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Model Interpretability (XAI)
Learning Objectives
- •Global vs. Local Interpretability
- •SHAP (SHapley Additive exPlanations)
- •LIME (Local Interpretable Model-agnostic Explanations)
- •Feature Importance (Permutation Importance, Partial Dependence Plots)
- •Fairness and Bias in AI
Weekly Outcome
By the end of this module you will be able to interpret complex machine learning models, ensure fairness and transparency, and apply scalable data engineering techniques for processing and analyzing large datasets.