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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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Choosing the Right Model for a Task

Choosing the Right Model for a Task

Pick a model by testing it on your own task, weighing quality, speed, cost, context and data handling.All levelsBuilding with LLM APIs4.3(3)60 lessonsSampleGabriela Sousa$5 Fallbacks for Provider Outages and Errors

Fallbacks for Provider Outages and Errors

Keep LLM features working through outages, overloads and slowdowns with deadlines, breakers and fallbacks.AdvancedBuilding with LLM APIs4.7(3)60 lessonsSampleFarid Haddad$11 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 Tool Calling: Let the Model Use Your Code

Tool Calling: Let the Model Use Your Code

Define tools, run the call and result loop safely, and get a model to use your functions correctly.IntermediateBuilding with LLM APIs4.7(3)59 lessonsSampleGreta Lindqvist$8 Your First RAG Pipeline, End to End

Your First RAG Pipeline, End to End

Build a small retrieval augmented chatbot over your own documents, step by step, and see where it fails.BeginnerBuilding with LLM APIs4.7(3)59 lessonsSampleEmeka NwosuFree Metadata Filters and Permission Aware Retrieval

Metadata Filters and Permission Aware Retrieval

Filter retrieval by date, product or tenant and make sure users only ever retrieve what they may see.AdvancedRAG and search4.7(3)57 lessonsSampleEmeka Nwosu$11 Token Counting and Cost Control

Token Counting and Cost Control

Count tokens, predict what a feature will cost and cut spending without hurting answer quality.BeginnerBuilding with LLM APIs4.3(3)57 lessonsSampleFarid Haddad$4 Pair Programming Habits with an AI Assistant

Pair Programming Habits with an AI Assistant

Work with a coding assistant like a good pair: small steps, clear roles and code you understandBeginnerAI coding assistants4.3(3)56 lessonsSampleJude Mensah$4 Guardrails and Permissions for AI Agents

Guardrails and Permissions for AI Agents

Decide what your agent may read, write and spend, and enforce those limits in code rather than promptsIntermediateAI agents4.7(3)56 lessonsSampleIngrid Solberg$8 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 Debugging a RAG App That Answers Badly

Debugging a RAG App That Answers Badly

Trace each bad RAG answer to its real cause, from missing documents to ignored passages, and fix the most common.IntermediateRAG and search4.7(3)56 lessonsSampleFumiko Arai$8 Cleaning Text with Python and Regex

Cleaning Text with Python and Regex

Clean and normalise messy text for AI pipelines with string methods, Unicode care and readable regex.BeginnerPython for AI4.7(3)54 lessonsSampleEfua Mensah$4