Fundamentals of Machine Learning for Healthcare
- 4.8
-
![]()
![]()
![]()
![]()
![]()
Approx. 12 hours to complete
Course Summary
This course covers the fundamental concepts of machine learning and how it can be applied to healthcare. It covers a wide range of topics, from data preprocessing and feature engineering to building predictive models and evaluating their performance.Key Learning Points
- Learn how to apply machine learning techniques to healthcare data
- Understand the importance of data preprocessing and feature engineering
- Build and evaluate predictive models for clinical outcomes
Job Positions & Salaries of people who have taken this course might have
- Healthcare Data Analyst
- USA: $60,000 - $110,000
- India: ₹5,00,000 - ₹12,00,000
- Spain: €25,000 - €50,000
- Machine Learning Engineer
- USA: $90,000 - $150,000
- India: ₹10,00,000 - ₹20,00,000
- Spain: €35,000 - €70,000
- Healthcare Analytics Manager
- USA: $100,000 - $160,000
- India: ₹15,00,000 - ₹30,00,000
- Spain: €50,000 - €90,000
Related Topics for further study
Learning Outcomes
- Understand the basics of machine learning and its application in healthcare
- Learn how to preprocess and engineer healthcare data for machine learning
- Build and evaluate predictive models for clinical outcomes
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics and programming
- Familiarity with healthcare data
Course Difficulty Level
IntermediateCourse Format
- Online Self-paced
- Video Lectures
- Quizzes and Assignments
Similar Courses
- Data Science and Machine Learning Bootcamp
- Applied Data Science with Python
- Machine Learning for Healthcare
Related Education Paths
- Certificate in Healthcare Analytics
- Master of Science in Health Data Analytics
- Certified Health Data Analyst (CHDA)
Notable People in This Field
- Co-founder of Coursera and Professor of Computer Science
- Co-director of Stanford Institute for Human-Centered Artificial Intelligence
Related Books
Description
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.
Knowledge
- 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.
Outline
- 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
- Study Guide Module 1
- Citations and Additional Readings
- 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
- 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
- Recommended Reading for Ethics
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
- Course Conclusion
- Wrap Up and Goodbyes
- Final Assessment Note
- Full Study Guide
- Final Assessment
Summary of User Reviews
Discover the fundamentals of machine learning in healthcare with this highly-rated course on Coursera. Students rave about the engaging content and practical applications of the material, giving it an overall positive rating.Key Aspect Users Liked About This Course
Many users appreciate the practical applications of the course material, allowing them to apply what they learned in real-world scenarios.Pros from User Reviews
- Engaging content that keeps users interested throughout the course
- Practical applications of machine learning in healthcare
- Well-organized and easy to follow course structure
- Instructors with extensive knowledge and experience in the field
- Great introduction to machine learning for beginners
Cons from User Reviews
- Some users found the material too basic and not challenging enough
- Limited interaction with instructors and other students
- Not enough emphasis on the technical aspects of machine learning
- Some technical glitches with the course platform
- Course may be too specific to healthcare for some users
Matthew LungrenTop Instructor
4.8 Raiting
Scala & Functional Programming Essentials | Rock the JVM
- 4.6
-
![]()
![]()
![]()
![]()
![]()
Become a Scala programmer and get the skills you need to work with Spark, Akka, and any Scala framework! Why Scala:...
Future of Artificial Intelligence
- 3.4
-
![]()
![]()
![]()
![]()
![]()
Learn fundamentals of Artificial Intelligence from Scratch Course Information: Artificial Intelligence (AI) is being widely recognized to be the power that will fuel the future global digital economy....
The Complete Python and JavaScript Course: Build Projects
- 4.1
-
![]()
![]()
![]()
![]()
![]()
Want to learn ES6 development and TensorFlow stock market prediction modeling? Build your first web app in this course! " ...
The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
- 4.8
-
![]()
![]()
![]()
![]()
![]()
It's the age of machine learning. Want to tap that potential? And for that reason, data scientists aren't the only ones who need to learn the fundamentals....
