Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.
Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.
By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.

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Spis treści

Applied Machine Learning Explainability Techniques. Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more eBook -- spis treści

• 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