Programme Parameters
Graduate / Intermediate
4 Weeks (40 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
ML Models for Air Quality Prediction and Health Impact pairs pollution forecasting with its human consequence. You learn to build models that predict pollutants like PM2.5 and NO2 from sensor and satellite data, and — the distinctive focus — to link that pollution to health impact: exposure modelling, health-outcome association, and translating predictions into public-health risk. The course connects environmental prediction to the health effects that make it matter. You finish able to model air quality and estimate its health impact. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers machine-learning models for air-quality prediction and health impact — forecasting pollution and, distinctively, quantifying its effect on public health.
📋 Course Objectives
1. Predict pollutants from sensor and satellite data.
2. Model population exposure to pollution.
3. Associate air quality with health outcomes.
4. Translate predictions into health risk.
5. Support public-health decisions.
👥 Who Should Enroll?
• Public-health and environmental data scientists
• Epidemiology and exposure researchers
• Air-quality and policy analysts
• Students of environmental health
🚀 Key Learning Outcomes
• The ability to model air quality and health impact.
• A health-outcome-focused perspective.
• An environmental-health project.
• 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
AI Fundamentals, Mathematics, and ML Models for Air Quality Prediction and Health Impact
Develop a comprehensive understanding of linear algebra and calculus for machine learning applications • Analyze the fundamentals of probability and statistics for data-driven decision making • Design basic neural network architectures using Python and popular deep learning libraries
Data Engineering, Preprocessing, and Feature Pipelines
Configure data pipelines using Apache Beam and Google Cloud Dataflow for efficient data processing • Implement data preprocessing techniques such as handling missing values and data normalization • Evaluate the effectiveness of feature engineering methods for improving model performance
Model Architecture, Algorithm Design, and ML Models for Air Quality Prediction and Health Impact
Design and implement convolutional neural networks for image-based air quality prediction • Develop and train recurrent neural networks for time-series forecasting of health impacts • Optimize model architectures using hyperparameter tuning and cross-validation techniques
Training, Hyperparameter Optimization, and Evaluation
Train machine learning models using popular frameworks such as TensorFlow and PyTorch • Implement hyperparameter optimization techniques such as grid search and random search • Evaluate model performance using metrics such as mean squared error and R-squared
Deployment, MLOps, and Production Workflows
Deploy machine learning models using cloud-based platforms such as AWS SageMaker and Google Cloud AI Platform • Implement continuous integration and continuous deployment pipelines using Jenkins and Docker • Configure model monitoring and logging using tools such as Prometheus and Grafana
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of machine learning models on society and environment • Develop strategies for mitigating bias in machine learning models using techniques such as data augmentation • Implement fairness metrics and evaluation frameworks for ensuring responsible AI practices
Industry Integration, Business Applications, and Case Studies
Develop business cases for implementing machine learning models in industry settings • Analyze real-world case studies of successful machine learning deployments in air quality prediction and health impact • Design and propose machine learning-based solutions for industry partners and stakeholders
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Apache Beam |
| Covered Tool / Platform | Google Cloud Dataflow |
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