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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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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 Eigenvalues, Eigenvectors and PCA

Eigenvalues, Eigenvectors and PCA

Derive principal component analysis from eigenvectors and use it with full understandingAdvancedMachine learning4.7(3)39 lessonsSampleKatarzyna Wolska$11 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 Reading Statistics in Research and News

Reading Statistics in Research and News

Judge a study or headline claim with a short list of sharp questionsAll levelsData science and statistics4.7(3)34 lessonsSampleLina Khoury$5 ML Engineering Interview Preparation

ML Engineering Interview Preparation

Practise ML fundamentals, coding, ML system design and behavioural rounds with realistic mock questionsAdvancedAI careers4.7(3)30 lessonsSampleYohannes Tesfaye$13 Initialisation and normalisation layers

Initialisation and normalisation layers

Understand how weight initialisation and normalisation keep deep networks trainableAdvancedDeep learningNewMira Okafor$11 Medical imaging AI: how it is built and checked

Medical imaging AI: how it is built and checked

Understand how imaging models are trained, validated and overseen, for education onlyAll levelsComputer visionNewMalik Brennan$8 Hyperparameter Tuning on a Budget

Hyperparameter Tuning on a Budget

Search smarter, spend less compute and avoid overfitting your validation setAdvancedMachine learningNewLukas Brenner$11 Information Theory and Cross Entropy

Information Theory and Cross Entropy

Understand entropy, cross entropy, KL divergence and perplexity from first principlesAdvancedMath for AINewKenta Arai$11 Self supervised and contrastive learning

Self supervised and contrastive learning

Learn how models build useful representations from unlabelled data, and how to test themAdvancedComputer visionNewMateo Rojas$11 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