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.

- Preface
- 1. Test-Driven Machine Learning
- 2. A Quick Introduction to Machine Learning
- 3. K-Nearest Neighbors Classification
- 4. Naive Bayesian Classification
- 5. Hidden Markov Models
- 6. Support Vector Machines
- 7. Neural Networks
- 8. Clustering
- 9. Kernel Ridge Regression
- 10. Improving Models and Data Extraction
- 11. Putting It All Together
- Index