before i say anything directly about the book, i'd like to point out that for simple systems (like these), the most challenging parts are overwhelmingly data collection, normalization / featurization, and model testing, rather than actually creating or using models. while there are rare cases where a simple solution (hey, let's throw naive bayes at it) will give you a good answer, these are almost always because someone did a very good job collecting and sanitizing the input. furthermore, stuff like the twitter movie sentiment analysis - while great in theory - rarely ends up doing what you expect in practice. product recommendation and collaborative filtering are proven to work very well in practice, but sentiment systems are a totally different monster.<p>onto the book - it looks promising for an intro to recommendation systems. no opinion about classification yet. doesn't appear to have anything on graphs or network effects which is somewhat disappointing. that being said i need to review bayesian stuff / teach myself some of the harder stuff and it will be nice to have a practical walkthrough.<p>that being said no one should be implementing these themselves (except the dumb stuff like distance metrics).. it's useful to learn but scikit-learn is amazing when it comes to fancy algorithms.