Thoughtful machine learning with Python : a test-driven approach

Originally Published in Sebastopol, CA. by O'Reilly

Includes index.

Probably approximately correct software -- A quick introduction to machine learning -- K-nearest neighbors -- Naive Bayesian classification -- Decision trees and random forests -- Hidden Markov models -- Support vector machines -- Neural networks -- Clustering -- Improving models and data extraction -- Putting it together: conclusion.

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 Pythons Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If youre 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.