Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Machine learning and artificial intelligence are new technological steps that can be applied in numerous fields to bring innovative breakthroughs. Machine learning and artificial intelligence has also an extraordinary scope and application in the medical and healthcare field. Fundamentals of Machine Learning for Healthcare will help the students to understand the significance of machine learning and artificial intelligence in the medical field and their potential to bring about transformations.
Fundamentals of Machine Learning for Healthcare Certification Syllabus will introduce the learners to the basic concepts of machine learning and its applications in medicine and healthcare. By taking the course offered by Stanford University, the students will learn healthcare, health policy, pharmaceutical development, data science, and many more.
Fundamentals of Machine Learning for Healthcare Certification Course, administered by Coursera, is a beginner-level programme that can be taken by the candidates at their own pace. Fundamentals of Machine Learning for Healthcare Certification by Coursera, instructed by Matthew Lungren who is the Associate Professor at the Department of Radiology, and Serena Yeung who is the Assistant Professor at Biomedical Data Science, is the third course in AI in Healthcare Specialization.
The Highlights
- Provided by Coursera
- Approximately 14 hours of programme
- Offered by the Stanford University
- Flexible Deadlines
- Self-Paced Learning Option
- Shareable Certificate
- Beginner Level Course
- Financial Aid Available
- 100% Online Course
Programme Offerings
- English videos with multiple subtitles
- practice quizzes
- Graded Assignments with peer feedback
- graded Quizzes with feedback
- Graded Programming Assignments
- Course Videos & Readings
- EMI payment options
- 14 day refund period.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
The fees for the course Fundamentals of Machine Learning for Healthcare is -
Description | Total Fee in INR |
Course Fee, 1 month | Rs. 6,638 |
Course Fee, 3 months | Rs. 13,276 |
Course Fee, 6 months | Rs. 19,915 |
Eligibility Criteria
Certification Qualifying Details
The students are required to complete successfully all the gamut of proceedings of the programme, including the course readings, quizzes, assignments, and final assessment, to get conferred with the Fundamentals of Machine Learning for Healthcare Certification.
What you will learn
At the end of the Fundamentals of Machine Learning for Healthcare Training, the students will learn:
- Relationships between the fields of machine learning, biostatistics, and traditional computer programming.
- Advanced neural network architectures for text classification, object detection, and segmentation.
- Pharmaceutical development
- Data Science
- Using data for the validation and test of machine learning models.
Who it is for
Fundamentals of Machine Learning for Healthcare Classes can be a highly recommended programme for the professionals like
- Doctor
- Nurse
- Medical Lab Technician
- Health Inspector
- Health Officer
Admission Details
Step 1- At first, the students will have to register and sign up on https://www.coursera.org/ to get access to the courses offered by Coursera.
Step 2 - After activating the Coursera account, the candidate can sign in.
Step 3 - Then, the candidate can search the ‘Stanford University’ in the search column, and then, the courses offered by Stanford University will appear on the screen.
Step 4 - Then, find the course ‘Fundamentals of Machine Learning for Healthcare’ in the list and click on it.
Step 5- Then, the page of the course will appear on the screen, and then, click on the option ‘enroll’. The students can enroll in the programme either free of cost or pay the fee prescribed by Coursera.
The Syllabus
Videos
- 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?
Readings
- Getting Started: Creators of This Course
- Video Image Credit
- Video Image Credit
- Study Guide Module
- Citations and Additional Readings
- Video Image Credit
Quizzes
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
Videos
- Machine Learning Terms, Definitions, and Jargon Part 1
- Machine Learning Terms, Definitions, and Jargon Part 2
- How Machines Learn Part
- How Machines Learn Part
- Supervised Machine Learning Approaches: Regression and the "No Free Lunch" Theorem
- Other Traditional Supervised Machine Learning Approaches
- Support Vector Machine (SVM)
- Unsupervised Machine Learning
Readings
- Study Guide Module
- Citations and Additional Readings
Quizzes
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
Videos
- Introduction to Deep Learning and Neural Network
- 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
Readings
- Video Image Credit
- Study Guide Module
- Citations and Additional Readings
Quizzes
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
Videos
- Introduction to Model Performance Evaluation2m
- Overfitting and Underfitting8m
- Strategies to Address Overfitting, Underfitting and Introduction to Regularization 5m
- Statistical Approaches to Model Evaluation5m
- Receiver Operator and Precision Recall Curves as Evaluation Metrics
Readings
- Study Guide Module 45m
- Citations and Additional Readings
Quizzes
- Reflection Exercise 1
- Reflection Exercise 2
- Knowledge Check
Videos
- Introduction to Common Clinical Machine Learning Challenges6m
- Utility of Causative Model Predictions2m
- Context in Clinical Machine Learning7m
- Intrinsic Interpretability 3m
- Medical Data Challenges in Machine Learning Part 15m
- Medical Data Challenges in Machine Learning Part 25m
- How Much Data Do We Need?3m
- Retrospective Data in Medicine and "Shelf Life" for Data5m
- Medical Data: Quality vs Quantity
Readings
- Study Guide Module
- Citations and Additional Readings
Quizzes
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
Videos
- 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
Readings
- Study Guide Module
- Citations and Additional Readings
- Video Image Credit
- Recommended Reading for Ethics
Quizzes
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
Video
- Introduction to Foundation Models
- Adapting to Technology
- General AI and Emergent Behavior
- How Foundation Models Work
- Healthcare Use Cases for Text Data
- Healthcare Use Cases for Non-textual Unstructured Data
- Challenges and Pitfalls
- Conclusion
Video
- Wrap Up and Goodbyes
Readings
- Final Assessment Note
- Claim CME Credit
- Full Study Guide
Quiz
- Final Assessment
Instructors
Stanford Frequently Asked Questions (FAQ's)
The course is provided by Stanford University.
The programme is offered completely in the online mode.
The learners can complete the programme within about 14 hours.
Yes, the students will be awarded a shareable certificate from the Stanford University School of Medicine which is accredited by the Accreditation Council for Continuing Medical Education (ACCME).
Yes, the learners can pay the fee in installment mode.