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Machine Learning: a Concise Introduction – eBook

eBook details

  • Author: Steven W. Knox
  • File Size: 18 MB
  • Format: PDF
  • Length: 352 pages
  • Series: Wiley Series in Probability and Statistics
  • Publisher: Wiley
  • Publication Date: March 15, 2018
  • Language: English
  • ASIN: B07BHYKL4V
  • ISBN-10: 1119439191
  • ISBN-13: 9781119439196

$63.99 $10.00

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Author(s)

Steven W. Knox

An introduction to machine learning that includes fundamental methods, techniques, and applications.

Machine Learning: a Concise Introduction (PDF) offers a comprehensive introduction to the approaches, core concepts, and applications of machine learning. The author — an expert in the field — presents terminology, fundamental ideas, and techniques for solving applied problems in classification, clustering, regression, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more successful and flexible applications. Machine Learning: a Concise Introduction also includes methods for risk estimation, optimization, and model selection— essential elements of most applied projects.

This important resource:

  • Contains useful information for effectively communicating with clients
  • Presents R source code which shows how to apply and interpret many of the techniques covered
  • Includes many thoughtful exercises as an integral part of the textbook, with an appendix of selected solutions
  • Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods

machine learning prose award

A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning.

NOTE: This source only includes the ebook Machine Learning: a Concise Introduction by Knox in PDF. No access codes included.

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