# 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.