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Machine Learning in Action

Peter Harrington
  • April 2012
  • ISBN 9781617290183
  • 384 pages
  • printed in black & white

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An approachable and useful book.

Alexandre Alves, Oracle Corporation
Look inside

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

about the book

A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

what's inside

  • A no-nonsense introduction
  • Examples showing common ML tasks
  • Everyday data analysis
  • Implementing classic algorithms like Apriori and Adaboos

about the reader

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

about the author

Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.

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Smart, engaging applications of core concepts.

Patrick Toohey, Mettler-Toledo Hi-Speed

Great examples! Teach a computer to learn anything!

John Griffin, Coauthor of <i>Hibernate Search in Action</i>

An approachable taxonomy skillfully created from the diversity of ML algorithms.

Stephen McKamey, Isomer Innovations
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