### Review: Machine Learning for Hackers

#### by darribas

Machine Learning for Hackers by Drew Conway

My rating: 3 of 5 stars

The book is a hands-on review of the main topics of machine learning (ML). All along the chapters, the authors gently walk the reader through examples with real world data, paying particular attention to the more practical aspects of the implementation of the analysis and showing a lot of R code, which becomes a de-facto replacement for the equations one would expect to see in a purely stats book.

This approach has several nice advantages, of which I’ll highlight three: first, the use of real world data and stories shows the readily applicability of ML and introduces a way of understanding the world we live in through numbers; second, the non-technical but rather practical approach makes the book much more readable than a purely math-type textbook; and third, you get to learn some cool R tricks and libraries, which is never to be underestimated.

If any, the only downside I would point out is that it falls a bit short in introducing the statistical methods and background behind the algorithms. As a quantitative social scientist, I am used to more depth in explaining the “science” behind practice, and the book tends to skip those explanations as soon as they get a little heavy. I guesss for that there are other references and, as I said, it makes the book much more readable from cover to cover. At any rate, great entry port to the field if you like code more than equations.

After reading this review, I’m left with a few questions. How is ML different from programming generally? What makes ML, ML? In what problem domains is it used, and what types of problems is it good for solving? Thanks.