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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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Profiling and Speeding Up Python

Profiling and Speeding Up Python

Find where your Python data and AI code really spends time and memory, then fix the parts that matter.AdvancedPython for AI4.7(3)78 lessonsSample
Felix Brandt$10
Evaluating Retrieval Quality

Evaluating Retrieval Quality

Measure whether your retrieval finds the right passages with real queries, relevance labels and the right metrics.AdvancedEvaluation and testing4.7(3)75 lessonsSample
Fumiko Arai$11
Evaluating an AI Feature Before Launch

Evaluating an AI Feature Before Launch

Build an evaluation set, grade outputs reliably and set launch gates your team agrees onAdvancedAI for product and operations4.7(3)70 lessonsSample
Sanjana Rao$14
Building Small Internal Tools with an AI Assistant

Building Small Internal Tools with an AI Assistant

Build simple internal apps, forms and dashboards with AI help that colleagues can safely rely onAll levelsAI automation4.3(3)69 lessonsSample
Jude Mensah$5
Reviewing AI Support Answers for Quality

Reviewing AI Support Answers for Quality

Build a sampling and rubric process to judge AI support answers and catch failures earlyAdvancedAI for product and operations4.7(3)67 lessonsSample
Tariq Haddad$12
When Not to Automate

When Not to Automate

Recognise the tasks where automation costs more than it saves or puts people at riskAll levelsAI automation4.7(3)67 lessonsSample
Isabela DuarteFree
RAG Explained for Non Engineers

RAG Explained for Non Engineers

Understand how AI assistants answer from company documents, what can go wrong and what to ask your team.All levelsRAG and search4.7(3)66 lessonsSample
Emeka NwosuFree
Automation for Operations Teams

Automation for Operations Teams

Build a shared, well governed set of automations across an operations team instead of scattered one offsAll levelsAI automation4.3(3)65 lessonsSample
Hamza Qureshi$6
Model Graded Evals and Their Pitfalls

Model Graded Evals and Their Pitfalls

Use language models as graders without fooling yourself: biases, validation against people and safeguards.AdvancedEvaluation and testing4.3(4)60 lessonsSample
Gonzalo Ibarra$11
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 lessonsSample
Gabriela 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 lessonsSample
Farid 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 lessonsSample
Neha VaradanFree