Dr Data Insight - Machine Learning Basics Course

Welcome

M1: Foundations of Machine Learning

Welcome to the Basics in Machine Learning Course!

Machine learning (ML) is rapidly transforming how data is used in healthcare—from predicting patient outcomes and identifying risk factors to optimising hospital resources. This introductory course is designed for professionals with little or no prior experience in ML who want to understand its principles, applications, and real-world impact, especially within healthcare and the NHS.

As the NHS expands its use of artificial intelligence and data science—reflected in the £250 million investment into the NHS AI Lab—the ability to understand and contribute to ML projects is becoming increasingly valuable. Through practical, hands-on lessons, you’ll learn the fundamentals of machine learning, including supervised vs. unsupervised learning, model evaluation, and ethical considerations.

Whether you're an analyst, clinician, researcher, or part of a digital transformation team, this course will help you build the confidence to engage with machine learning tools and concepts—and begin your journey into applied healthcare AI.

Purpose of This Course

This course is designed for NHS professionals, data analysts, clinical informatics specialists, and digital transformation leads who want to understand and apply machine learning (ML) in real-world healthcare scenarios.

Whether you're new to ML or looking to deepen your analytical skills, this course takes you from statistical intuition to deployable models — all through the lens of NHS-specific datasets, challenges, and priorities.

Why Machine Learning in Healthcare Matters

The NHS sits on vast datasets: HES, SUS, diagnostics, A&E attendances, outpatient DNA patterns, and more. Machine learning helps us:

Predict readmissions before they happen
Identify high-risk patients for early intervention
Segment patient cohorts for tailored services
Improve performance forecasting and capacity planning
Support clinical decisions with data-driven evidence

But we do this with care, ethics at the core.

What You’ll Learn

By the end of this course, you’ll be able to:

Understand key ML models and when to use them
Prepare NHS-style datasets for training and testing
Build interpretable models with Python (Pandas, scikit-learn)
Communicate your results confidently to technical and non-technical audiences
Handle interview scenarios related to healthcare AI and NHS values

Course Structure

This is a 5-module journey:

ML Foundations in Healthcare
Data Preparation and Feature Engineering
Classification & Prediction Models
Clustering and Unsupervised Learning
Deployment, Communication & Interview Readiness

Each module includes:

✅ A focused lesson
✅ Hands-on exercises in Python
✅ Real NHS use cases
✅ Optional quizzes
✅ Interview preparation prompts

Requirements

No prior ML experience required
Basic Python/Pandas familiarity recommended
Access to Google Colab or Jupyter Notebook (cloud or local)

Let’s Begin

"Machine learning isn't magic. It's logic, guided by data and powered by curiosity."
— Your Course Lead

Click Next to start with Module 1: ML Foundations in Healthcare