Amazon.com: Data Mining: (Morgan Kaufmann Series in Data Management Systems) eBook: Ian H. Witten, Eibe Frank, Mark A. Hall: Kindle Store

There exists a couple of classics of Machine learning, with various strengths and weaknesses. I'd say this is the most practical of the three books. The other two I mentioned are oriented towards theoretical underpinnings, and cataloging the rich zoology of machine learning techniques. This one tells you how to get stuff done. It even has practical advice on things you really need an expert opinion on: for example, when using data folding techniques for cross validation ... what is a good number of folds to use? This book will tell you.