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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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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 Confidence Intervals, Clearly

Confidence Intervals, Clearly

Build, read and explain confidence intervals without the usual misunderstandingsIntermediateData science and statistics4.3(3)66 lessonsSampleLina Khoury$6 Evaluating a model after fine tuning

Evaluating a model after fine tuning

Prove a tuned model is actually better, on your task and everywhere else it mattersIntermediateEvaluation and testing4.7(3)65 lessonsSampleNeha Varadan$8 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 Model Interpretability and Explanations

Model Interpretability and Explanations

Explain what drives a model's predictions, globally and case by case, without overclaimingIntermediateMachine learning4.3(3)64 lessonsSampleKojo Amankwah$7 Designing Experiments: Power, Units and Blocking

Designing Experiments: Power, Units and Blocking

Plan experiments that can actually detect the effects you care aboutIntermediateData science and statistics4.7(3)64 lessonsSampleKojo Amankwah$7 Maximum Likelihood, Step by Step

Maximum Likelihood, Step by Step

Derive estimates by maximising likelihood and see why common losses are likelihoods in disguiseIntermediateMath for AI4.7(3)63 lessonsSampleKenta Arai$7 Time Series Forecasting Basics

Time Series Forecasting Basics

Forecast demand, traffic or sales with honest baselines, proper backtests and useful intervalsIntermediateData science and statistics4.3(3)62 lessonsSampleKenta Arai$8 SQL Window Functions and Cohorts

SQL Window Functions and Cohorts

Rank, compare and track users over time with window functions and clean CTEsIntermediateData science and statistics4.7(3)61 lessonsSampleLin Zhao$6 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 Fine tune, prompt or retrieve?

Fine tune, prompt or retrieve?

Choose between prompting, retrieval and fine tuning for your problem, and know whyAll levelsFine tuning and training4.7(3)59 lessonsSampleNeha VaradanFree