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
Real-World Applications Apply ML for Epidemiological Forecasting and Vaccine Logistics Optimization skills directly to academic research, thesis work, and publications. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Real-World Applications
Apply ML for Epidemiological Forecasting and Vaccine Logistics Optimization skills directly to academic research, thesis work, and publications
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A demonstrable biotechnology 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
The Data You Actually Get
• Case, hospitalisation and death series and their differing reliability
• Reporting delays, backfill and weekday effects that mislead naive models
• Nowcasting the present before attempting to forecast the future
Compartmental Models
• SIR and SEIR structure, R0 and the effective reproduction number
• Parameter estimation and identifiability problems with limited data
• What a mechanistic model gives that a pure ML model cannot: interpretable scenarios
Machine Learning Forecasts
• Time series methods, gradient boosting and sequence models on epidemic data
• Feature construction from mobility, weather and search data, with their instability
• Hybrid and ensemble forecasts, which consistently beat single models
Scoring a Forecast Honestly
• Probabilistic forecasts, quantiles and the weighted interval score
• Backtesting with data as it appeared at the time, not as later revised
• Communicating uncertainty to decision makers without collapsing to a point
From Forecast to Delivery
• Cold chain constraints, wastage and the shelf life of a thawed vial
• Facility location, allocation and routing as optimisation problems
• Equity constraints in allocation and last-mile reality in rural distribution
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | XGBoost |
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