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

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems

• Explore various explainability methods for designing robust and scalable explainable ML systems
• Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems
• Design user-centric explainable ML systems using guidelines provided for industrial applications