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 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 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 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.