17 December 2015

Test-Driven Machine Learning

Test-Driven Machine Learning

The book “Test-Driven Machine Learning” by Justin Bozonier, published by Packt Publishing, is in print now. I was a technical reviewer of this book, and in this post you will learn some details about it. The book is available on the publisher’s website as well as on Safari Books Library.

If you are a software developer with experience in TDD, and you’re interested in Data Science and Machine Learning, this is a very good book for you. But also, if you’re a data scientist who’s interested in learning how to apply the best practices of software development to Data Science work, this is an excellent resource.

The book uses python, so some familiarity with the language will be useful to get the most out of the book.


Table of Contents

  1. Introducing Test-Driven Machine Learning
  2. Perceptively Testing a Perceptron
  3. Exploring the Unknown with Multi-armed Bandits
  4. Predicting Values with Regression
  5. Making Decisions Black and White with Logistic Regression
  6. You’re So Naïve, Bayes
  7. Optimizing by Choosing a New Algorithm
  8. Exploring scikit-learn Test First
  9. Bringing It All Together

What You Will Learn:

  • Get started with an introduction to test-driven development and familiarize yourself with how to apply these concepts to machine learning
  • Build and test a neural network deterministically, and learn to look for niche cases that cause odd model behaviour
  • Learn to use the multi-armed bandit algorithm to make optimal choices in the face of an enormous amount of uncertainty
  • Generate complex and simple random data to create a wide variety of test cases that can be codified into tests
  • Develop models iteratively, even when using a third-party library
  • Quantify model quality to enable collaboration and rapid iteration
  • Adopt simpler approaches to common machine learning algorithms
  • Take behaviour-driven development principles to articulate test intent

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