Learn Explainable AI

Overview

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.

Syllabus

• Introduction to Explainable AI: Learn how to use explainable AI techniques, including linear model coefficients, tree-based feature importance, permutation importance, and PDP/ICE plots.
Lesson: Introduction to Explainable AI
Project: Explainable AI in Employee Attrition Prediction
Quiz: Introduction to XAI
• Introduction to SHAP: Learn how to use SHAP to explain ML and AI models.
Lesson: Introduction to SHAP
Project: Explaining Breast Cancer Diagnosis Predictions with SHAP
Quiz: Intro to SHAP quiz
• Introduction to LIME: Learn how to use LIME to explain ML and AI models.
Lesson: Introduction to LIME
Project: Explaining Breast Cancer Diagnosis Predictions with LIME
Quiz: Intro to LIME quiz
Informational: Explainable AI Next Steps

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Subjects

Computer Science