This is an good book on machine learning for students at the advancedundergraduate or Masters level, or for self study, particularly ifsome of the background math (eigenvectors, probability theory, etc)is not already second nature.Although I am now familiar with much of the math in this area and considermyself to have intermediate knowledge of machine learning, I can still recallmy first attempts to learn some mathematical topics. At that time my approachwas to implement the ideas as computer programs and plot the results. Thisbook takes exactly that approach, with each topic being presented bothmathematically and in Python code using the new Numpy and Scipy libraries.Numpy resembles Matlab and is sufficiently high level that the book codeexamples read like pseudocode.(Another thing I recall when I was first learning was the mistakenbelief that books are free from mistakes. I've since learned toexpect that every first edition is going to have some, and doubly sofor books with math and code examples. However the fact that many of the examplesin this book produce plots is reassuring.)As mentioned I have only intermediate knowledge of machine learning, andhave no experience with some techniques. I learned regression treesand ensemble learning from this book -- and then implemented an ensembletree classifier that has been quite successful at our company.Some other strong books are the two Bishop books (Neural Networks for PatternRecognition; Pattern Recognition and Machine Learning),Friedman/Hastie/Tibshirani (Elements of Statistical Learning) andDuda/Hart/Stork (Pattern Classification). Of these, I think the first Bishopbook is the only other text suitable for a beginner, but it doesn't have theexplanation-by-programming approach and is also now a bit dated (Marslandincludes modern topics such as manifold learning, ensemble learning, and a bitof graphical models). Friedman et al. is a good collection of algorithms,including ones that are not presented in Marsland; it is a bit dry however.The new Bishop is probably the deepest and best current text, but it isprobably most suited for PhD students. Duda et al would be a good book at aMasters level though its coverage of modern techniques is more limited. Ofcourse these are just my impressions. Machine learning is a broad subject andanyone using these algorithms will eventually want to refer to several of these books.For example, the first Bishop covers the normalized flavor of radial basisfunctions (a favorite technique for me), and each of the mentioned books hastheir own strengths.