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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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NLP Clear all

Named entity recognition in practice

Named entity recognition in practice

Extract people, places, organisations and custom entities from text, and evaluate the results properlyIntermediateNLP4.7(3)79 lessonsSampleNadia Haddad$7 Text classification from baseline to transformer

Text classification from baseline to transformer

Build text classifiers step by step, starting with a strong simple baseline and honest metricsBeginnerMachine learning4.3(4)74 lessonsSampleNadia Haddad$5 Word and sentence embeddings

Word and sentence embeddings

Understand how meaning becomes vectors, compute similarity yourself and choose embeddings wiselyBeginnerNLP4.7(3)74 lessonsSampleNadia Haddad$5 Machine translation: how it works and fails

Machine translation: how it works and fails

Understand how machines translate, judge translation quality and know when a human translator is neededAll levelsNLP4.3(3)74 lessonsSampleNadia Haddad$6 Sentiment analysis done carefully

Sentiment analysis done carefully

Measure opinions in text without fooling yourself about sarcasm, mixed views or skewed averagesBeginnerNLP4.3(3)59 lessonsSampleNadia Haddad$4 Question answering, from extractive to generative

Question answering, from extractive to generative

Understand how QA systems find, read and generate answers, and how to tell when they should abstainIntermediateNLP4.7(3)56 lessonsSampleMateo Rojas$7 Tokenisation: how text becomes numbers

Tokenisation: how text becomes numbers

See how models split text into tokens, why it matters for cost and context, and where it trips upBeginnerHow language models work4.7(3)56 lessonsSampleNadia HaddadFree Speech to text pipelines

Speech to text pipelines

Turn recordings into accurate, timestamped transcripts and understand where speech recognition failsBeginnerAudio and voice AI4.7(3)54 lessonsSampleNadia Haddad$5 Summarisation systems and their failure modes

Summarisation systems and their failure modes

Build and judge summaries that stay faithful to the source, from short notes to long reportsAll levelsNLP4.7(3)46 lessonsSampleMateo Rojas$6 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 RNNs, LSTMs and why transformers took over

RNNs, LSTMs and why transformers took over

Understand recurrent networks, their gates and limits, and the real reasons attention replaced themIntermediateDeep learning4.5(2)32 lessonsSampleNikolai Sorin$7 Evaluating generated text

Evaluating generated text

Measure the quality of generated text with metrics, people and model judges, and know each one's limitsIntermediateEvaluation and testingNewMateo Rojas$8