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657 tutors in 31 topics, built by 75 teachers. Each one follows a lesson plan its teacher wrote.

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Foundations 89

All Foundations89 AI basics25 How language models work23 AI safety and ethics49

Everyday AI 91

All Everyday AI91 Prompting20 ChatGPT and Claude at work25 AI for writing29 AI for research and study33

Building with AI 162

All Building with AI162 Python for AI21 Building with LLM APIs30 RAG and search21 Evaluation and testing21 AI agents31 AI automation25 AI coding assistants30

Machine learning 149

All Machine learning149 Machine learning35 Data science and statistics30 Math for AI23 Deep learning28 NLP14 Computer vision14 Fine tuning and training12 MLOps and deployment18

Creative AI 77

All Creative AI77 Image and video AI58 Audio and voice AI12 AI for design33

AI at work 145

All AI at work145 AI for business34 AI for marketing and sales23 AI for product and operations28 AI for education28 AI for professionals29 AI careers18

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Monitoring models and catching drift

Monitoring models and catching drift

Notice when a live model starts getting worse, even before the true answers arriveIntermediateMLOps and deployment4.7(3)58 lessonsSampleMalik Brennan$9 Batching and caching for model inference

Batching and caching for model inference

Serve more requests on the same hardware by batching smartly and caching what can be reusedIntermediateMLOps and deployment4.7(3)57 lessonsSampleMagnus Eriksen$9 Data Science Interview Practice

Data Science Interview Practice

Practise statistics, ML, SQL and case questions with structured feedback on your answersAll levelsData science and statistics4.3(3)57 lessonsSampleKojo Amankwah$8 Optimisers: SGD, momentum and Adam

Optimisers: SGD, momentum and Adam

Choose and tune optimisers and learning rate schedules with understanding instead of guessworkIntermediateDeep learning4.7(3)56 lessonsSampleMira Okafor$8 Question answering, from extractive to generative

Question answering, from extractive to generative

Understand how QA systems find, read and generate answers, and how to tell when they should abstainIntermediateNLP4.7(3)56 lessonsSampleMateo Rojas$7 Reading Maths Notation in AI Papers

Reading Maths Notation in AI Papers

Translate the symbols in machine learning papers into plain words and small examplesAll levelsMath for AI4.7(3)55 lessonsSampleKatarzyna Wolska$6 Metric Definitions That Hold Up

Metric Definitions That Hold Up

Define metrics precisely so every dashboard and team means the same thingAll levelsData science and statistics4.3(3)54 lessonsSampleLin Zhao$6 Random Forests and Bagging

Random Forests and Bagging

Understand why averaging many trees works and how to tune a forest sensiblyIntermediateMachine learning4.3(3)54 lessonsSampleKavya Raman$6 Probability for Machine Learning

Probability for Machine Learning

Use random variables, conditional probability and distributions the way ML models doIntermediateMath for AI4.3(3)53 lessonsSampleKenta Arai$7 The Chain Rule and Backpropagation

The Chain Rule and Backpropagation

Compute gradients through a network by hand and see exactly what backpropagation doesIntermediateMath for AI4.7(3)52 lessonsSampleLeandro Ferraz$7 Incident response for ML systems

Incident response for ML systems

Detect, contain and learn from ML failures, from silent quality drops to harmful outputsAll levelsMLOps and deployment4.7(3)51 lessonsSampleMalik Brennan$7 GANs: generator versus discriminator

GANs: generator versus discriminator

Understand how adversarial training works, why it is unstable and where GANs still make senseIntermediateDeep learning4.0(3)50 lessonsSampleMateo Rojas$8