Michael Bowles

Machine Learning with Spark and Python (eBook, ePUB)

Essential Techniques for Predictive Analytics

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 368
• Erscheinungstermin: 15. Oktober 2019
• Englisch
• ISBN-13: 9781119561958
• Artikelnr.: 58045294

Autorenporträt

MICHAEL BOWLES teaches machine learning at UC Berkeley, University of New Haven and Hacker Dojo in Silicon Valley, consults on machine learning projects, and is involved in a number of startups in such areas as semi conductor inspection, drug design and optimization and trading in the financial markets. Following an assistant professorship at MIT, Michael went on to found and run two Silicon Valley startups, both of which went public. His courses are always popular and receive great feedback from participants.

Inhaltsangabe

Introduction xxi

Chapter 1 The Two Essential Algorithms for Making Predictions 1

Chapter 2 Understand the Problem by Understanding the Data 23

Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data 77

Chapter 4 Penalized Linear Regression 129

Chapter 5 Building Predictive Models Using Penalized Linear Methods 169

Chapter 6 Ensemble Methods 221

Chapter 7 Building Ensemble Models with Python 265

Index 329