A relevant text in 2010, outdated and superseded in 2020.

A testament to how quickly this area has developed. There are now many many additional computational options than those covered here. ( )

This book does a good job making an introduction of machine learning technologies to the average programmer. This is its main merit. Having said that, the introduction to the subjects is very simplified, so you'll need further reference to actually implement anything at all. It's full of Python code snippets only work to make the subject appear accessible to the programmer, and look like waffle to me. Mathematical formulas in which code snippets are based can only be found (without further explanation) on an annex. Algorithm alternatives or optimizations for real-life operations are not described. But in the end, this is the objective of this book. My main criticism would be that the book doesn't fully succeed at explaining exactly what you should use each technique for and which are their pros and cons.

To sum up, read this book if you are a programmer, you have no previous knowledge on machine learning topics and you want a math-free introduction on the subject. ( )

This is a terrific resource for anyone interested in modern methods of data aggregation and statistics because it provides actual working code that you can copy and modify. You can get all the source files online from the author's web site. ( )

Amazing piece of work. Makes much sense of the power of software and algorithms in predicting customer preferences and behavior based on aggregate data. Very well done with concise examples in Python programming language. Entire book is quite focused on modern web sites and web technology. ( )