Overview
This Deep Learning for Healthcare Specialization offered by Coursera in partnership with University of Illinois is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field.
You will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from University of Illinois Urbana-Champaign
Skills you'll gain
- Big Data
- Machine Learning
- Deep Learning
- Health Care
- Graphs
- Unsupervised Learning
- Autoencoder
Programme Structure
Courses include:
- Health Data Science Foundation
- Deep Learning Methods for Healthcare
- Advanced Deep Learning Methods for Healthcare
Key information
Duration
- Part-time
- 2 months
- Flexible
Start dates & application deadlines
Language
Delivered
Campus Location
- Mountain View, United States
Disciplines
Health Sciences Artificial Intelligence View 127 other Short Courses in Artificial Intelligence in United StatesWhat students do after studying
Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Advanced level
- Programming experience in Python recommended, as well as knowledge of fundamentals in Machine Learning
Tuition Fees
-
International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
Additional Details
- Coursera Plus: Subscribe to build job-ready skills from world-class institutions.
- $59/month, cancel anytime or $399/year with 14-day money-back guarantee
Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.