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1 Burnside Artificial Intelligence- General

Thoughtful Machine Learning: A Test-Driven Approach

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Thoughtful Machine Learning: A Test-Driven Approach Cover

 

Synopses & Reviews

Publisher Comments:

Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks.

Machine-learning algorithms often have tests baked in, but they cant account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If youre familiar with Ruby 2.1, youre ready to start.

  • Apply TDD to write and run tests before you start coding
  • Learn the best uses and tradeoffs of eight machine learning algorithms
  • Use real-world examples to test each algorithm through engaging, hands-on exercises
  • Understand the similarities between TDD and the scientific method for validating solutions
  • Be aware of the risks of machine learning, such as underfitting and overfitting data
  • Explore techniques for improving your machine-learning models or data extraction

Synopsis:

Apply a fully test-driven approach to machine-learning algorithms, and save yourself the pain of missing mistakes in your analyses. Most data scientists have run an analysis and simply accepted any answer that wasnt an error message. But just because it runs doesnt mean its correct. Missed mistakes can ruin research and harm reputations.

All of that can be avoided by writing tests and building checks into your work. This book shows you how to write tests and build checks into their work. Using the Ruby programming language, software developers, business analysts, and CTOs will learn how to test machine-learning code, and understand whats happening "behind the scenes."

  • Code machine-learning algorithms in a test-driven way
  • Gain confidence to utilize machine learning
  • Dissect algorithms from the granular pieces using unit tests
  • Get real-world examples of utilizing machine learning code

About the Author

Matthew Kirk holds a B.S. in Economics and a B.S. in Applied and Computational Mathematical Sciences with a concentration in Quantitative Economics from the University of Washington. He started Modulus 7, a data science and Ruby development consulting firm, in early 2012. Matthew has spoken around the world about using machine learning and data science with Ruby.

Product Details

ISBN:
9781449374068
Author:
Kirk, Matthew
Publisher:
O'Reilly Media
Subject:
Software Engineering-Algorithms
Subject:
algorithms;code;data analysis;data science;data sets;developers;machine learning;real world;ruby;scientists;testing
Edition Description:
Print PDF
Publication Date:
20141031
Binding:
TRADE PAPER
Language:
English
Pages:
234
Dimensions:
9.19 x 7 in

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computer Languages » Ruby
Computers and Internet » Software Engineering » Algorithms
Computers and Internet » Software Engineering » Programming and Languages

Thoughtful Machine Learning: A Test-Driven Approach Used Trade Paper
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Product details 234 pages O'Reilly Media - English 9781449374068 Reviews:
"Synopsis" by ,

Apply a fully test-driven approach to machine-learning algorithms, and save yourself the pain of missing mistakes in your analyses. Most data scientists have run an analysis and simply accepted any answer that wasnt an error message. But just because it runs doesnt mean its correct. Missed mistakes can ruin research and harm reputations.

All of that can be avoided by writing tests and building checks into your work. This book shows you how to write tests and build checks into their work. Using the Ruby programming language, software developers, business analysts, and CTOs will learn how to test machine-learning code, and understand whats happening "behind the scenes."

  • Code machine-learning algorithms in a test-driven way
  • Gain confidence to utilize machine learning
  • Dissect algorithms from the granular pieces using unit tests
  • Get real-world examples of utilizing machine learning code

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