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
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.
- 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)
- Pattern Classification by Richard O. Duda, David G. Stork, Peter E.Hart, 2015.
- A Course in Machine Learning by Hal Daumé III, 2017.