Coursera
Coursera Logo

Machine Learning in Healthcare: Fundamentals & Applications 

  • Offered byCoursera

Machine Learning in Healthcare: Fundamentals & Applications
 at 
Coursera 
Overview

Duration

18 hours

Total fee

Free

Mode of learning

Online

Official Website

Explore Free Course External Link Icon

Credential

Certificate

Machine Learning in Healthcare: Fundamentals & Applications
 at 
Coursera 
Highlights

  • Earn a certificate from Northeastern University
  • Add to your LinkedIn profile
  • 23 quizzes
Details Icon

Machine Learning in Healthcare: Fundamentals & Applications
 at 
Coursera 
Course details

More about this course
  • Examines data mining perspectives and methods in a healthcare context
  • Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each
  • Students are exposed to contemporary data mining software applications and basic programming skills
  • Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling

Machine Learning in Healthcare: Fundamentals & Applications
 at 
Coursera 
Curriculum

Demystifying Data Mining and Artificial Intelligence

Meet Your Faculty: Paul Cerrato

Meet Your Faculty: Sonya Makhni

Module Overview

Defining Data Mining

Differences Between Machine Learning and Deep Learning

Linear Regression

Dataset Construction

Dataset Preparation

Welcome to Machine Learning in Healthcare: Fundamentals & Applications

Syllabus

Recommended Prior Knowledge: Basic Statistics

Recommended Prior Knowledge: How to Read Journal Articles

Algorithm Project Introduction

Lesson Resources

Module Summary

Question to Consider

Check Your Knowledge

Check Your Knowledge

Check Your Knowledge

Module Quiz

Operational Plan and Dataset for AI Algorithm (Peer Review)

Welcome to the Course!

Addressing the 30-Day Readmission Problem

Exploring the AI/Machine Learning Toolbox

Module Overview

Logistic Regression

Decision Trees and Random Forest Modeling

Gradient Boosting

Clustering

Neural Networks

Week 2 Project Preview

Lesson Resources

Week 2 Project Introduction

Module Summary

AI Techniques in Clinical Decision Support

Clustering Study

Gradient Boosting Study

AI Explained: What Is A Neural Network?

Question to Consider

Check Your Knowledge

Check Your Knowledge

Check Your Knowledge

Check Your Knowledge

Module Quiz

Honors Quiz

Can Neural Networks Improve Diagnosis?

Modeling Technique Selection

Practical Application of AI/Machine Learning

Module Overview

Applying Data Mining and Machine Learning to Real-World Problems Part 1

Applying Data Mining and Machine Learning to Real-World Problems Part 2

Comparing AI Performance to Clinician Performance Part 1

Analyzing the EAGLE Study

Comparing AI Performance to Clinician Performance Part 2

Week 3 Project Preview

Study Values: Specificity, Sensitivity, AUC

Lesson Resources

Module Summary

Week 3 Project Introduction: The EAGLE Study

Question to Consider

Check Your Knowledge

Check Your Knowledge

Check Your Knowledge

Check Your Knowledge

Check Your Knowledge

Module Quiz

Doctors vs. Algorithms

EAGLE Study

The Credibility Gap

Module Overview

Why Clinicians Resist AI-Enabled Algorithms

Addressing Validation Issues

Internal/External Validation

Clinical Validation Studies

Mayo Clinic on Health AI Part 1

Mayo Clinic on Health AI Part 2

Week 4 Project Preview

Lesson Resources

Lesson Resources

Week 4 Project Introduction

Module Summary

Course Summary

Check Your Knowledge

Check Your Knowledge

Check Your Knowledge

Module Quiz

Healthcare Professionals and AI

Validation

Other courses offered by Coursera

– / –
3 months
Beginner
– / –
20 hours
Beginner
– / –
2 months
Beginner
– / –
3 months
Beginner
View Other 6715 CoursesRight Arrow Icon
qna

Machine Learning in Healthcare: Fundamentals & Applications
 at 
Coursera 

Student Forum

chatAnything you would want to ask experts?
Write here...