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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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79 tutors

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Summarisation systems and their failure modes

Summarisation systems and their failure modes

Build and judge summaries that stay faithful to the source, from short notes to long reportsAll levelsNLP4.7(3)46 lessonsSampleMateo Rojas$6 Model cards and ML governance

Model cards and ML governance

Document models honestly and set up light, real governance that helps people make good decisionsBeginnerAI safety and ethics4.0(3)45 lessonsSampleMalik Brennan$5 Charts That Tell the Truth

Charts That Tell the Truth

Choose, build and critique charts that show data honestly and clearlyBeginnerData science and statistics4.7(3)45 lessonsSampleLin Zhao$4 Autoencoders and latent spaces

Autoencoders and latent spaces

Learn how networks compress data into a small code and rebuild it, and what that code is good forBeginnerDeep learning4.5(2)44 lessonsSampleMateo Rojas$5 Data augmentation for vision

Data augmentation for vision

Choose image augmentations that reflect real variation, keep labels correct and improve generalisationBeginnerComputer vision4.0(3)44 lessonsSampleNoor Siddiqui$4 Vectors and Matrices for AI

Vectors and Matrices for AI

Understand vectors and matrices as the language AI uses to store and transform dataBeginnerMath for AI4.7(3)43 lessonsSampleKatarzyna Wolska$4 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 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