Introduction to machine learning with Python : a guide for data scientists
Machine 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 machine-learning application with Python and the scikit-learn 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. -- Provided by publisher
eBook, English, 2016
First edition View all formats and editions
O'Reilly Media, Sebastopol, CA, 2016
1 online resource (xii, 376 pages :) illustrations.
9781449369415, 1449369413
1005225247
Introduction
Supervised learning
Unsupervised learning and preprocessing
Representing data and engineering features
Model evaluation and improvement
Algorithm chains and pipelines
Working with text data
Wrapping up
Includes index
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