I really enjoyed working through this book. It is definitely mathematical and algorithmic in its treatment of the topics covered: Statistical "Learning", Monte Carlo Methods, Unsupervised "Learning", Regression Models, Regularization and Kernel Methods, Classification, Decision Trees and Ensemble Methods, Deep "Learning" ( Neural Networks )  As a statistician and data scientist, I find the convoluted "machine learning" terminology very affected and as helpful as trying to design a plane based on the flying dynamics of a bird.  Sometimes the rigourous mathematical notation is difficult to follow on the initial reading.  Most algorithms are implemented in Python but the code should be more clearly documented so that one can follow the implementation of the solution without getting stuck on coding issues as the book encourages the reader to focus on the algorithm and to not treat the python code as a black box.  The list of references is quite complete and it was interesting to check my library to see just how many of the references I already had.  If this book is to be used for training analysts then there should be more practical examples and code solutions available