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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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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 Word and sentence embeddings

Word and sentence embeddings

Understand how meaning becomes vectors, compute similarity yourself and choose embeddings wiselyBeginnerNLP4.7(3)74 lessonsSampleNadia Haddad$5 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 Maths Refresher for Returning Adults

Maths Refresher for Returning Adults

Rebuild the school maths you need for data and AI, calmly and at your own paceBeginnerMath for AI4.7(3)73 lessonsSampleKatarzyna WolskaFree Regression Metrics and Residual Analysis

Regression Metrics and Residual Analysis

Choose between MAE, RMSE and friends, then read residuals to find what your model missesBeginnerMachine learning4.0(3)73 lessonsSampleLukas Brenner$4 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 Feature Engineering for Tabular Data

Feature Engineering for Tabular Data

Create features that help models learn, without leaking the answerIntermediateMachine learning4.3(3)72 lessonsSampleLin Zhao$7 Image embeddings and visual search

Image embeddings and visual search

Build image similarity and text to image search, and measure whether results are actually relevantIntermediateComputer vision4.7(3)71 lessonsSampleNoor Siddiqui$7 Logs and Exponentials for ML

Logs and Exponentials for ML

Get comfortable with exponents and logarithms, the quiet workhorses of machine learning formulasBeginnerMath for AI4.7(3)69 lessonsSampleLeandro FerrazFree Attention mechanisms, step by step

Attention mechanisms, step by step

Compute attention by hand, then understand masks, heads, KV caching and efficient variantsIntermediateDeep learning4.3(4)66 lessonsSampleNikolai Sorin$8 LoRA and parameter efficient fine tuning

LoRA and parameter efficient fine tuning

Fine tune large models on modest hardware by training small low rank adapters instead of every weightIntermediateFine tuning and training4.7(3)66 lessonsSampleNeha Varadan$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