Favoured

  • 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.

Good Text Books

  • Peter Flach 2012 - Machine Learning - The Art and Science of Algorithms that Make Sense of Data: examples are clear.

  • Machine Learning by Tom M Mitchell.

  • PRML by Christopher Bishop.

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