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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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Inference optimisation for serving at scale

Inference optimisation for serving at scale

Serve language models faster and cheaper by understanding prefill, decode, batching and cachingAdvancedMLOps and deployment4.7(3)83 lessonsSampleMagnus Eriksen$15 Communicating Results to Non Experts

Communicating Results to Non Experts

Turn analysis into clear sentences, simple charts and decisions people act onAll levelsData science and statistics4.3(4)82 lessonsSampleKojo Amankwah$5 Which Model When: Choosing an Algorithm

Which Model When: Choosing an Algorithm

Pick a sensible model family for your data, constraints and goals, then test it fairlyAll levelsMachine learning4.3(3)74 lessonsSampleKenta Arai$7 Machine translation: how it works and fails

Machine translation: how it works and fails

Understand how machines translate, judge translation quality and know when a human translator is neededAll levelsNLP4.3(3)74 lessonsSampleNadia Haddad$6 End to End Tabular ML Project

End to End Tabular ML Project

Take one tabular dataset from question to tested model to clear write upAll levelsData science and statistics4.3(4)72 lessonsSampleLukas Brenner$9 Debugging neural network training

Debugging neural network training

A systematic method for finding why a model will not train, diverges or quietly underperformsAdvancedDeep learning4.5(4)66 lessonsSampleNikolai Sorin$10 Planning the cost of a training run

Planning the cost of a training run

Estimate compute, time, memory and budget for a training or fine tuning run before you spendAll levelsFine tuning and training4.3(3)65 lessonsSampleMagnus Eriksen$7 Survey Data: Design to Analysis

Survey Data: Design to Analysis

Write better questions, weight responses sensibly and report survey results honestlyAll levelsData science and statistics3.7(3)65 lessonsSampleLina Khoury$6 Vision language models: what they see

Vision language models: what they see

Know how AI models read images, what they get right and wrong, and how to check their answersAll levelsComputer vision4.7(3)61 lessonsSampleNoor Siddiqui$6 Preference tuning: RLHF, DPO and related methods

Preference tuning: RLHF, DPO and related methods

Understand how models are tuned on human preferences, from reward models to direct preference lossesAdvancedFine tuning and training4.7(3)60 lessonsSampleNeha Varadan$14 The transformer, block by block

The transformer, block by block

Trace a token through every part of a transformer and count where the parameters liveAdvancedDeep learning4.7(3)60 lessonsSampleNikolai Sorin$12 Gradient Boosting for Tabular Data

Gradient Boosting for Tabular Data

Understand, tune and debug boosted trees, the workhorse of structured data problemsAdvancedMachine learning4.7(3)60 lessonsSampleKavya Raman$12