Northeastern University
Machine Learning in Healthcare: Fundamentals & Applications
Northeastern University

Machine Learning in Healthcare: Fundamentals & Applications

Taught in English


Gain insight into a topic and learn the fundamentals

Sonya Makhni
Paul Cerrato

Instructors: Sonya Makhni

Beginner level
No prior experience required
18 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace

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There are 4 modules in this course

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.

What's included

8 videos7 readings5 quizzes1 peer review2 discussion prompts

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.

What's included

6 videos8 readings7 quizzes2 discussion prompts

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.

What's included

6 videos5 readings7 quizzes2 discussion prompts

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.

What's included

7 videos6 readings4 quizzes2 discussion prompts


Sonya Makhni
Northeastern University
1 Course617 learners

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