📚 Syllabus & Course Curriculum
Antimicrobial Resistance (AMR)
Module-by-module breakdown of Predictive Epidemiology: ML Frameworks for Global AMR Tracking and Surveillance, from foundations to a certified capstone project.
Outline
Explore the global burden and drivers of antimicrobial resistance • Understand infectious‑disease surveillance principles • Identify key AMR data sources and reporting frameworks
Outline
Clean and preprocess multi‑dimensional AMR datasets • Visualise pathogen‑antibiotic resistance patterns • Generate temporal trend analyses for surveillance intelligence
Outline
Frame AMR problems as classification and risk‑scoring tasks • Engineer features from pathogen, geography, patient and genomic variables • Build and tune models such as Logistic Regression, Random Forest, Gradient Boosting and XGBoost
Outline
Assess performance using accuracy, precision, recall, F1‑score, ROC‑AUC and confusion matrix • Interpret model outputs for public‑health decision making • Address bias, uncertainty and data‑quality issues in healthcare surveillance
Outline
Integrate clinical, epidemiological and genomic AMR signals • Navigate platforms such as CARD, ResFinder, NCBI Pathogen Detection, Microreact and Nextstrain • Track resistance genes, pathogen lineages and mutation dynamics
Outline
Design real‑time surveillance dashboards with Streamlit and Plotly • Visualise geographic hotspots and temporal trends • Implement basic early‑warning alerts for emerging resistance threats
Earn government-registered certification in Predictive Epidemiology: ML Frameworks for Global AMR Tracking and Surveillance
e-Certificate and e-Marksheet issued on successful completion.
