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
Explore cutting‑edge techniques in battery performance optimization and degradation analysis through machine learning. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Explore cutting‑edge techniques in battery performance optimization and degradation analysis through machine learning.
📋 Course Objectives
1. Get comfortable working with machine learning.
2. Translate AI Enablement theory into practical, reproducible analysis.
3. 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
• Data and computational scientists moving into machine learning
🚀 Key Learning Outcomes
• Confidence to reason about machine learning in real projects.
• 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
Day 1 – Battery Fundamentals and Degradation Data
Understand battery types, working principles, and lifecycle behavior • Analyze capacity fade, cycle aging, and failure mechanisms • Import, clean, and visualize cycle data in Google Colab
Day 2 – Machine Learning for Battery Lifetime Prediction
Explore AI’s role in health monitoring and lifetime estimation • Engineer predictive features and build regression models • Develop a simple ML model to forecast capacity fade in Google Colab
Day 3 – Interpretation, Optimization, and Research Insights
Interpret feature importance and degradation drivers • Tune, validate, and compare model performance • Generate research‑ready analysis and visualizations
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | Seaborn |
Frequently Asked Questions
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