Introduction

Machine Learning (ML) is transforming global public health systems by enabling predictive analytics, disease surveillance, outbreak forecasting, and evidence-based healthcare decision-making. Machine Learning in Public Health Training Course is designed to equip learners with cutting-edge competencies in health data science, epidemiological modeling, AI-driven diagnostics, and population health intelligence systems. Participants will gain hands-on exposure to real-world datasets and tools used in health informatics, digital epidemiology, and AI-powered health monitoring systems, ensuring they can apply machine learning techniques to improve healthcare delivery and policy outcomes.

With the rise of pandemics, chronic disease burdens, and health inequities, ML has become a critical enabler of precision public health, real-time disease tracking, and automated health risk prediction. This course integrates theoretical foundations with practical applications using Python, R, TensorFlow, and health data platforms, empowering professionals to design scalable solutions for smart healthcare systems, AI-assisted diagnostics, and population-level health interventions. Learners will engage in case-driven learning based on global health challenges such as COVID-19 surveillance, malaria prediction systems, and maternal health analytics.

Programme Curriculum

Machine Learning in Public Health Training Course

Introduction

Course Duration

5 days

Course Objectives

Understand fundamentals of Machine Learning in Healthcare Analytics
Apply predictive modeling for disease outbreak forecasting
Develop skills in health data preprocessing and feature engineering
Implement AI-driven epidemiological surveillance systems
Use deep learning for medical image analysis
Design public health decision support systems
Apply natural language processing (NLP) in health records
Build real-time disease monitoring dashboards
Analyze social determinants of health using ML models
Evaluate health risk prediction algorithms
Implement population health analytics frameworks
Use cloud-based health data platforms for ML deployment
Develop ethical understanding of AI in healthcare governance

Target Audience

Public health professionals
Epidemiologists and biostatisticians
Healthcare data analysts
Medical researchers
Government health policymakers
AI/ML engineers in healthcare
NGO and global health workers
Graduate students in public health, data science, and biomedical fields

Course Modules

Module 1: Introduction to Machine Learning in Public Health

Basics of supervised, unsupervised, reinforcement learning
Role of ML in global health systems
Public health data ecosystems overview
Data sources: WHO, CDC, hospital EHR systems
Case Study: COVID-19 predictive modeling using ML

Module 2: Health Data Collection & Preprocessing

Data cleaning and normalization techniques
Handling missing and noisy health data
Feature extraction from clinical datasets
Data integration from multiple health systems
Case Study: Malaria dataset preprocessing for Africa region

Module 3: Predictive Analytics in Disease Outbreaks

Time-series forecasting models
Regression and classification techniques
Outbreak early warning systems
Risk scoring algorithms
Case Study: Dengue fever outbreak prediction model

Module 4: Machine Learning for Epidemiology

Epidemiological modeling with ML
Transmission dynamics analysis
Contact tracing systems
Cluster detection algorithms
Case Study: Tuberculosis spread modeling in urban populations

Module 5: Deep Learning in Medical Imaging

CNNs for X-ray and MRI analysis
Image classification techniques
Diagnostic automation systems
Transfer learning in healthcare
Case Study: AI-based pneumonia detection from chest X-rays

Module 6: Natural Language Processing in Healthcare

Clinical text mining from EHRs
Sentiment analysis of patient records
Named entity recognition in medical data
Chatbots for healthcare support
Case Study: Automated extraction of symptoms from clinical notes

Module 7: Public Health Decision Support Systems

AI-driven policy modeling
Resource allocation optimization
Health intervention simulation
Dashboard development for health agencies
Case Study: Hospital bed allocation during COVID-19 surge

Module 8: Ethical AI & Deployment in Healthcare

Bias and fairness in health algorithms
Data privacy and HIPAA/GDPR compliance
Explainable AI in healthcare decisions
Cloud deployment of ML models
Case Study: Ethical evaluation of AI-based triage system

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Predictive modeling Data preprocessing Algorithm selection Health data analysis

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000