Introduction - If you have any usage issues, please Google them yourself
The book mainly covers the current variety of the most practical machine learning theory and algorithms, including the concept of learning, decision trees, neural networks, Bayesian learning, instance-based learning, genetic algorithms, rule learning, explanation-based learning and enhance learning