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AI & Machine Learning

AI fundamentals through deep learning, LLMs, and responsible AI — the entry point to the AI curriculum.

All categories · 5 courses

FreeAI & Machine Learning

AI Ethics & Responsible AI

A practical foundation for people who design, buy, or oversee AI systems. You will learn to spot bias and fairness trade-offs, protect personal data, explain model decisions, read risk-based rules such as the EU AI Act, and set up governance that teams can actually follow. Ten lessons with worked examples take about 12 hours.

11 lessons · Capstone Exam · Certificate

FreeAI & Machine Learning

Artificial Intelligence Fundamentals

A practical entry point for professionals and students who need a clear picture of artificial intelligence without writing production code. You will explain what AI is, how machine learning uses data, how training differs from inference, and where AI helps or fails in work and research. Ten lessons with worked examples take about 12 hours.

11 lessons · Capstone Exam · Certificate

FreeAI & Machine Learning

Deep Learning & Neural Networks

A practical intermediate course for analysts, engineers and managers who need to understand how modern neural networks learn. You will follow a signal from a single neuron through backpropagation, activations and optimisers, then into convolutional, recurrent and transformer models, and finish by planning a small vision project and a responsible training setup. Ten lessons with worked examples take about 14 hours.

11 lessons · Capstone Exam · Certificate

FreeAI & Machine Learning

Generative AI & Large Language Models

A practical course for professionals who use or specify large language models at work. You will learn how these models generate text, write and test prompts, ground answers with retrieval, compare adaptation options, design simple applications and agents, and judge risk before deployment. Ten lessons with worked examples take about 12 hours.

11 lessons · Capstone Exam · Certificate

FreeAI & Machine Learning

Machine Learning Essentials

A practical foundation for analysts, engineers and managers who want to build supervised models without a research background. You will follow a full workflow from questions and data checks through splits, regression and classification, regularisation, metrics, features, pipelines, model choice and a first step toward production. Ten lessons with worked examples take about 12 hours.

11 lessons · Capstone Exam · Certificate

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