Artificial Intelligence in Combating Bacterial Resistance
Antibiotic bacterial resistance is a growing global challenge, threatening public health and increasing medical costs worldwide. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools to predict, monitor, and mitigate bacterial resistance, transforming clinical data into practical, real-world solutions.
This 100% online course is hands-on, comprehensive, and results-oriented, preparing healthcare professionals, students, and researchers to apply AI to real bacterial resistance data, with the potential for international career opportunities.
Course Content
Module 1 – Introduction and Context
- Global crisis of bacterial resistance: mortality, costs, and forecasts
- Why AI is revolutionary in combating AMR
- Success cases of AI in healthcare and pharmacology
- Basic concepts: resistant bacteria, resistance genes, and clinical risk factors
Module 2 – Python and Data Science Fundamentals
- Python for biomedical applications: variables, functions, control structures
- Data manipulation with Pandas and NumPy
- Data visualization: Matplotlib, Seaborn, interactive dashboards
- ML fundamentals: Random Forest, XGBoost, evaluation metrics
Module 3 – Hands-On Projects with Real Data
Project 1 – Demographic and Microbiological Data
- Datasets: ARMD, ResistanceMap
- Objective: explore patient data, preprocess, and train ML models to predict resistance
- Application: interactive risk classification app in Python
Project 2 – Resistance Gene Identification
- Datasets: CARD, MEGARes
- Objective: analyze resistance genes, feature selection, ML model training for classification
- Application: app for laboratory data interpretation and clinical decision support
Project 3 – Resistance Surveillance Dashboards
- Objective: build interactive dashboards to visualize resistance patterns by region, hospital, or population
- Techniques: data aggregation, interactive charts, ML model integration
Module 4 – Ready-to-Use Clinical Tools
- WHONET and AMRFinderPlus (NCBI)
- Laboratory data analysis and integration with custom ML models
- Dashboard creation for clinical decision support
Module 5 – Application and Communication
- Presenting ML results in healthcare settings
- Visualization of resistance patterns
- Data-driven mitigation strategies
- Best practices for communicating results to healthcare managers and professionals
Module 6 – International Career Preparation (Premium)
- Create competitive CVs and cover letters for international AI health positions
- Optimize professional profiles (LinkedIn, GitHub)
- Prepare for technical and behavioral interviews
- Strategies to find opportunities in the UK, Europe, Canada, and Australia
- Informative guidance on visa types and documentation
- Strategic support in applying to up to 5 positions
- Recommendation letter signed by Dr. Alexandre Cobre
Note: Module 6 offers consultative support only. Employment or visa approval is not guaranteed.
Instructor
The course is led by Prof. Dr. Alexandre de Fátima Cobre, a Mozambican scientist affiliated with The University of Manchester (UK), specialist in AI applied to drug discovery, collaborator of the WHO, and founder of MozBioMed.AI.
- Ranked among the Top 3 Pharmaceutical Scientists in Brazil (2025)
- Author of over 50 international publications
- Recognized for pioneering AI-based discoveries in COVID-19
Basic Plan – £175 (15,000 MZN)
- Complete course + practical projects + international certificate
- Discounted price (one-time payment): £165 (14,000 MZN)
- Installments: 3x £60 (total £180 / 15,000 MZN)
Premium Plan – £580 (50,000 MZN)
- Everything in the Basic Plan + full Module 6
- Strategic support for the international job market: CV, GitHub, recommendation letter, guidance on job applications and visa
- Discounted price (one-time payment): £550 (47,000 MZN)
- Installments: 5x £120 (total £600 / 50,000 MZN)
Course Highlights
- 100% online with pre-recorded lessons
- Hands-on projects with real clinical data and interactive dashboards
- Development of AI apps for hospital and laboratory use
- Completion possible in 3 weeks for dedicated students
- International certificate of completion
- Preparation for the global AI in health market
