Machine learning is the process of teaching machines to remember data patterns, use them to predict future outcomes, and offer choices that would appeal to individuals based on past preferences. Learning to build machine learning alogirthms within a controlled test framework will speed up your time to deliver, quantify quality expectations, and enabled rapid iteration and collaboration. This book will show you how to quantifiably test machine learning algorithms.
- Get started w/ an introduction to test-driven development & familiarize yourself with how to apply these concepts to machine learning
- Build & test a neural network deterministically, and learn to look for niche cases that cause odd model behavior
- Learn to use the multi-armed bandit algorithm to make optimal choices
- Generate complex & simple random data to create a wide variety of test cases
- Develop models iteratively, even when using a third-party library
- Quantify model quality to enable collaboration & rapid iteration
- Adopt simpler approaches to common machine learning algorithms
- Take behavior-driven development principles to articulate test intent
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