Book description
Learn a simpler and more effective way to analyze data and predict outcomes with Python
Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions.
Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language.
Predict outcomes using linear and ensemble algorithm families
Build predictive models that solve a range of simple and complex problems
Apply core machine learning algorithms using Python
Use sample code directly to build custom solutions
Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.
Table of contents
 Introduction
 Chapter 1 The Two Essential Algorithms for Making Predictions

Chapter 2 Understand the Problem by Understanding the Data
 The Anatomy of a New Problem
 Classification Problems: Detecting Unexploded Mines Using Sonar
 Visualizing Properties of the Rocks versus Mines Data Set
 RealValued Predictions with Factor Variables: How Old Is Your Abalone?
 RealValued Predictions Using RealValued Attributes: Calculate How Your Wine Tastes
 Multiclass Classification Problem: What Type of Glass Is That?
 Summary
 Reference
 Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data
 Chapter 4 Penalized Linear Regression
 Chapter 5 Building Predictive Models Using Penalized Linear Methods
 Chapter 6 Ensemble Methods

Chapter 7 Building Ensemble Models with Python
 Solving Regression Problems with Python Ensemble Packages
 Coding Bagging to Predict Wine Taste
 Incorporating NonNumeric Attributes in Python Ensemble Models
 Solving Binary Classification Problems with Python Ensemble Methods
 Solving Multiclass Classification Problems with Python Ensemble Methods
 Comparing Algorithms
 Summary
 References
 Title page
 Copyright
 Dedication
 About the Author
 About the Technical Editor
 Credits
 Acknowledgments
 EULA
Product information
 Title: Machine Learning in Python: Essential Techniques for Predictive Analysis
 Author(s):
 Release date: April 2015
 Publisher(s): Wiley
 ISBN: 9781118961742
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