Machine Learning with Spark and Python: Essential Techniques for Predictive Analytics

✍ Scribed by Michael Bowles

• Publisher: Wiley
• Year: 2019
• Tongue: English
• Leaves: 356
• Edition: 2
• Category: Library

✦ Synopsis

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark--a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.

Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.

✦ Table of Contents

Cover
Machine Learning with
Spark and Python:
Essential Techniques for
Predictive Analytics
Copyright
Dedication
About the Author
About the Technical Editor
Acknowledgments
Contents at a Glance
Contents
Introduction
1 The Two Essential Algorithms for
Making Predictions
2 Understand the Problem by
Understanding the Data
3 Predictive Model Building:
Balancing Performance,
Complexity, and Big Data
4 Penalized Linear Regression
5 Building Predictive Models Using
Penalized Linear Methods
6 Ensemble Methods
7 Building Ensemble Models
with Python
Index