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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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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 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 Descriptive Statistics, First Steps

Descriptive Statistics, First Steps

Summarise any set of numbers honestly with averages, spread and shapeBeginnerData science and statistics4.7(3)65 lessonsSampleLina KhouryFree Your first fine tune, start to finish

Your first fine tune, start to finish

Run one small, honest fine tuning project end to end and decide whether it was worth doingBeginnerFine tuning and training4.7(3)64 lessonsSampleNeha Varadan$6 Image classification, first principles

Image classification, first principles

Plan, train and honestly evaluate an image classifier, from defining classes to studying its mistakesBeginnerComputer vision4.7(3)64 lessonsSampleNoor SiddiquiFree MLOps for a team of one

MLOps for a team of one

Put a model into use responsibly with the few practices that matter when you work aloneBeginnerMLOps and deployment4.7(3)62 lessonsSampleMalik BrennanFree Derivatives and Gradients from Scratch

Derivatives and Gradients from Scratch

Understand rates of change, derivatives and gradients as the compass that guides learningBeginnerMath for AI4.7(3)62 lessonsSampleLeandro Ferraz$4 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 Correlation Is Not Causation

Correlation Is Not Causation

Learn the everyday ways correlations mislead and what real causal evidence looks likeBeginnerData science and statistics4.5(4)60 lessonsSampleKojo AmankwahFree Framing a Problem for Machine Learning

Framing a Problem for Machine Learning

Turn a vague business wish into a clear prediction task, or learn that it does not need ML at allBeginnerMachine learning4.7(3)60 lessonsSampleKavya RamanFree A/B Testing for Beginners

A/B Testing for Beginners

Run your first A/B test properly, from hypothesis to a decision you can defendBeginnerData science and statistics4.7(3)60 lessonsSampleKojo Amankwah$4