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