Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Table of Contents

1. Foundational Concepts of Explainability Techniques
2. Model Explainability Methods
3. Data-Centric Approaches
4. LIME for Model Interpretability
5. Practical Exposure to Using LIME in ML
6. Model Interpretability Using SHAP
7. Practical Exposure to Using SHAP in ML
8. Human-Friendly Explanations with TCAV
9. Other Popular XAI Frameworks
10. XAI Industry Best Practices
11. End User-Centered Artificial Intelligence