Of course there are also many other introductory books about Machine Learning, in particular:
Joel Grus, Data Science from Scratch (O’Reilly). This book presents the fundamentals of Machine Learning, and implements some of the main algorithms in pure Python (from scratch, as the name suggests).
Stephen Marsland, Machine Learning: An Algorithmic Perspective (Chapman and Hall). This book is a great introduction to Machine Learning, covering a wide range of topics in depth, with code examples in Python (also from scratch, but using NumPy).
Sebastian Raschka, Python Machine Learning (Packt Publishing). Also a great introduction to Machine Learning, this book leverages Python open source libraries (Pylearn 2 and Theano).
François Chollet, Deep Learning with Python (Manning). A very practical book that covers a large range of topics in a clear and concise way, as you might expect from the author of the excellent Keras library. It favors code examples over mathematical theory.
Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, Learning from Data (AMLBook). A rather theoretical approach to ML, this book provides deep insights, in particular on the bias/variance tradeoff
Char problems - The semantics of comparing character columns of different types can lead to some confusion, so before I get into the main body of this note here’s a little...
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