Data Mining Practical Machine Learning Tools and Techniques

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  • Engels
  • Paperback
  • 9780128042915
  • 20 december 2016
  • 621 pagina's
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Ian H. Witten

"Ian H. Witten is a computer scientist at the University of Waikato, New Zealand. He is a Chartered Engineer with the Institute of Electrical Engineers in London who graduated from the University of Cambridge with a BA and MA (First Class Honours) in mathematics in 1969 and an M.Sc. in mathematics and computer science from the University of Calgary, where he was a Commonwealth Scholar, in 1970. He received his Ph.D., Learning to Control in 1976 from the University of Essex, England (Electrical Engineering Science). Witten is a co-creator of the sequitur algorithm and original creator of the WEKA software package for data mining.

(Bron: Wikipedia. Beschikbaar onder de licentie Creative Commons Naamsvermelding/Gelijk delen.)"

Samenvatting

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.
  • Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
  • Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
  • Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
  • Includes open-access online courses that introduce practical applications of the material in the book

Productspecificaties

Inhoud

Taal
en
Bindwijze
Paperback
Oorspronkelijke releasedatum
20 december 2016
Aantal pagina's
621
Illustraties
Nee

Betrokkenen

Hoofdauteur
Ian H. Witten
Tweede Auteur
Eibe Frank
Co Auteur
Christopher J. Pal
Hoofduitgeverij
Morgan Kaufmann

Overige kenmerken

Editie
4
Extra groot lettertype
Nee
Product breedte
191 mm
Product hoogte
52 mm
Product lengte
235 mm
Studieboek
Ja
Verpakking breedte
189 mm
Verpakking hoogte
30 mm
Verpakking lengte
234 mm
Verpakkingsgewicht
1320 g

EAN

EAN
9780128042915

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