Lilly Tech Systems
Learn Explainable AI
Master the techniques that make machine learning models transparent and interpretable — from SHAP values and LIME to attention maps and regulatory compliance.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why explainability matters, black-box vs. interpretable models, and the regulatory landscape.
2. SHAP Values
Shapley Additive Explanations, TreeExplainer, DeepExplainer, and KernelExplainer.
3. LIME
Local Interpretable Model-agnostic Explanations for tabular, text, and image data.
4. Attention Maps
Visualize what neural networks focus on using Grad-CAM, attention weights, and saliency maps.
5. Model-Specific Methods
Feature importance, partial dependence plots, counterfactual explanations, and ICE plots.
6. Best Practices
Regulatory requirements, choosing methods, communicating explanations, and production deployment.
What You'll Learn
By the end of this course, you'll be able to:
Explain Any Model
Apply SHAP and LIME to generate human-readable explanations for any machine learning model.
Visualize Decisions
Create attention maps, saliency visualizations, and feature importance plots for deep learning models.
Meet Compliance
Understand GDPR, EU AI Act, and industry regulations requiring model explainability.
Build Trust
Communicate model decisions effectively to stakeholders, regulators, and end users.