Table of Contents

1. Interpreting Interpretable Machine Learning
2. Model Types and Interpretability Techniques
3. Interpretability Taxonomy and Techniques
4. Feature Effects Analysis with Plots
5. Post-Hoc Methods
6. Anchors and Counterfactuals
7. Interpretability in Neural Networks
8. Explainable Neural Networks
9. Explainability in Transformers and Large Language Models
10. Explainability and Responsible AI
      Index