Machine Learning and AI Applications in Healthcare

This course is part of Microsoft Azure AI in Healthcare Professional Certificate

Instructor: Microsoft

Access provided by ExxonMobil

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

18 assignments¹

AI Graded see disclaimer

Taught in English

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Build your Machine Learning expertise

This course is part of the Microsoft Azure AI in Healthcare Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
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

ML Fundamentals Mastery 90 minutes
Lifecycle Management Proficiency 90 minutes
Validation Excellence Assessment 90 minutes
Machine Learning Foundations and Model Development 30 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

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
AI Bias, Reliability, and Interpretability 30 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 AI Proficiency 30 minutes
Risk Prediction Excellence 90 minutes
Model Library Utilization 30 minutes
Medical Imaging and Predictive Analytics 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.