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 +1 more
37,225 already enrolled
Gain insight into a topic and learn the fundamentals.
(610 reviews)
Beginner level
No prior experience required
Flexible schedule
1 week at 10 hours a week
Learn at your own pace
96%
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.
Skills you'll gain
- Category: Machine Learning
- Category: Healthcare Ethics
- Category: Healthcare Industry Knowledge
- Category: Machine Learning Algorithms
- Category: Deep Learning
- Category: Health Policy
- Category: Artificial Neural Networks
- Category: Applied Machine Learning
- Category: Reinforcement Learning
- Category: Artificial Intelligence and Machine Learning (AI/ML)
- Category: Responsible AI
- Category: Data Preprocessing
- Category: Model Evaluation
- Category: Data Ethics
- Category: Convolutional Neural Networks
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
19 assignments
Taught in English
Build your subject-matter expertise
This course is part of the AI in Healthcare Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- 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
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
Earn a career certificate
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Instructors
Instructor ratings
(208 ratings)Matthew Lungren
Stanford University
2 Courses 44,529 learners
Serena Yeung
Stanford University
2 Courses 44,529 learners
Offered by
Stanford University
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Showing 3 of 610
MN
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.
VL
Reviewed on Sep 10, 2023
Delivered all it promises. Fast paces and geared to people with lots of ML knowledge. Thank you.Muchas gracias.Valdez Ladd
GM
Reviewed on Nov 11, 2020
Completing this course has given me a solid foundation and confidence to engage at a deeper level with AIML in health, both as a student and exponent thereof.
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.
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.