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
The integration of machine learning and data science in materials research, equipping participants with methodologies to analyze complex datasets, predict material properties, and drive innovation in next‑generation materials development. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course explores the integration of machine learning and data science in materials research, equipping participants with methodologies to analyze complex datasets, predict material properties, and drive innovation in next‑generation materials development.
📋 Course Objectives
1. Apply AI Enablement methods to authentic research and industry problems.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 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
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• 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
Foundations & Data Understanding
Understand materials data and load a real‑world dataset in Google Colab • Clean and preprocess data using Pandas • Convert composition into machine‑learnable features
Machine Learning for Property Prediction
Build regression models (Linear Regression, Random Forest) for property prediction • Engineer features tailored to materials datasets • Evaluate models using R², MAE and visual diagnostics
Optimization, Interpretation & Research Output
Improve model performance with tuning and validation techniques • Analyze feature importance and interpret results • Generate publication‑ready plots and export a complete case study
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Excel |
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