Machine Learning in Healthcare: Fundamentals & Applications

Grow your skills with Coursera Plus for $239/year (usually $399). Save now.

Machine Learning in Healthcare: Fundamentals & Applications

Instructors: Sonya Makhni

1,679 already enrolled

Included with

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

Beginner level

No prior experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

Beginner level

No prior experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

23 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There are 4 modules in 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.

In this module, we’ll start demystifying the terminology. We’ll begin by exploring the differences between AI, machine learning and deep learning. You’ll also gain hands-on experience in planning your own AI algorithm development, and learn what goes into preparing and constructing datasets for research questions.

What's included

8 videos7 readings5 assignments1 peer review2 discussion prompts

8 videos Total 23 minutes

Meet Your Faculty: Paul Cerrato 1 minute
Meet Your Faculty: Sonya Makhni 2 minutes
Module Overview 1 minute
Defining Data Mining 4 minutes
Differences Between Machine Learning and Deep Learning 4 minutes
Linear Regression 2 minutes
Dataset Construction 5 minutes
Dataset Preparation 4 minutes

7 readings Total 73 minutes

Welcome to Machine Learning in Healthcare: Fundamentals & Applications 1 minute
Syllabus 10 minutes
Recommended Prior Knowledge: Basic Statistics 5 minutes
Recommended Prior Knowledge: How to Read Journal Articles 30 minutes
Algorithm Project Introduction 5 minutes
Lesson Resources 20 minutes
Module Summary 2 minutes

5 assignments Total 16 minutes

Module Quiz 9 minutes
Question to Consider 1 minute
Check Your Knowledge 3 minutes
Check Your Knowledge 1 minute
Check Your Knowledge 2 minutes

1 peer review Total 120 minutes

Operational Plan and Dataset for AI Algorithm (Peer Review) 120 minutes

2 discussion prompts Total 70 minutes

Welcome to the Course! 10 minutes
Addressing the 30-Day Readmission Problem 60 minutes

In this module, we’ll take a deep dive into several sophisticated AI modeling techniques, including random forest modeling, gradient boosting, clustering and neural networks.

What's included

6 videos8 readings7 assignments2 discussion prompts

6 videos Total 19 minutes

Module Overview 1 minute
Logistic Regression 3 minutes
Decision Trees and Random Forest Modeling 4 minutes
Gradient Boosting 4 minutes
Clustering 5 minutes
Neural Networks 3 minutes

8 readings Total 70 minutes

Week 2 Project Preview 1 minute
Lesson Resources 12 minutes
Week 2 Project Introduction 5 minutes
Module Summary 2 minutes
AI Techniques in Clinical Decision Support 7 minutes
Clustering Study 6 minutes
Gradient Boosting Study 34 minutes
AI Explained: What Is A Neural Network? 3 minutes

7 assignments Total 26 minutes

Module Quiz 11 minutes
Honors Quiz 6 minutes
Question to Consider 1 minute
Check Your Knowledge 2 minutes
Check Your Knowledge 2 minutes
Check Your Knowledge 2 minutes
Check Your Knowledge 2 minutes

2 discussion prompts Total 120 minutes

Can Neural Networks Improve Diagnosis? 60 minutes
Modeling Technique Selection 60 minutes

In this module, you’ll dive deeper into the nitty gritty of how AI algorithms are trained and validated, and examine how they compare to clinicians in the field.

What's included

6 videos5 readings7 assignments2 discussion prompts

6 videos Total 16 minutes

Module Overview 0 minutes
Applying Data Mining and Machine Learning to Real-World Problems Part 1 4 minutes
Applying Data Mining and Machine Learning to Real-World Problems Part 2 3 minutes
Comparing AI Performance to Clinician Performance Part 1 3 minutes
Analyzing the EAGLE Study 3 minutes
Comparing AI Performance to Clinician Performance Part 2 4 minutes

5 readings Total 163 minutes

Week 3 Project Preview 1 minute
Study Values: Specificity, Sensitivity, AUC 25 minutes
Lesson Resources 46 minutes
Module Summary 1 minute
Week 3 Project Introduction: The EAGLE Study 90 minutes

7 assignments Total 31 minutes

Module Quiz 11 minutes
Question to Consider 5 minutes
Check Your Knowledge 3 minutes
Check Your Knowledge 3 minutes
Check Your Knowledge 3 minutes
Check Your Knowledge 3 minutes
Check Your Knowledge 3 minutes

2 discussion prompts Total 105 minutes

Doctors vs. Algorithms 90 minutes
EAGLE Study 15 minutes

In this module, we’ll explore why so many potentially useful algorithms are not being implemented by healthcare providers. That critique will explore the black box dilemma, and the challenges involved in developing accurate and equitable data sets. That means examining the many ways in which algorithms can discriminate against various marginalized segments of the population.

What's included

7 videos6 readings4 assignments2 discussion prompts

7 videos Total 27 minutes

Module Overview 1 minute
Why Clinicians Resist AI-Enabled Algorithms 5 minutes
Addressing Validation Issues 5 minutes
Internal/External Validation 4 minutes
Clinical Validation Studies 2 minutes
Mayo Clinic on Health AI Part 1 6 minutes
Mayo Clinic on Health AI Part 2 5 minutes

6 readings Total 60 minutes

Week 4 Project Preview 2 minutes
Lesson Resources 17 minutes
Lesson Resources 36 minutes
Week 4 Project Introduction 3 minutes
Module Summary 1 minute
Course Summary 1 minute

4 assignments Total 14 minutes

Module Quiz 6 minutes
Check Your Knowledge 3 minutes
Check Your Knowledge 3 minutes
Check Your Knowledge 2 minutes

2 discussion prompts Total 150 minutes

Healthcare Professionals and AI 60 minutes
Validation 90 minutes

Instructors

Sonya Makhni

Northeastern University

1 Course 1,679 learners

Offered by

Northeastern University

Explore more from Research

Northeastern University
Machine Learning for Healthcare Professionals
Course
Cleveland Clinic
Machine Learning in Healthcare: Foundations and Applications
Course
SkillUp
Machine Learning for Healthcare Applications
Course
Stanford University
Fundamentals of Machine Learning for Healthcare
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

5 stars
70%
4 stars
30%
3 stars
0%
2 stars
0%
1 star
0%

Showing 3 of 10

Reviewed on Feb 5, 2025

It was fine. The only complaint about the food is the cookie needed to be heated up and the lines were too long.

View more reviews

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.