Thoughtful Machine Learning with Python: A Test-Driven Approach

Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.

Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you:

• Reference real-world examples to test each algorithm through engaging, hands-on exercises
• Apply test-driven development (TDD) to write and run tests before you start coding
• Explore techniques for improving your machine-learning models with data extraction and feature development
• Watch out for the risks of machine learning, such as underfitting or overfitting data
• Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms

Table of Contents

Chapter 1. Probably Approximately Correct Software
Chapter 2. A Quick Introduction to Machine Learning
Chapter 3. K-Nearest Neighbors
Chapter 4. Naive Bayesian Classification
Chapter 5. Decision Trees and Random Forests
Chapter 6. Hidden Markov Models
Chapter 7. Support Vector Machines
Chapter 8. Neural Networks
Chapter 9. Clustering
Chapter 10. Improving Models and Data Extraction
Chapter 11. Putting It Together: Conclusion