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.
Machine Learning and AI Applications in Healthcare
Machine Learning and AI Applications in Healthcare
本课程是 Microsoft Azure AI in Healthcare 专业证书 的一部分
位教师： Microsoft
访问权限由 Coursera Learning Team 提供
您将学到什么
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.
您将获得的技能
- Medical Imaging
- Artificial Intelligence
- Data Visualization Software
- Health Informatics
- Predictive Analytics
- MLOps (Machine Learning Operations)
- Power BI
- Feature Engineering
- Model Deployment
- Microsoft Azure
- Machine Learning
- Azure Synapse Analytics
- Model Evaluation
- Image Analysis
- Applied Machine Learning
- Data Preprocessing
- Computer Vision
- Responsible AI
- 技能部分已折叠。显示 8 项技能，共 18 项。
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累 Machine Learning 领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 通过 Microsoft 获得可共享的职业证书
该课程共有4个模块
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.
涵盖的内容
6个视频6篇阅读材料4个作业
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.
涵盖的内容
6个视频5篇阅读材料5个作业
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.
涵盖的内容
6个视频6篇阅读材料4个作业
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.
涵盖的内容
6个视频6篇阅读材料5个作业
获得职业证书
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