If you use Python, even as a beginner, this book, subtitled A Guide for Data Scientists will teach you practical ways to build your own machine learning solutions. Authors Andreas Müller and Sarah Guido say that with all the data available today, machine learning applications are limited only by your imagination, and set out to show what is possible. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library.

<ASIN:1449369413>

The book focuses 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 more from this book.

Author: Andreas C. Müller and Sarah Guido
Publisher: O'Reilly
Date: October 2016
Pages: 394
ISBN: 978-1449369415
Print: 1449369413
Kindle: B01M0LNE8C
Audience: Python programmers
Level: Intermediate
Category: Artificial Intelligence

Topics covered include:

• Fundamental concepts and applications of machine learning
• Advantages and shortcomings of widely used machine learning algorithms
• How to represent data processed by machine learning, including which data aspects to focus on
• Advanced methods for model evaluation and parameter tuning
• The concept of pipelines for chaining models and encapsulating your workflow
• Methods for working with text data, including text-specific processing techniques
• Suggestions for improving your machine learning and data science skills

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