Today, Machine Learning (ML) is being applied to a growing variety of problems in a bewildering variety of domains. When doing machine learning, a fundamental challenge is connecting the abstract mathematics of a particular Machine Learning technique to a concrete, real-world problem. This book tackles this challenge through model-based Machine Learning. Model-based Machine Learning is an approach which focuses on understanding the assumptions encoded in a ML system, and their corresponding impact on the behaviour of the system. The practice of model-based ML involves separating out these assumptions being made about a real-world situation from the detailed mathematics of the algorithms needed to do the ML. This approach makes it easier to both understand the behaviour of a ML system and to communicate this to others.