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Machine Learning Refined: Foundations, Algorithms, and Applications

Machine Learning Refined: Foundations, Algorithms, and Applications

Machine Learning Refined: Foundations, Algorithms, and Applications
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Machine Learning Refined: Foundations, Algorithms, and Applications Hardback - 2020

by Jeremy Watt

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Details

  • Title Machine Learning Refined: Foundations, Algorithms, and Applications
  • Author Jeremy Watt
  • Binding Hardback
  • Condition New
  • Pages 594
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2020-01-09
  • Features Bibliography, Index
  • Bookseller's Inventory # SKU110447
  • ISBN 9781108480727 / 1108480721
  • Weight 2.9 lbs (1.32 kg)
  • Dimensions 9.8 x 7 x 1.1 in (24.89 x 17.78 x 2.79 cm)
  • Category Technology & Industrial Arts
  • Library of Congress subjects Machine learning
  • Library of Congress Catalogue Number 2019052924
  • Dewey Decimal Code 006.31
  • Quantity available 1

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Reader reviews for Machine Learning Refined: Foundations, Algorithms, and Applications

From the publisher

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
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