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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 wasn t an error message. But just because it runs doesn t mean it s 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 what s 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.
Summary
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.
