Section 1 – Conceptual Exposure

This section will give you the necessary conceptual exposure to explainability techniques for machine learning (ML) models with practical examples. You will learn about the foundational concepts, different dimensions of explainability, various model explainability methods, and even data-centric approaches to explainability. Knowledge of the foundational concepts will help you understand the guidelines for designing robust explainable ML systems like those covered in this book.

This section comprises the following chapters:

• Chapter 1, Foundational Concepts of Explainability Techniques
• Chapter 2, Model Explainability Methods
• Chapter 3, Data-Centric Approaches