See also:


  • MLAPP (Kevin Murphy), Machine Learning - A Probablistic Perspective, is more comprehensive, insightful and interesting, and contains more "real" examples/problems. However, the presents are kinda out of order, which can be difficult to follow for a first book.

  • PRML (Christopher Bishop), Pattern Recognition and Machine Learning, is for the ones are very comfortable with calculus/linear algebra.

  • ESL (Trevor Hastie), Elements of Statistical Learning, 2n ed. Springer 2017.
    (中文翻译《统计学习要素》、R代码实现及其习题解答 via Python数据科学)

  • MLY (Andrew Ng), Machine Learning Yearning (EN full: ch1-13), 《CN: 机器学习秘籍》(CN Web online), (CN Github) and someone's notes: part 1 (bak), part 2 (bak) and part 3.

Good Text Books

  • Machine Learning by Peter Flach (Cambridge University Press 2012) - The Art and Science of Algorithms that Make Sense of Data. [The examples in this book are clear and easy to understand.]

  • Machine Learning by Tom M Mitchell (McGraw-Hill 1997). (Classical, many original terms and ideas.)

  • PRML by Christopher Bishop.

Deep Learning

  • DL With Python, by Francois Chollet (also the author of Keras). Detailed introduction of CNN & RNN on CV & text analysis in Keras.
  • DL for CV with Python [//trilogy], by Adrian Rosebrock.
  • Hands on ML with SL and TF [//genius for clarity], by Aurélien Géron.
  • DL - A Practioner's Approach, [java], by Adam Gibson & Josh Patterson.
  • DL with Python, by Jason Brownlee. [//with code, easy to understand]
  • Practical Python and Open CV, by Adrian Rosebrock. [// fast into CV]
  • DL with TF, by Giancarlo Zaccone, [// DL 101]. ref(cn)