What this book covers

Chapter 1, Introducing to Test-Driven Machine Learning, explains what Test-Driven Development is, what it looks like, and how it is done in practice.

Chapter 2, Perceptively Testing a Perceptron, develops a perceptron from scratch and defines its behavior even though it behaves non-deterministically.

Chapter 3, Exploring the Unknown with Multi-armed Bandits, introduces multi-armed bandit problems, testing different algorithms, and iterating on their performance.

Chapter 4, Predicting Values with Regression, uses statsmodels to implement regression and report on key performance metrics. We will also explore tuning the model.

Chapter 5, Making Decisions Black and White with Logistic Regression, continues exploring regression as well as quantifying quality of this different type of it. We will use statsmodels again to create our regression models.

Chapter 6, You're So Na?ve, Bayes, helps us develop a Gaussian Na?ve Bayes algorithm from scratch using test-driven development.

Chapter 7, Optimizing by Choosing a New Algorithm, continues the work from Chapter 6, You're So Na?ve, Bayes, and attempts to improve upon it using a new algorithm: Random Forests.

Chapter 8, Exploring scikit-learn Test First, teaches how to teach oneself. You probably already have a lot of experience of this. This chapter will build upon this by teaching you to use the test framework to document sci-kit learn.

Chapter 9, Bringing it all Together, takes a business problem that requires a couple of different algorithms. Again, we will develop everything we need from scratch and mix our code with third party libraries, completely test-driven.