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Machine Learning (The Art and Science of Algorithms that Make Sense of Data)

Machine Learning (The Art and Science of Algorithms that Make Sense of Data)

Machine Learning (The Art and Science of Algorithms that Make Sense of Data)
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Machine Learning (The Art and Science of Algorithms that Make Sense of Data) Paperback - 2012

by Flach, Peter

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Cambridge University Press, 2012. Book. Fine. Soft cover. Excellent copy with no significant flaws and no markings to text. ***Shipped within 24 hours from the beautiful Baltimore inner harbor area. First class service; accurate descriptions. Most items packed in boxes, not envelopes.***.
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Details

  • Title Machine Learning (The Art and Science of Algorithms that Make Sense of Data)
  • Author Flach, Peter
  • Binding Paperback
  • Edition 1st edition
  • Condition Used - Fine
  • Pages 409
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press, Cambridge
  • Publication date 2012
  • Features Bibliography, Index
  • Bookseller's Inventory # 228253
  • ISBN 9781107422223 / 1107422221
  • Weight 2.25 lbs (1.02 kg)
  • Dimensions 9.6 x 7.3 x 0.9 in (24.38 x 18.54 x 2.29 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Computers - General Information
  • Library of Congress subjects Machine learning, Apprentissage automatique - Manuels scolaires
  • Library of Congress Catalogue Number 2012289353
  • Dewey Decimal Code 004.67
  • Bookseller catalogues Computers

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From the publisher

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

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