Top positive review
88 of 91 people found this helpful
on January 30, 2014
Some of the other reviewers seem to be unclear on the concept of "introduction", demanding rigor and depth - and, unfortunately, not suggesting any alternatives. If you want deep understanding of the algorithms, you will need to start with a proper textbook, like "Pattern matching" by Bishop, or "Elements of statistical learning" by Hastie and Tibshirani. If you want a more accessible, high-level presentation with examples that can be reproduced, in R, you want to get "Introduction to statistical learning" by James, Witten, Hastie and Tibshirani. If you want a *really* accessible introduction to the techniques - again, with examples that you can try and build on - well, "Machine learning with R" just might be the best choice today. (On R side; if you have invested in Python, Peter Harrington's "Machine learning in action" and books by Wes McKinney are a good bet). OK, this is not an outstanding book, it is under-edited and plain-looking - unfortunately, Packt follow the no-frills approach of O'Reilly - but it is friendly, reasonably well written, and offers a good deal of content. (Extra brownie points for Chapter 11). Let me put it this way: you want to read "Introduction to statistical learning", but "Machine learning with R" is a good warm-up.