Data Mining: Practical Machine Learning Tools and Techniques

I've seen the Norvig book recommended by others on this site for ML, but I don't understand why: the Norvig book is an AI book, not an ML book. It only has a few chapters on learning, and IIRC much of that is on reinforcement learning. Obviously the Norvig book is very well-written and is good background for learning about ML, but I don't think it is a sufficient ML book as such. As far as ML goes, I found the "Programming Collective Intelligence" book to very readable and practical, but very light on the theoretical foundations (which is intentional, of course). I've got a copy of the Witten ML book ("Data Mining: Practical Machine Learning Tools and Techniques"), but to be honest I haven't gotten much from it yet, either: it doesn't seem to discuss SVMs in any detail, nor random forests or neural networks. But I haven't really dug into it yet.

If you want a fairly easy read without too many equations, try: Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) http://www.cs.waikato.ac.nz/~ml/weka/book.html Which goes nicely with the Weka open source ML toolkit http://www.cs.waikato.ac.nz/ml/weka/ (although it is a good read without the toolkit) If you want a bit more math, I really like the recent (Oct 2007) book: Pattern Recognition and Machine Learning by Christopher M. Bishop http://www.amazon.com/Pattern-Recognition-Learning-Informati... (http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738) It is nicely self contained, going through all the stats you'll need.