AI · Moderate · Video-based learning

ML Models for Air Quality Prediction and Health Impact

A Hands-On Workshop on Forecasting Air Pollution and Assessing Its Health Consequences Using Machine Learning

★ 4.1 (31 ratings)Access 24/7 · 365 daysVideo lectures of 3+ hrsCertificate
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About This Course

“ML Models for Air Quality Prediction and Health Impact” is a 3-days practical workshop that teaches how to build, evaluate, and deploy machine learning models to forecast air pollution and assess exposure-related health outcomes such as respiratory and cardiovascular risks.

Participants will explore data preprocessing, feature engineering, time series modeling, deep learning approaches, and health risk estimation frameworks. Case studies from urban monitoring systems, satellite data sources, and health impact databases will be integrated for hands-on experience.

Aim

To train participants in designing and applying machine learning (ML) models for predicting air quality levels and quantifying their health impact, supporting policy, urban planning, and early intervention strategies.

Course Objectives

  • Enable data-driven policymaking for air quality management
  • Equip professionals to link AI outputs to actionable health insights
  • Support interdisciplinary collaboration between data science, environment, and health
  • Drive awareness and advocacy through predictive intelligence tools
  • Build capacity for integrating real-time environmental sensing and analytics
Next: Syllabus →

Syllabus

 

Module 1: Foundations of Air Quality & Data Acquisition

  • Air Quality Indices (AQI, PM2.5, PM10, NO₂, O₃) and Health Guidelines
  • Data Sources: OpenAQ, EPA, CPCB, WHO, Sentinel-5P
  • Exploratory Data Analysis and Spatial-Temporal Trends

Module 2: Machine Learning for Air Quality Forecasting

  • Time Series Forecasting: Linear Regression, ARIMA, XGBoost, LSTM
  • Spatiotemporal ML Models and Feature Engineering
  • Model Evaluation (RMSE, MAE, R², Confusion Matrix)
  • Tools: Python, Scikit-learn, Prophet, TensorFlow

Module 3: Health Impact Estimation & Deployment

  • Quantifying Short- and Long-Term Health Risks
  • Exposure Mapping and Population Vulnerability
  • Linking ML Outputs to Public Health Metrics (DALY, Mortality)
  • Visualization & Dashboarding (Streamlit, Power BI, Dash)

Who Should Enrol?

  • Environmental engineers and urban data analysts
  • Health data scientists and epidemiologists
  • AI/ML professionals working in climate and environment
  • Government air quality officers and public health planners
  • Graduate students and researchers in environmental health or data science

Course Outcomes

  • Build predictive ML models for air quality and exposure forecasting
  • Quantify and visualize public health impacts from pollution
  • Analyze and interpret air quality datasets using AI tools
  • Deploy dashboards and data products for public awareness
  • Earn a certificate in “ML for Air Quality and Health Impact Prediction”
Next: Fees →

Course fee

Learners in India

₹2,499

Learners outside India

$50

  • Video lectures
  • Study material
  • Certificate after completion
  • 365-day access

Enrol in the course

The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.

Next: FAQ →

Frequently asked questions

How is this course taught?

Through recorded video lectures from the original live workshop, with the study material. Watch at your own pace, any time.

How long do I have access?

365 days from your registration, on your NanoSchool dashboard.

Do I get a certificate?

Yes, an e-certificate after completion, with a QR code anyone can verify and an "Add to LinkedIn" button.

How can I pay?

By card, UPI or PayPal on the payment page; international bank transfer is also available there.

What is the refund policy?

See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy

Need Help?

We’re here for you!


(+91) 120-4781-217
WhatsApp +91 99581 61117

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