FastAPI Fundamentals: Routing, Pydantic Models, and Dependency Injection
Build real FastAPI endpoints: typed routing, Pydantic request and response models, dependency injection, and automatic docs.
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Build real FastAPI endpoints: typed routing, Pydantic request and response models, dependency injection, and automatic docs.
Understand asyncio and the event loop, then fan out API and LLM calls concurrently with httpx, gather, and a semaphore to bound load.
Use Pydantic v2 to validate data at the boundary: models, field constraints, custom validators, and clean serialization, the backbone of FastAPI and reliable LLM output.
Set up a fast, reproducible Python 3.14 project with uv, a src layout, Ruff, and mypy: the foundation for the FastAPI and LLM work ahead.
Python enables us to predict and analyze any given data using Linear regression. Linear Regression is one of the basic machine learning or statistical techniques created to solve complex problems.
To start working on Python you need to have Python installed on your PC. If you haven’t installed python. Go to the Python website and get it installed.