Machine Learning Foundations

This course is a practical introduction to machine learning. It is designed for learners at a intermediate level who want to move from scattered knowledge to a clear, usable understanding they can apply straight away.

Across 5 lessons you will work through the core ideas step by step, with short exercises after each section so you can check your understanding as you go rather than at the very end.

What you’ll cover:

• Supervised Versus Unsupervised Learning
• Features, Labels and Splits
• Training Your First Model
• Evaluating Model Performance
• Overfitting and Regularisation

By the end you should be comfortable enough with machine learning to keep learning on your own, and to apply what you have covered in a real data context. No prior specialist background is assumed beyond basic familiarity with the subject area.