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Examines data mining perspectives and methods in a healthcare context. Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each. Students are exposed to contemporary data mining software applications and basic programming skills. Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling.
Machine Learning in Healthcare: Fundamentals & Applications
您将获得的技能
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
该课程共有4个模块
In this module, we’ll start demystifying the terminology. We’ll begin by exploring the differences between AI, machine learning and deep learning. You’ll also gain hands-on experience in planning your own AI algorithm development, and learn what goes into preparing and constructing datasets for research questions.
涵盖的内容
8个视频7篇阅读材料5个作业1次同伴评审2个讨论话题
In this module, we’ll take a deep dive into several sophisticated AI modeling techniques, including random forest modeling, gradient boosting, clustering and neural networks.
涵盖的内容
6个视频8篇阅读材料7个作业2个讨论话题
In this module, you’ll dive deeper into the nitty gritty of how AI algorithms are trained and validated, and examine how they compare to clinicians in the field.
涵盖的内容
6个视频5篇阅读材料7个作业2个讨论话题
In this module, we’ll explore why so many potentially useful algorithms are not being implemented by healthcare providers. That critique will explore the black box dilemma, and the challenges involved in developing accurate and equitable data sets. That means examining the many ways in which algorithms can discriminate against various marginalized segments of the population.
涵盖的内容
7个视频6篇阅读材料4个作业2个讨论话题
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