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 intensive, hands‑on course explores the integration of Artificial Intelligence (AI) and Lifecycle Assessment (LCA) to help professionals and researchers assess and optimize the environmental impacts of products, processes, and systems. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This intensive, hands‑on course explores the integration of Artificial Intelligence (AI) and Lifecycle Assessment (LCA) to help professionals and researchers assess and optimize the environmental impacts of products, processes, and systems.
📋 Course Objectives
1. Apply Artificial Intelligence 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 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
• Tangible, reproducible Artificial Intelligence 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 – Introduction to LCA and AI Integration
Understand the importance of LCA in global sustainability • Explore key LCA stages and their application across industries • Set up LCA framework and calculate carbon footprints using OpenLCA • Preprocess environmental data and engineer features in Google Colab
Day 2 – Machine Learning Models for Environmental Impact
Introduce regression, classification, and clustering models for LCA • Build a regression model to predict carbon emissions with Scikit‑learn • Apply K‑means clustering to categorize products by impact • Validate and evaluate models using global data standards
Day 3 – Optimizing LCA with AI & Real‑Time Data
Implement genetic algorithms for lifecycle optimization • Integrate IoT streams for real‑time decision making • Apply Multi‑Criteria Decision Analysis for sustainable interventions • Explore future trends: digital twins, circular economy, smart cities
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | OpenLCA |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | ThingSpeak |
| Covered Tool / Platform | DEAP |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | pandas |
| Covered Tool / Platform | NumPy |
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