This is a very compact, densely written volume. It covers all the basics of machine learning: perceptrons, support vector machines, neural networks, decision trees, Bayesian learning, etc. Algorithms are explained, but from a very high level, so this isn't a good reference if you're looking for tutorials or implementation details. However, it's quite handy to have on your shelf for a quick reference.