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Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann
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Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) Paperback - 2016 - 4th Edition

by Frank, Eibe

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Morgan Kaufmann, 2016-12-01. 4. paperback. Good. 19.1x3.8x23.5. Buy with confidence. Excellent Customer Service & Return policy.
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  • Title Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)
  • Author Frank, Eibe
  • Binding Paperback
  • Edition number 4th
  • Edition 4
  • Condition Used - Good
  • Pages 654
  • Volumes 1
  • Language ENG
  • Publisher Morgan Kaufmann
  • Publication date 2016-12-01
  • Bookseller's Inventory # ANAIS-0128042915
  • ISBN 9780128042915 / 0128042915
  • Weight 2.84 lbs (1.29 kg)
  • Dimensions 9.25 x 7.5 x 1.1 in (23.50 x 19.05 x 2.79 cm)
  • Size 19.1x3.8x23.5
  • Category Computers - Data Base Management
  • Library of Congress subjects Data mining
  • Library of Congress Catalogue Number 2016948470
  • Quantity available 1

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From the publisher

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.

Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.

Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.

It contains

  • Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
  • Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
  • Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.

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