Paperback ☆ Machine Learning Epub ✓

Machine Learning Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data Probably the first book you want in academic setting when studying machine learning it s simple yet effective, and contains less mathematical mind twisters andconcepts of machine learning algorithms. This is an introductory book on Machine Learning There is quite a lot of mathematics and statistics in the book, which I like A large number of methods and algorithms are introduced Neural Networks Bayesian Learning Genetic Algorithms Reinforcement LearningThe material covered is very interesting and clearly explained I find the presentation, however, a bit lacking I think it has to do with the chosen fonts and lack of highlighting of important terms Maybe it would have been better to have This is an introductory book on Machine Learning There is quite a lot of mathematics and statistics in the book, which I like A large number of methods and algorithms are introduced Neural Networks Bayesian Learning Genetic Algorithms Reinforcement LearningThe material covered is very interesting and clearly explained I find the presentation, however, a bit lacking I think it has to do with the chosen fonts and lack of highlighting of important terms Maybe it would have been better to have shorter paragraphs too.If you are looking for an introductory book on machine learning right now, I would not recommend this book, because in recent years better books have been written on the subject These are better obviously, because they include coverage ofmodern techniques I give this book 3 out of 5 stars Great intro to ML For someone who doesn t have a formal Comp Sci background, this took a lot out of me I found it helpful to stop after every chapter and listen to arecent lecture to tie loose ends Highly recommend reading this book in conjunction with professor Ng s ML intro course. 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.

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