Machine Learning and AI Applications in Healthcare
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Machine Learning and AI Applications in Healthcare
This course is part of Microsoft Azure AI in Healthcare Professional Certificate
Instructor: Microsoft
Included with
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What you'll learn
Build and deploy machine learning models using healthcare datasets and Azure AI tools.
Create predictive analytics solutions for patient outcomes and clinical decision support.
Evaluate and interpret AI models to ensure fairness, reliability, and actionable insights in healthcare.
Skills you'll gain
- Health Equity
- Machine Learning
- Responsible AI
- Machine Learning Methods
- Predictive Analytics
- MLOps (Machine Learning Operations)
- Predictive Modeling
- Data Visualization Software
- Medical Imaging
- Model Training
- Model Evaluation
- Image Analysis
- Azure Synapse Analytics
- Data Presentation
- Dashboard Creation
- Artificial Intelligence and Machine Learning (AI/ML)
Tools you'll learn
Details to know
January 2026
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- Earn a shareable career certificate from Microsoft
There are 4 modules in this course
This comprehensive course bridges machine learning fundamentals with specialized healthcare AI applications, guiding students through the complete AI model lifecycle from data preprocessing to production deployment. You'll master core ML algorithms and deep learning architectures while gaining hands-on experience building medical imaging analysis systems, predictive models for patient outcomes, and clinical NLP applications using Azure AI services including Azure Machine Learning, Cognitive Services, and Computer Vision. The curriculum emphasizes healthcare-specific challenges including rigorous clinical validation methodologies that satisfy regulatory requirements, comprehensive bias detection and mitigation strategies to ensure equitable performance across diverse patient populations, and secure HIPAA-compliant data handling practices. Through practical labs and real-world case studies, you'll develop skills in model training, hyperparameter optimization, performance evaluation using clinical metrics (sensitivity, specificity, AUC), MLOps implementation with CI/CD pipelines, and creating compelling data visualizations that communicate AI insights to clinical stakeholders.
This foundational module introduces learners to essential machine learning concepts specifically applied to healthcare contexts. Students explore the complete AI model lifecycle from initial data preparation through deployment, gaining hands-on experience with Azure ML Studio's visual interface. The module emphasizes practical application of ML fundamentals while establishing critical validation practices necessary for clinical environments.
What's included
6 videos6 readings4 assignments
6 videos•Total 19 minutes
- Why Machine Learning Revolutionizes Healthcare•2 minutes
- Building Your First Healthcare ML Model in Azure•3 minutes
- From Data to Deployment: The Journey•3 minutes
- End-to-End Model Development in Azure ML•4 minutes
- When Models Fail: The Importance of Validation•2 minutes
- Advanced Validation Techniques in Azure ML•4 minutes
6 readings•Total 315 minutes
- Machine Learning Essentials for Healthcare Professionals•15 minutes
- Hands-on – Readmission Risk Predictor Development•90 minutes
- The Complete Healthcare AI Model Lifecycle•15 minutes
- Hands-on – Complete Lifecycle Implementation•90 minutes
- Clinical Model Validation Strategies•15 minutes
- Hands-on – Multi-Site Validation Exercise•90 minutes
4 assignments•Total 300 minutes
- Machine Learning Foundations and Model Development•30 minutes
- ML Fundamentals Mastery•90 minutes
- Lifecycle Management Proficiency•90 minutes
- Validation Excellence Assessment•90 minutes
This module addresses critical challenges in healthcare AI implementation by focusing on bias detection, system reliability, and model interpretability. Learners develop expertise in identifying and mitigating bias in healthcare datasets while implementing fairness constraints and reliability frameworks. The module emphasizes creating interpretable AI solutions that translate complex model outputs into clinically meaningful insights for healthcare professionals.
