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OverviewLearn 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 can't 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 you're familiar with Ruby 2.1, you're ready to start.Apply TDD to write and run tests before you start codingLearn the best uses and tradeoffs of eight machine learning algorithmsUse real-world examples to test each algorithm through engaging, hands-on exercisesUnderstand the similarities between TDD and the scientific method for validating solutionsBe aware of the risks of machine learning, such as underfitting and overfitting dataExplore techniques for improving your machine-learning models or data extraction Full Product DetailsAuthor: Matthew KirkPublisher: O'Reilly Media Imprint: O'Reilly Media ISBN: 9781322665269ISBN 10: 1322665265 Pages: 235 Publication Date: 01 January 2014 Audience: General/trade , General Format: Electronic book text Publisher's Status: Active Availability: In stock We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately. Table of ContentsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |