Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles.
Fundamentals of Machine Learning for Healthcare
Fundamentals of Machine Learning for Healthcare
This course is part of AI in Healthcare Specialization
Instructors: Matthew Lungren
27,231 already enrolled
Gain insight into a topic and learn the fundamentals.
4.8
(500 reviews)
Beginner level
No prior experience required
Flexible schedule
Approx. 14 hours
Learn at your own pace
97%
Most learners liked this course
What you'll learn
Define important relationships between the fields of machine learning, biostatistics, and traditional computer programming.
Learn about advanced neural network architectures for tasks ranging from text classification to object detection and segmentation.
Learn important approaches for leveraging data to train, validate, and test machine learning models.
Understand how dynamic medical practice and discontinuous timelines impact clinical machine learning application development and deployment.
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
19 assignments
Taught in English
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This course is part of the AI in Healthcare Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 8 modules in this course
What's included
7 videos6 readings3 assignments
What's included
8 videos2 readings3 assignments
What's included
10 videos3 readings3 assignments
What's included
5 videos2 readings3 assignments
What's included
9 videos2 readings3 assignments
What's included
6 videos4 readings3 assignments
What's included
8 videos
What's included
1 video3 readings1 assignment
Instructors
Instructor ratings
4.9 (180 ratings)Offered by
Recommended if you're interested in Machine Learning
Stanford University
Northeastern University
Stanford University
Northeastern University
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4.8
500 reviews
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Showing 3 of 500
FW
5
Reviewed on May 7, 2023
Excellent introduction to ML and AI in the context of healthcare. Loaded with information without being overwhelming
MN
4
Reviewed on Jan 2, 2021
Good course but the language needs to be simpler. Sometimes simple facts are complicated with the use of high pedigree words that don't really add much to conveying the overall message.
QB
5
Reviewed on Jul 21, 2023
This course will give you the complete information that is required by a beginner. Best if you want help to start working on ML project related to healthcare in beginning of your career.
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Frequently asked questions
Dates and Duration
Original Release Date: 08/10/2023
Expiration Date: 08/10/2026
Estimated Time to Complete: 11 hours
CME Credits Offered: 11.00
Accreditation
The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The Stanford University School of Medicine designates this enduring material for a maximum of 11.00 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Disclosures
The Stanford University School of Medicine adheres to ACCME Criteria, Standards and Policies regarding industry support of continuing medical education. There are no relevant financial relationships with ACCME-defined commercial interests for anyone who was in control of the content of this activity.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.