Stanford University - Fundamentals of Machine Learning for Healthcare
5.0 /5- Offered byCoursera
Fundamentals of Machine Learning for Healthcare at Coursera Overview
Duration | 12 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Fundamentals of Machine Learning for Healthcare at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Fundamentals of Machine Learning for Healthcare at Coursera Course details
- 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.
- This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare.
- The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies.
- Co-author: Geoffrey Angus
- Contributing Editors:
- Mars Huang
- Jin Long
- Shannon Crawford
- Oge Marques
- The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.
Fundamentals of Machine Learning for Healthcare at Coursera Curriculum
Why machine learning in healthcare?
Why machine learning in healthcare?
History of AI in Medicine
Course Overview
Why Healthcare Needs Machine Learning
Machine Learning Magic
Machine Learning, Biostatistics, Programming
Can Machine Learning Solve Everything?
Getting Started: Creators of This Course
Video Image Credit
Video Image Credit
Study Guide Module 1
Citations and Additional Readings
Video Image Credit
Reflection Exercise
Reflection Exercise
Knowledge Check
Concepts and Principles of machine learning in healthcare part 1
Machine Learning Terms, Definitions, and Jargon Part 1
Machine Learning Terms, Definitions, and Jargon Part 2
How Machines Learn Part 1
How Machines Learn Part 2
Supervised Machine Learning Approaches: Regression and the "No Free Lunch" Theorem
Other Traditional Supervised Machine Learning Approaches
Support Vector Machine (SVM)
Unsupervised Machine Learning
Study Guide Module 2
Citations and Additional Readings
Reflection Exercise
Reflection Exercise
Knowledge Check
Concepts and Principles of machine learning in healthcare part 2
Introduction to Deep Learning and Neural Networks
Deep Learning and Neural Networks
Cross Entropy Loss
Gradient Descent
Representing Unstructured Image and Text Data
Convolutional Neural Networks
Natural Language Processing and Recurrent Neural Networks
The Transformer Architecture for Sequences
Commonly Used and Advanced Neural Network Architectures
Advanced Computer Vision Tasks and Wrap-Up
Video Image Credit
Study Guide Module 3
Citations and Additional Readings
Reflection Exercise
Reflection Exercise
Knowledge Check
Evaluation and Metrics for machine learning in healthcare
Introduction to Model Performance Evaluation
Overfitting and Underfitting
Strategies to Address Overfitting, Underfitting and Introduction to Regularization
Statistical Approaches to Model Evaluation
Receiver Operator and Precision Recall Curves as Evaluation Metrics
Study Guide Module 4
Citations and Additional Readings
Reflection Exercise 1
Reflection Exercise 2
Knowledge Check
Strategies and Challenges in Machine Learning in Healthcare
Introduction to Common Clinical Machine Learning Challenges
Utility of Causative Model Predictions
Context in Clinical Machine Learning
Intrinsic Interpretability
Medical Data Challenges in Machine Learning Part 1
Medical Data Challenges in Machine Learning Part 2
How Much Data Do We Need?
Retrospective Data in Medicine and "Shelf Life" for Data
Medical Data: Quality vs Quantity
Study Guide Module 5
Citations and Additional Readings
Reflection Exercise
Reflection Exercise
Knowledge Check
Best practices, teams, and launching your machine learning journey
Clinical Utility and Output Action Pairing
Taking Action - Utilizing the OAP Framework
Building Multidiciplinary Teams for Clinical Machine Learning
Governance, Ethics, and Best Practices
On Being Human in the Era of Clinical Machine Learning
Death by GPS and Other Lessons of Automation Bias
Study Guide Module 6
Citations and Additional Readings
Video Image Credit
Recommended Reading for Ethics
Reflection Exercise
Reflection Exercise
Knowledge Check
Course Conclusion
Wrap Up and Goodbyes
Final Assessment Note
Claim CME Credit
Full Study Guide
Final Assessment
Fundamentals of Machine Learning for Healthcare at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Fundamentals of Machine Learning for Healthcare at Coursera Students Ratings & Reviews
2 Ratings- 4-52
Student Forum
Anything you would want to ask experts?
Data ScienceMachine LearningMachine Learning and Pattern RecognitionFundamentals of Machine Learning for Healthcare