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
This 3‑day intensive program bridges materials science, sustainability, and artificial intelligence. Participants will extract polymer data from research sources, engineer features, and build predictive models for mechanical strength, thermal behavior, and biodegradability. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 3‑day intensive program bridges materials science, sustainability, and artificial intelligence. Participants will extract polymer data from research sources, engineer features, and build predictive models for mechanical strength, thermal behavior, and biodegradability.
📋 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
• A demonstrable AI Enablement project for your research or industry portfolio.
• 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 – Research Foundations & Data Preparation
Explore bio‑sourced polymers and circular‑economy principles • Extract and clean research‑paper data for ML • Engineer features for polymer performance prediction • Hands‑on: Build a polymer dataset and conduct exploratory analysis in Colab
Day 2 – Predictive Machine Learning for Polymer Properties
Implement a full ML workflow for materials science • Train regression models (Linear, Random Forest, Gradient Boosting) • Predict mechanical, thermal, and biodegradability properties • Evaluate models with MAE, RMSE, R² and interpret feature importance
Day 3 – Circular Economy Applications & Decision Models
Integrate predictive ML into circular‑economy decision frameworks • Conduct multi‑objective analysis of performance vs sustainability • Rank polymers for packaging, biomedical, and infrastructure use cases • Develop AI‑assisted recommendation systems for research/industry
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | Plotly |
Programme Faculty & Mentors
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