pGain 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. p 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 ul liReference real-world examples to test each algorithm through engaging, hands-on exercises liApply test-driven development TDD to write and run tests before you start coding liExplore techniques for improving your machine-learning models with data extraction and feature development liWatch out for the risks of machine learning, such as underfitting or overfitting data liWork with K-Nearest Neighbors, neural networks, clustering, and other algorithms ul