Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Anaerobic microbes are crucial for energy production, waste treatment, and environmental sustainability, driving processes like methanogenesis, fermentation, and biodegradation. Analyzing their complex interactions and metabolic potential demands advanced computational methods. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Anaerobic microbes are crucial for energy production, waste treatment, and environmental sustainability, driving processes like methanogenesis, fermentation, and biodegradation. Analyzing their complex interactions and metabolic potential demands advanced computational methods.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
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 biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• Tangible, reproducible biotechnology 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
Introduction to Anaerobic Microbes and Their Significance
Understand the characteristics of anaerobic microbes and their role in biogeochemical cycles. • Identify key processes: methanogenesis, fermentation, and denitrification. • Explore applications in biogas production, bioremediation, and health.
Data Acquisition and Foundations of AI in Microbial Analysis
Examine sequencing techniques like 16S rRNA and shotgun sequencing. • Access and utilize public repositories for microbiome data. • Perform hands-on analysis of metagenomic datasets using Python and machine learning tools.
Advanced Data Preprocessing and Microbial Diversity
Implement data preprocessing techniques: quality control, filtering, and normalization. • Extract relevant features from microbial genomic and metabolic data. • Calculate and interpret microbial diversity metrics (alpha and beta diversity).
AI Models for Microbial Identification and Functional Annotation
Apply AI models for microbial species identification. • Conduct functional annotation of microbial communities. • Predict metabolic capabilities using PICRUSt and Tax4Fun integrated with machine learning.
Predictive Modeling in Biotechnological Applications
Model microbial performance in biogas production and bioremediation. • Utilize AI for metabolic pathway prediction using deep learning. • Analyze case studies on AI-driven methane production in anaerobic digesters.
Hands-on Predictive Model Building and Evaluation
Build predictive models for anaerobic microbial applications using Python and AI libraries. • Evaluate model performance using accuracy, precision, recall, and F1-score. • Gain practical experience in end-to-end AI-driven microbial analysis.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | XGBoost |
| Covered Tool / Platform | PICRUSt |
| Covered Tool / Platform | Tax4Fun |
Programme Faculty & Mentors
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