Introduction to Machine Learning with Python: A Guide for Data ScientistsMachine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machinelearning application with Python and the scikitlearn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn:

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This book is very practical  exactly what I was looking for. I use it quite a bit for reference.
Contents
Section 24  
Section 25  
Section 26  
Section 27  
Section 28  
Section 29  
Section 30  
Section 31  
Section 15  
Section 16  
Section 17  
Section 18  
Section 19  
Section 20  
Section 21  
Section 22  
Section 23  
Section 32  
Section 33  
Section 34  
Section 35  
Section 36  
Section 37  
Section 38  
Section 39  
Section 40  
Section 41  
Section 42  
Section 43  
Section 44  
Section 45  