📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Introduction to Explainable AI, from foundations to a certified capstone project.
Outline
What is Explainable AI (XAI)? • Why Explainability Matters in AI • Black Box vs Interpretable Models • Applications of Explainable AI
Outline
How AI Models Make Predictions • Concept of Model Outputs and Features • Importance of Transparency • Trust and Reliability in AI Systems
Outline
Feature Importance Concepts • Local vs Global Interpretability • Simple Explanation Methods • Understanding Model Behavior
Outline
Explainability and AI Ethics • Bias, Fairness, and Accountability • Role of XAI in Decision-Making Systems • Limitations of Explainable AI
Outline
Explainable AI in Healthcare, Finance, and Business • Regulations and AI Governance Basics • Career Opportunities in Responsible AI • Mini Learning Activity / Concept-Based Practice
Earn government-registered certification in Introduction to Explainable AI
e-Certificate and e-Marksheet issued on successful completion.
