Gain the confidence you need to apply machinelearning in your daily work. With this practical guide, author Matthew Kirkshows you how to integrate and test machine learning algorithms in your code,without the academic subtext.
Featuring graphs and highlighted code examplesthroughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn,and SciPy data science libraries. If you’re a software engineer or businessanalyst 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
