Test Driven Machine Learning

Test Driven Machine Learning offers a fresh perspective on applying machine learning with test-driven development (TDD). By combining foundational algorithms with test frameworks, this book enables developers to deliver effective machine learning solutions efficiently. You will learn techniques to iteratively build, test, and refine models to create reliable and quantifiable machine learning systems.

What this Book will help me do

• Understand and apply test-driven development principles to machine learning algorithms.
• Build neural networks and regression models with test frameworks for rapid experimentation.
• Use the multi-armed bandit algorithm to navigate uncertain environments with optimal choices.
• Design and execute tests for model quality and performance, leveraging scikit-learn.
• Develop reliable machine learning tools iteratively, enabling faster delivery of results.

Author(s)

Justin Bozonier is a seasoned data professional with extensive experience in machine learning and test-driven development. Through his career, Justin has focused on building scalable and reliable machine learning systems, emphasizing practical techniques and iterative development. This expertise enables him to guide readers in creating robust models efficiently and effectively.

Who is it for?

Test Driven Machine Learning is designed for data scientists, developers, and analysts who already have experience with machine learning and are proficient in Python. It is ideal for professionals seeking methods to implement TDD in their ML workflows to produce reliable, high-quality models efficiently. Readers with a background in predictive modeling and algorithm design will find it particularly insightful.