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.
Apply TDD to write and run tests before you start coding
Learn the best uses and tradeoffs of eight machine learning algorithms
Use real-world examples to test each algorithm through engaging, hands-on exercises
Understand the similarities between TDD and the scientific method for validating solutions
Be aware of the risks of machine learning, such as underfitting and overfitting data
Explore techniques for improving your machine-learning models or data extraction
Matthew Kirk holds a B.S. in Economics and a B.S. in Applied and Computational Mathematical Sciences with a concentration in Quantitative Economics from the University of Washington. He started Modulus 7, a data science and Ruby development consulting firm, in early 2012. Matthew has spoken around the world about using machine learning and data science with Ruby.
Comments about oreilly Thoughtful Machine Learning:
I get it that this book is in Early Release form, but I am rather shocked that O'Reilly would let this get out the door in this form under any any designation. ("Early Outline" edition?) My hope is that this book will be substantially improved, and eventually I'll find that it has important content that I need to learn. The first couple of chapters? Confused presentation, incorrect similies, and dubious references. "Early Release" should only be an option for proven authors, who can be trusted to provide close-to-publishible quality in early drafts. This is far from publishible. I can't believe O'Reilly would have ever let this into the wild 10 years ago.
Maybe in 9 months I'll take a look at the latest revision, in hopes that it will by then be worth attempting later chapters.
Bottom Line No, I would not recommend this to a friend