Learning. I really like the following in this book:1. This book is focused on the statistical learning part with concepts applications and R code2. Chapters are very well organized with increasing order of difficulty3. Its almost like reading a story. Each figure and equation are weaved into the story4. R examples were a breeze. I had no knowledge of R language but I did not have any problemsI also enjoy rereading this book every time. :-)

This is the first review I ve ever left for a product but for this book I had to. I needed to prepare for a data scientist interview/take home challenge and scoured the internet for source material in regression classification and clustering. I had accumulated a bunch of disjoint references when a friend of mine pointed this book out to me. It immediately became my Bible and everything else was secondary. It was clear concise to the point written in easy to understand language while astoundingly managing to cover a wide array of topics. Best of all - it even had R code at the end of each chapter so I didn t have to bang my head into a wall trying to figure out how to apply the methods.Even though I had the book in PDF form I bought in hardcover because it deserved a place on my bookshelf. I ma recommending this book to everyone I know as the Bible! and will move on to the next level book: elements of statistical learning soon.

A more readable introduction compared to Elements . Comparable to Mitchell s Machine Learning only more up to date and includes hands-on labs (using R... well better than nothing... had they used something like numpy/python 5-stars!). Good companion to Elements .

The book is very informative and easy to understand. It explains the subject matter by showing experiments in R code. The mathematical equations and theory is little-bit low here (you can find it in the Elementary of Statistical Learning). However the book is very useful for machine learning beginners and cracking interviews.