Drew Conway and John Myles White have a new book out, Machine Learning for Hackers. As the name implies, the emphasis is on exploration rather than mathematical theory. Lots of code, no equations.

If you’re looking for a hands-on introduction to machine learning, maybe as a prelude to or complement to a more theoretical text, you’ll enjoy this book. Even if you’re not all that interested in machine learning, you might enjoy the examples, such as how a computer could find patterns in senatorial voting records and twitter networks. And R users will find examples of using advanced language features to solve practical problems.

As a tip, go to O’Reillys web site directly instead of Amazon. The paper book is slightly more expensive, but they have a “buy 3 pay for two” book deal. Getting the paper book and ebook together is cheaper than at Amazon, and the O*Reilly ebook is DRM-free so you can back it up and read it anywhere, not just with the Kindle app.

Without the underlying theory and algorithms, code monkeys might as well just use a library.

Josh: This book isn’t starting from scratch. They’re using a lot of libraries. But there’s still work to do if you’re going to “just use a library.” You have obtain the data, clean it, put it in the right format, know what library to use and how to use it, then present and interpret your results. And that’s the kind of work the book focuses on.

Drew and John urge readers to read theoretical books such as this one. I hope that future theoretical books will return the favor and recommend that theorists read books like ML for Hackers.

Are you familiar with the book “Programming Collective Intelligence: Building Smart Web 2.0 Applications”? (http://www.amazon.com/Programming-Collective-Intelligence-Applications-ebook/dp/product-description/B0028N4WM4)

It seems it is quite similar to “ML for Hackers”, but this one goes into some details about the algorithms. And all the code is Python.