An Urdu-English progression from machine-learning and NLP vocabulary through pretrained models, prompting, and a citation-aware research-agent pattern.
Build a conceptual path from machine learning and NLP to large language models, pretrained model tooling, prompting, and a Python research-agent capstone. The sequence is presented in Urdu-English instruction and should be used as a foundation for experiments, not as a promise that a model API, library, or hosted service will remain unchanged. Assignments emphasize source handling, structured outputs, and human review.
Explain core machine-learning, NLP, and LLM concepts in an Urdu-English technical vocabulary. Describe why pretrained models and model hubs are useful, along with their limits. Design prompts with explicit context, output structure, and verification steps. Specify a small research agent that uses tools, citations, and reviewable outputs.
Basic Python and file-handling skills, willingness to read Urdu-English technical instruction, and familiarity with simple prompts. No prior agent framework experience is required.
Learners with basic familiarity who are ready to build practical, independent skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
7 lessons · Intermediate · Full course.
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