BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

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
Stock photo: cover may vary

Machine Learning: The Art and Science of Algorithms that Make Sense of Data Hardback - 2012

by Flach, Peter

Add to wish list
  • Used
  • Good
  • Hardback
Used - Good

Description

hardcover. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
Ask the seller a question Add to wish list
A$236.64
Free Delivery within USA
Standard delivery: 7 to 14 days
More delivery options
Dropship order
Ships from Bonita (California, United States)
The BIBLIO Guarantee

Details

  • Title Machine Learning: The Art and Science of Algorithms that Make Sense of Data
  • Author Flach, Peter
  • Binding Hardback
  • Condition Used - Good
  • Pages 410
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2012-09-20
  • Features Bibliography, Index
  • Bookseller's Inventory # 1107096391.G
  • ISBN 9781107096394 / 1107096391
  • Weight 2.3 lbs (1.04 kg)
  • Dimensions 9.8 x 7.3 x 1 in (24.89 x 18.54 x 2.54 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 1

About Bonita California, United States

Biblio member since 2020

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Bonita

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

Write a review for this book

There are no reviews for this book yet. Be the first to write a review!

Your review will be anonymous unless you login or create an account first.

Overall rating
Remember: You’re rating the book as a work, not the seller or the condition of the specific copy you purchased!

Important Terms and Guidelines

  • Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
  • Reviews must be at least 140 characters in length.
  • Please do not reveal critical plot elements.
  • This is not a help line. Contact customer support if you need help.
Your review must not include:
  • Obscenities, discriminatory language, or other insulting language not suitable for public domain
  • Advertisements, “spam” content, or references to other products, offers or websites.
  • Email addresses, URLs, phone numbers, physical addresses or other contact information.
  • Overly critical comments about other reviews or reviewers
  • Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
  • Availability, price, or alternative ordering/shipping information

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.

Please wait a moment while we load the requested page.

tracking-