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Unlock the power of explainable AI (XAI) and gain insights into how machine learning models make decisions! In this course, you'll explore key techniques for interpreting models, from simple linear regression to complex neural networks. You'll learn how to analyze feature importance, visualize decision-making processes, and build more transparent AI systems.
We’ll cover fundamental XAI methods, including linear model coefficients, tree-based feature importance, permutation importance, partial dependence (PDP), and individual conditional expectation (ICE) plots. You'll also dive into SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to better understand model predictions at both global and individual levels.
We’ll cover fundamental XAI methods, including linear model coefficients, tree-based feature importance, permutation importance, partial dependence (PDP), and individual conditional expectation (ICE) plots. You'll also dive into SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to better understand model predictions at both global and individual levels.
