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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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How to read a deep learning paper

How to read a deep learning paper

Read papers in passes, find the real claim and judge the evidence behind itAll levelsAI for research and study4.7(3)41 lessonsSampleNikolai Sorin$6 Reinforcement learning basics

Reinforcement learning basics

Understand agents, rewards and policies, and compute a Q learning update yourselfBeginnerDeep learning4.7(3)41 lessonsSampleNikolai SorinFree Probability Intuition Without Formulas

Probability Intuition Without Formulas

Reason about chance, risk and test results using counts instead of equationsBeginnerData science and statistics4.7(3)40 lessonsSampleLina Khoury$3 Image segmentation: semantic, instance, panoptic

Image segmentation: semantic, instance, panoptic

Label every pixel correctly: understand the three kinds of segmentation, their models and metricsIntermediateComputer vision4.3(3)40 lessonsSampleNoor Siddiqui$8 Eigenvalues, Eigenvectors and PCA

Eigenvalues, Eigenvectors and PCA

Derive principal component analysis from eigenvectors and use it with full understandingAdvancedMachine learning4.7(3)39 lessonsSampleKatarzyna Wolska$11 Preparing a fine tuning dataset

Preparing a fine tuning dataset

Build a clean, consistent, legally sound dataset that teaches a model exactly what you intendBeginnerFine tuning and training4.7(3)39 lessonsSampleNeha Varadan$5 Which Maths Do You Need for ML?

Which Maths Do You Need for ML?

Build a realistic maths study plan matched to the kind of AI work you want to doAll levelsMath for AI4.3(3)36 lessonsSampleLeandro Ferraz$5 Sampling and Bias in Samples

Sampling and Bias in Samples

Judge whether a sample can speak for a population, and why bigger is not always betterBeginnerData science and statistics4.5(2)35 lessonsSampleLina Khoury$4 Cross Validation Without Fooling Yourself

Cross Validation Without Fooling Yourself

Use k fold, grouped, time series and nested cross validation correctlyIntermediateMachine learning4.5(2)34 lessonsSampleLukas Brenner$6 Reading Statistics in Research and News

Reading Statistics in Research and News

Judge a study or headline claim with a short list of sharp questionsAll levelsData science and statistics4.7(3)34 lessonsSampleLina Khoury$5 Clustering Without Guesswork

Clustering Without Guesswork

Group data with k-means, hierarchical clustering and DBSCAN, and check the groups are usefulBeginnerMachine learning4.7(3)33 lessonsSampleKatarzyna Wolska$4 RNNs, LSTMs and why transformers took over

RNNs, LSTMs and why transformers took over

Understand recurrent networks, their gates and limits, and the real reasons attention replaced themIntermediateDeep learning4.5(2)32 lessonsSampleNikolai Sorin$7