Machine Learning

Supervised and unsupervised learning, model evaluation, and an introduction to neural networks.

Why learn machine learning?

Machine learning is what powers recommendation engines, fraud detection, image recognition and much of what people now call "AI." This course focuses on understanding how these models actually work, and building them yourself.

A specialisation, not a starting point — this course goes deeper into the modelling side of data science.
High demand, growing fast — ML and AI roles are among the fastest-growing in tech right now.
Project-based — you build and evaluate real models, not just read about the theory.

Before you start: we recommend our Data Science course first, or equivalent comfort with Python and basic statistics.

Machine learning syllabus

Six modules, from core algorithms to a real end-to-end project.

Module 01

Machine learning foundations

Types of learning: supervised, unsupervised, reinforcement
The end-to-end ML workflow

Module 02

Supervised learning

Regression algorithms
Classification algorithms
Decision trees and ensemble basics

Module 03

Unsupervised learning

Clustering (k-means)
Dimensionality reduction basics

Module 04

Model evaluation & tuning

Train/test split and cross-validation
Accuracy, precision, recall and other metrics
Avoiding overfitting

Module 05

Introduction to neural networks

How a neural network works, conceptually
A first model with a deep-learning library

Module 06

Real-world project

Building an end-to-end ML pipeline on a real dataset

New to data entirely?

Start with Data Analytics or Data Science first — this course assumes you're already comfortable with Python and basic statistics.