Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The Introduction to Explainable AI course is a free, beginner-friendly self-paced program designed to help learners understand how artificial intelligence models make decisions and how those decisions can be interpreted and explained. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Introduction to Explainable AI course is a free, beginner-friendly self-paced program designed to help learners understand how artificial intelligence models make decisions and how those decisions can be interpreted and explained.
📋 Course Objectives
1. Apply Artificial Intelligence methods to authentic research and industry problems.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in Artificial Intelligence
• R&D engineers and working professionals applying Artificial Intelligence in industry
• Academics and educators building research or teaching capacity in Artificial Intelligence
🚀 Key Learning Outcomes
• A portfolio-grade Artificial Intelligence deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Introduction to Explainable AI
What is Explainable AI (XAI)? • Why Explainability Matters in AI • Black Box vs Interpretable Models • Applications of Explainable AI
Understanding AI Decisions
How AI Models Make Predictions • Concept of Model Outputs and Features • Importance of Transparency • Trust and Reliability in AI Systems
Basic Explainability Techniques
Feature Importance Concepts • Local vs Global Interpretability • Simple Explanation Methods • Understanding Model Behavior
Responsible and Ethical AI
Explainability and AI Ethics • Bias, Fairness, and Accountability • Role of XAI in Decision-Making Systems • Limitations of Explainable AI
Applications and Future Scope
Explainable AI in Healthcare, Finance, and Business • Regulations and AI Governance Basics • Career Opportunities in Responsible AI • Mini Learning Activity / Concept-Based Practice
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Explainable AI |
| Covered Tool / Platform | Model Interpretability |
| Covered Tool / Platform | Feature Importance |
| Covered Tool / Platform | Transparency |
| Covered Tool / Platform | Responsible AI |
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