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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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Numerical Stability: Softmax and Log-Sum-Exp

Numerical Stability: Softmax and Log-Sum-Exp

Stop overflows, underflows and NaNs by computing ML formulas the stable wayAdvancedMath for AINewLeandro Ferraz$10 Graph neural networks

Graph neural networks

Learn message passing on graphs and build models for nodes, edges and whole graphs without leakageAdvancedDeep learningNewNikolai Sorin$11 Synthetic data for training, used with care

Synthetic data for training, used with care

Generate training data with models where it helps, and filter, verify and document it properlyAll levelsFine tuning and trainingNewNeha Varadan$7 Causal Inference Basics

Causal Inference Basics

Estimate causal effects from observational data and state your assumptions out loudAdvancedData science and statisticsNewKojo Amankwah$12 Vision transformers in depth

Vision transformers in depth

Understand patch tokens, position embeddings, data needs and compute trade offs in vision transformersAdvancedComputer visionNewNoor Siddiqui$12 NLP for multilingual and low resource languages

NLP for multilingual and low resource languages

Build and evaluate language technology for languages that most datasets and models leave behindAll levelsNLPNewNadia Haddad$7 Catastrophic forgetting and how to limit it

Catastrophic forgetting and how to limit it

Measure what a model loses when you fine tune it, and use replay, regularisation and merging to limit itAdvancedDeep learningNewNeha Varadan$12 Topic discovery and text clustering

Topic discovery and text clustering

Find themes in large text collections and check that the topics you found are real and usefulIntermediateData science and statisticsNewNadia Haddad$7 On device and edge models

On device and edge models

Decide when a model should run on the phone or device itself, and make it fit and run wellBeginnerMLOps and deploymentNewMagnus Eriksen$6 Knowledge distillation: teaching a smaller model

Knowledge distillation: teaching a smaller model

Train compact student models from large teachers with soft targets, generated data and careful evaluationAdvancedDeep learningNewMagnus Eriksen$12 From Software Engineer to ML Engineer

From Software Engineer to ML Engineer

Use your engineering strengths to move into ML engineering with a focused plan for maths and MLAdvancedAI careersNewYohannes Tesfaye$11 k Nearest Neighbours, Built by Hand

k Nearest Neighbours, Built by Hand

Predict by similarity and learn why distance, scaling and k decide everythingBeginnerMachine learningNewKavya Raman$3