I'm Matthew Kirk, a software engineer based out of Seattle, WA. I am also the author of Thoughtful Machine Learning, where I present doing test-driven software development with data in Ruby, and Thoughtful Machine Learning with Python. I have been building web apps since 2009 and have always been "the data guy," thanks to my applied math degree and my previous life as a financial quant. In my career, I have been fortunate enough to speak around the world about software and work on exciting projects with later-stage startups. I have built social media sentiment engines, diamond recommendation tools, and e-commerce search algorithms...and always got frustrated with how my data projects never seemed to follow best development practices.

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:

For a practical guide to integrate and test machine learning algorithms, check out Matthew Kirk's Thoughtful Machine Learning with Python. We truly live in an exceptional point in history. The ability to ask your TV to queue up the next episode of Game of Thrones, or to have it "learn" what you like to watch, and then suggest new options, is staggering. For years, companies have latched on to the trend of utilizing machine learning algorithms for great effect, whether it's trading on Wall Street or recognizing cat images. But there's a catch: there are many problems associated with shipping machine learning code.

Learn how to apply test-driven development (TDD) to machine-learning algorithms--and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can't account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you're familiar with Ruby 2.1, you're ready to start.