Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Interpretable ML for Scientific Discovery is a 3‑day online hands‑on course designed to help participants build, evaluate, and interpret machine learning models for scientific data analysis. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Interpretable ML for Scientific Discovery is a 3‑day online hands‑on course designed to help participants build, evaluate, and interpret machine learning models for scientific data analysis.
📋 Course Objectives
1. Apply AI Enablement methods to authentic research and industry problems.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement 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
Day 1 – Machine Learning Foundations for Scientific Data Analysis
Explore the role of ML in scientific discovery • Prepare scientific datasets and perform train‑test splits • Build baseline models with Scikit‑learn and evaluate reliability
Day 2 – Feature Importance and Visual Model Interpretation
Apply built‑in and permutation feature importance techniques • Create diagnostics visualizations with Yellowbrick • Interpret influential variables in scientific datasets
Day 3 – SHAP Explainability and Scientific Insight Extraction
Generate global and local explanations with SHAP • Visualize SHAP summary plots and individual prediction impacts • Translate model explanations into concise scientific insights
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | SHAP |
| Covered Tool / Platform | Yellowbrick |
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