Applied machine learning / David Forsyth.

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Applied machine learning / David Forsyth.

9783030181147 (electronic book)
3030181146 (electronic book)
3030181138
9783030181130

Cham, Switzerland : Springer, [2019]

English

1 online resource

10.1007/978-3-030-18

Q325.5 .F67 2019

006.3/1

Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but arent necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing ones own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career. Broad coverage of the area ensures enough to get the reader started, and to realize that its worth knowing more in-depth knowledge of the topic. Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material.

Includes index.

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Description based on online resource; title from digital title page (viewed on August 23, 2019).

Print version: 9783030181130

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