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Machine Learning: a Concise Introduction by Steven W. Knox – eBook

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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

Description

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 Learninga 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 Learninga Concise Introduction by Knox in PDF. No access codes included.