Book

Applied Machine Learning Explainability Techniques

Explainable AI is a set of techniques used to demystify the outcome of machine learning and AI models, making algorithms more trustworthy and transparent by justifying model predictions. This book helps you to learn how to design explainable ML systems for industrial applications considering the best practices.

Offered byPackt Logo

Difficulty Level
Intermediate
Completion Time
10h12m approx.
Language
English
Certification
Not available

About Course

Book Content

chapters 10h12m total length

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

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