Machine Learning for Health Research

Build ethical, reliable machine learning models that turn complex health data into meaningful research insights.

20 hours

Basics of Python

Intermediate

Course information

This course is tailored to empower you with the knowledge and skills required to harness the potential of machine learning within the medical and health research domains.

In this course, you will learn a range of different Artificial Intelligence Algorithms.

You will learn the key characteristics of these algorithms, and how to implement them within Python.

After completing the course, you will be able to make decisions about when and how to implement different AI solutions for common problems you may encounter.

This course is roughly 20 hours of learnings with video lectures, quizzes and hands-on practical programming examples for you to work through.

Learning objectives

Gain an in-depth understanding of diverse ML algorithms and their applications in health research.

Handle missing data, particularly in the context of Electronic Health Records.

Utilize and optimize tree-based and ensemble models for complex health data.

Implement neural networks and understand their learning processes.

Effectively manage the entire machine learning pipeline from data acquisition to model evaluation.

Achieve data imputation using sophisticated techniques.

Understand the ethics and challenges in applying ML in healthcare contexts.

Enroll on this course