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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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Deploying an open weights model

Deploying an open weights model

Choose, size, secure and run an open weights model in production, and compare its real costIntermediateMLOps and deployment4.7(3)46 lessonsSampleMagnus Eriksen$9 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 How language models are pretrained

How language models are pretrained

Understand the data, objective, scaling and stability work behind large language model pretrainingAdvancedDeep learning4.7(3)45 lessonsSampleMateo Rojas$13 Dimensionality Reduction in Practice

Dimensionality Reduction in Practice

Use PCA, t-SNE and UMAP well, and avoid reading too much into pretty plotsIntermediateMachine learning4.3(3)45 lessonsSampleKatarzyna Wolska$7 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 Hypothesis Tests and P Values Done Right

Hypothesis Tests and P Values Done Right

Run tests, read p values correctly and report effects that actually matterIntermediateData science and statistics4.7(3)45 lessonsSampleLina Khoury$7 Support Vector Machines Explained

Support Vector Machines Explained

See margins, support vectors and kernels clearly, then tune C and gamma with confidenceIntermediateMachine learning4.5(2)45 lessonsSampleKavya Raman$7 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 Diffusion models from noise to sample

Diffusion models from noise to sample

Understand how diffusion models learn to remove noise and how guidance and latents shape the resultAdvancedDeep learning4.7(3)44 lessonsSampleMateo Rojas$12 Data Leakage Detective

Data Leakage Detective

Find the hidden leaks that make models look brilliant in testing and fail in productionAdvancedMachine learning4.7(3)43 lessonsSampleLukas Brenner$10 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