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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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Unit Tests for Python AI Code

Unit Tests for Python AI Code

Test the deterministic parts of LLM apps properly, with fake model clients instead of flaky live calls.IntermediateEvaluation and testing4.3(3)61 lessonsSampleFelix Brandt$6 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 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 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 Data Analysis Agents for Spreadsheets and SQL

Data Analysis Agents for Spreadsheets and SQL

Use or build agents that query data, run analysis and explain results you can actually verifyIntermediateAI agents4.3(3)53 lessonsSampleJamal Haddad$8 Rate Limits, Retries and Backoff

Rate Limits, Retries and Backoff

Handle 429s and overloads gracefully with backoff, jitter, client side pacing and retries that never cause storms.IntermediateBuilding with LLM APIs4.7(3)53 lessonsSampleFarid Haddad$7 Type Hints and Project Structure

Type Hints and Project Structure

Organise a growing AI codebase with clear modules, type hints and data models others can follow.IntermediatePython for AI4.5(2)52 lessonsSampleFelix Brandt$6 Designing Tools an Agent Can Actually Use

Designing Tools an Agent Can Actually Use

Write tool names, descriptions, inputs and error messages that make agents choose and call tools correctlyIntermediateAI agents4.7(3)52 lessonsSampleHiroshi Tanabe$7 Cost and Latency Budgets for LLM Features

Cost and Latency Budgets for LLM Features

Set cost and speed targets for an LLM feature, measure them honestly and trade them against quality.All levelsBuilding with LLM APIs4.5(2)52 lessonsSampleFarid Haddad$8