This course has been certified by the CPD Certification Service as conforming to continuing professional development principles. Find out more.

Apply machine learning to healthcare using R programming

Welcome to this introduction to the University of Aberdeen’s 16‑week course, Machine Learning for Healthcare, which runs in January. Should you wish to continue to the full course, you can enjoy 10% off your registration using the code FutureLearn26.

How can machine learning improve healthcare decision-making and patient outcomes? As health data grows increasingly complex, applying algorithms to sensitive medical information requires specialised skills and ethical awareness.

This three week course equips you with practical machine learning skills tailored to healthcare. Using R and RStudio, you’ll build, evaluate, and deploy models that address clinical problems from disease prediction to treatment optimisation.

Understand machine learning foundations for healthcare

Explore core concepts including supervised, unsupervised, and reinforcement learning through healthcare examples like predictive diagnostics and pattern recognition.

Set up R and RStudio for health data analysis

Develop hands-on skills with R programming, RStudio, packages, and R Markdown for reproducible workflows. You’ll practice data wrangling, visualization, and creating shareable outputs.

Through practical exercises, you’ll prepare health datasets, explore patterns, and implement machine learning pipelines while maintaining ethical data handling standards.

Apply machine learning to real healthcare challenges

Learn to select appropriate algorithms for real-world problems, evaluating performance and addressing biases. You’ll use health data to build models, test generalisability, and interpret results for clinical use.

You’ll tackle challenges including ethical AI implementation, regulatory considerations, and building trustworthy systems for healthcare settings.

Syllabus

Week 1
What is Machine Learning?
- Course Welcome
  An introduction to the course and an overview of machine learning in healthcare
- Applications of Machine Learning in Healthcare
  A closer look at how machine learning is applied in healthcare contexts.
- Practicalities of Machine Learning
  In this activity, in a series of short recordings, we take a closer look at the practicalities of machine learning.
Week 2
R and R Studio
- Introduction to R Studio
  In this activity, we’ll install R and R Studio and learn a little about the R programming language.
- Programming with R
  In this activity, we learn more about the practicalities of programming with R
- Practicing with R
  An opportunity get some hands-on experience practicing with R
Week 3
How Machines Learn
- How Machines Learn
  A look at the methods of how machines learn.
- Machine Learning Methods
  This series of recordings explores the methods used to facilitate machine learning
- Practicing with R
  A further opportunity to get some hands-on experience with R

Who is this accredited by?

The CPD Certification Service:

The CPD Certification Service was established in 1996 and is the leading independent CPD accreditation institution operating across industry sectors to complement the CPD policies of professional and academic bodies.

When would you like to start?

Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.

Available now

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

Explain fundamental machine learning principles and algorithms used in healthcare, including supervised, unsupervised, and reinforcement learning, and differentiate these approaches from traditional statistical methods.
Apply R and RStudio to prepare, manage, and visualise healthcare datasets using reproducible workflows while demonstrating ethical data handling practices.
Analyse and evaluate machine learning models for healthcare applications by selecting, tuning, and testing algorithms, and assessing performance, bias, and generalisability.
Interpret and communicate machine learning findings to support healthcare decision-making, considering clinical relevance, ethical implications, and regulatory requirements.

Who is the course for?

This course is ideal if you have:

a background in healthcare or health research and want to learn more about machine learning, or
a background in computational or data-intensive sciences and you’re keen to work in the health sector.

Who developed the course?

University of Aberdeen

University of Aberdeen is the 3rd oldest Scottish and 5th oldest UK University. With 14500 students from 120 countries, it is a world leader in medical research, energy, environment, law and business.

What's included?

University of Aberdeen are offering everyone who joins this course a free digital upgrade, so that you can experience the full benefits of studying online for free. This means that you get:

Unlimited access to this course

Includes any articles, videos, peer reviews and quizzes

A PDF Certificate of Achievement to prove your success when you’re eligible

Learning on FutureLearn

Your learning, your rules

Courses are split into weeks, activities, and steps to help you keep track of your learning
Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

Experience the power of social learning, and get inspired by an international network of learners
Share ideas with your peers and course educators on every step of the course
Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

As you work through the course, use notifications and the Progress page to guide your learning
Whenever you’re ready, mark each step as complete, you’re in control
Complete 90% of course steps and all of the assessments to earn your certificate

Want to know more about learning on FutureLearn? Using FutureLearn