📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Data-Driven Insights into Anaerobic Microbes AI for Microbial Analysis and Applications, from foundations to a certified capstone project.
Outline
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.
Outline
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.
Outline
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).
Outline
Apply AI models for microbial species identification. • Conduct functional annotation of microbial communities. • Predict metabolic capabilities using PICRUSt and Tax4Fun integrated with machine learning.
Outline
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.
Outline
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.
Earn government-registered certification in Data-Driven Insights into Anaerobic Microbes AI for Microbial Analysis and Applications
e-Certificate and e-Marksheet issued on successful completion.
