Machine Learning Books

Machine Learning Books

Machine Learning

An Introduction to Statistical Learning by Daniela Witten

 

  • A good book to start with machine learning
  • The book provides an accessible overview of the field of statistical learning, an essential tool set for making sense of the vast and complex data sets.
  • This book presents some of the most important modeling and prediction techniques, along with relevant applications.

 

The Elements of Statistical Learning

 

  • A good book to follow “An introduction to Statistical Learning” with, or to read at the same time.
  • This book describes the underlying concepts and considerations by which a researcher can judge a learning method.
  • This book is written in an intuitive fashion, emphasizing concepts rather than mathematical details.
  • While some mathematical details are needed, this book emphasize the methods and their conceptual underpinnings rather than their theoretical properties.

 

Pattern recognition and machine learning by Christopher M. Bishop

 

  • Good book for machine learning
  • The first few chapters give a really nice intuition on some of the key areas of stats while feeling very 'readable'.
  • This book also assumes a reasonable degree of mathematical maturity, although the table of contents may make the content appear more simple than it is, for example by listing a review of probability distributions including the Gaussian as Chapter 2.

 

Machine learning: a probabilistic perspective by Kevin Murphy

 

  • This book is a must read for Machine Learning
  • You need to have basic knowledge of elementary statistics and probability (from class XII books)

 

Statistical Learning Theory by Vladimir N. Vapnik

 

  • This is a well-respected, in-depth book that you might be interested in.
  • It's mainly about Learning Theory: e.g. Error bounds on ERM and SRM, and later part's delve into Support Vector Machines.

 

Deep Learning with Python by Francois Chollet

 

  • This books gives is a nice tour of the Keras library with lots of code snippets and with detailed explanations of the typical problems solved using deep learning.

 

Information Theory, Inference, and Learning Algorithms by David J. C. Mackay

 

  • This book remains the best source entry point for Bayesian learning theory.
  • Everybody who wants to really understand ml theory should at least flip through it.

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