What's included
6 videos5 readings5 assignments
6 videos•Total 22 minutes
- Hidden Bias, Real Consequences•2 minutes
- Detecting Bias with Azure Responsible AI Dashboard•5 minutes
- Building Trust Through Reliable AI•2 minutes
- Implementing Fairness Constraints in Azure ML•5 minutes
- Making Black Boxes Transparent•2 minutes
- Creating Interpretable Clinical AI with Azure•5 minutes
5 readings•Total 225 minutes
- Sources and Impacts of Healthcare AI Bias•15 minutes
- Hands-on – Bias Detection Workshop•90 minutes
- Ensuring Healthcare AI Reliability•15 minutes
- Explainable AI for Clinical Decision-Making•15 minutes
- Hands-on – Clinical Explanation Design•90 minutes
5 assignments•Total 390 minutes
- AI Bias, Reliability, and Interpretability•30 minutes
- Bias Identification and Assessment•90 minutes
- Reliability Engineering for Clinical AI•90 minutes
- Reliability and Fairness Implementation•90 minutes
- Interpretation and Translation Mastery•90 minutes
This module explores specialized applications of AI in medical imaging analysis and patient risk prediction. Students learn to implement computer vision solutions for diagnostic imaging support while developing sophisticated predictive models for clinical risk assessment. The module combines hands-on experience with Azure Cognitive Services and pre-built model libraries to create practical healthcare AI applications.
What's included
6 videos6 readings4 assignments
6 videos•Total 22 minutes
- AI as the Radiologist's Assistant•2 minutes
- Building a Chest X-Ray Classifier with Azure•5 minutes
- Predicting the Unpredictable•2 minutes
- Developing a Readmission Risk Model in Azure ML•5 minutes
- Standing on the Shoulders of Giants•2 minutes
- Implementing Pre-Built Models from Azure AI Gallery•5 minutes
6 readings•Total 315 minutes
- Medical Imaging AI Fundamentals•15 minutes
- Hands-on – Medical Image Analysis Pipeline•90 minutes
- Clinical Risk Prediction Modeling•15 minutes
- Hands-on – Comprehensive Risk Prediction System•90 minutes
- Leveraging Pre-Built Healthcare AI Models•15 minutes
- Hands-on – Model Library Evaluation and Selection•90 minutes
4 assignments•Total 180 minutes
- Medical Imaging and Predictive Analytics•30 minutes
- Medical Imaging AI Proficiency•30 minutes
- Risk Prediction Excellence•90 minutes
- Model Library Utilization•30 minutes
This module focuses on transforming healthcare data and AI predictions into actionable visual insights for clinical decision-making. Learners master data integration techniques using Azure Synapse while creating comprehensive dashboards with Power BI. The module emphasizes building visualization solutions that effectively communicate complex healthcare analytics to diverse stakeholder audiences, from clinicians to administrators.
What's included
6 videos6 readings5 assignments
6 videos•Total 20 minutes
- From Data Chaos to Clinical Insights•2 minutes
- Building a Healthcare Data Pipeline with Azure Synapse•4 minutes
- Seeing the Story in the Data•2 minutes
- Creating Clinical Dashboards with Power BI•5 minutes
- Predictive Analytics Meets Visual Intelligence•2 minutes
- Integrating ML Models with Power BI Dashboards•4 minutes
6 readings•Total 315 minutes
- Healthcare Data Integration Strategies•15 minutes
- Hands-on – Multi-Source Data Integration Project•90 minutes
- Effective Healthcare Data Visualization•15 minutes
- Hands-on – Comprehensive Healthcare Dashboard Development•90 minutes
- Integrated Analytics and ML Visualization•15 minutes
- Hands-on – Predictive Analytics Dashboard Creation•90 minutes
5 assignments•Total 210 minutes
- Healthcare Data Visualization and Analytics•30 minutes
- Patient Risk Prediction and Visualization System•90 minutes
- Data Integration Mastery•30 minutes
- Visualization Excellence•30 minutes
- Advanced Analytics Integration•30 minutes
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