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
Designed for researchers, professionals, and learners, this course focuses on measuring air quality parameters, identifying sensor drift, and detecting environmental anomalies using data‑driven methods. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Designed for researchers, professionals, and learners, this course focuses on measuring air quality parameters, identifying sensor drift, and detecting environmental anomalies using data‑driven methods.
📋 Course Objectives
1. Put AI in Industry & Manufacturing techniques to work on real datasets and case studies.
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 in Industry & Manufacturing
• R&D engineers and working professionals applying AI in Industry & Manufacturing in industry
• Academics and educators building research or teaching capacity in AI in Industry & Manufacturing
🚀 Key Learning Outcomes
• A demonstrable AI in Industry & Manufacturing 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 | MEASURE – High‑Fidelity Data Acquisition & Preprocessing
Review low‑cost sensor literature and address hardware constraints • Integrate sparse reference stations with dense IoT sensor arrays • Engineer advanced temporal features such as sinusoidal seasonality • Clean noisy readings, handle missing values and calibrate inconsistencies
Day 2 | DRIFT – Concept Drift & Sensor Recalibration
Define concept drift in environmental monitoring and its impact • Implement statistical tests and adaptive algorithms for drift detection • Compare global calibration models with dynamic importance weighting • Design remote recalibration workflows for long‑term IoT deployments
Day 3 | DETECT – Deep Learning for Pollution Forecasting & Event Detection
Deploy GRU and Temporal Fusion Transformer models for 24‑hour AQI forecasts • Apply tree‑based ensembles and autoencoders for unsupervised anomaly detection • Structure experiments, baselines, visualizations, and metrics for peer‑review quality • Translate model outputs into actionable alerts and decision‑support insights
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 | XGBoost |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | ADWIN |
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