Machine learning with Spark and Python : essential techniques for predictive analytics
Book

Machine learning with Spark and Python : essential techniques for predictive analytics

By Bowles, Michael, author.

Published [2020] by Wiley, Indianapolis, IN

ISBN 9781119561934

Bib Id 2311022

Edition Second edition.

Description xxvii, 340 pages : illustrations ; 24 cm

More Details

Leader
03623cam a2200433Ii 4500
ISBN
9781119561934 (paperback) $50.00
1119561930
Call #
006.31 B
Title
Machine learning with Spark and Python : essential techniques for predictive analytics
Edition
Second edition.
Publication Information
[2020] by Wiley, Indianapolis, IN :
Description
xxvii, 340 pages : illustrations ; 24 cm
Bibliography
Includes bibliographical references and index.
Contents
The two essential algorithms for making predictions -- Understand the problem by understanding the data -- Predictive model building : balancing performance, complexity, and big data -- Penalized linear regression -- Building predictive models using penalized linear methods -- Ensemble methods -- Building ensemble models with Python.
Summary
"Machine learning focuses on predition-- using what you know to predict what you would like to know based on historical relationships between the two. At its core, it's a mathematical/algorithm-based technology that, until recently, required a deep understanding of math and statistical concepts, and fluency in R and other specialized languages. "Machine learning with Spark and Python" simplifies machine learning for a broader audience and wider application by focusing on two algorithm families that effectively predict outcomes, and by showing you how to apply them using the popular and accessible Python programming language. This edition shows how pyspark extends these two algorithms to extremely large data sets requiring multiple distributed processors. The same basic concepts apply. Author Michael Bowles draws from years of machine learning expertise to walk you through the design, construction, and implementation of your own machine learning solutions. The algorithms are explained in simple terms with no complex math, and sample code is provided to help you get started right away. You'll delve deep into the mechanisms behind the constructs, and learn how to select and apply the algorithm that will best solve the problem at hand, whether simple or complex. Detailed examples illustrate the machinery with specific, hackable code, and descriptive coverage of penalized linear regression and ensemble methods helps you understand the fundamental processes at work in machine learning. The methods are effective and well tested, and the results speak for themselves."--

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