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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. 1. Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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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 1
  • Condition Used - Very good
  • Pages 409
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press, Cambridge
  • Publication date 2012-09-20
  • Features Bibliography, Index
  • Bookseller's Inventory # 1107422221-11-1
  • 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
  • Quantity available 2

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Reader reviews for Machine Learning: The Art and Science of Algorithms that Make Sense of Data

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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