Test-driven development approach enables reliable and maintainable machine learning code
Hands-on exercises provide practical experience with real-world machine learning scenarios
Features code examples using NumPy, Pandas, Scikit-learn, and SciPy for comprehensive learning
Explains techniques for improving machine learning models, including data extraction and feature development
Covers common machine learning risks such as underfitting and overfitting for informed model evaluation
Summarized by Shop
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, Panda