Creating a Movie Recommendation Backend using Codex and FastAPI

Overview

Build a movie recommendation backend with Codex by cleaning and preparing real-world datasets, designing a robust database and API, and implementing a test-driven recommendation system that delivers reliable, data-driven results.

What you'll learn

Use Codex to explore, clean, and enrich real-world movie data with Python
Configure Codex using initialization commands, AGENTS.md, and Model Context Protocols (MCP)
Design PostgreSQL tables and build scripts that load and validate dataset imports
Develop repository layers, database connections, and FastAPI endpoints
Implement movie recommendation features using an AI-assisted test-driven approach
Verify backend reliability with automated tests and smoke checks using pytest

Advantages and disadvantages

Advantages

University courses you can audit for free, with lectures, readings and practice quizzes.
A verified certificate from the university if you pay for it.
Self-paced: start any time.

Disadvantages

Graded assignments and the certificate need the paid track.
Audit access can expire a few weeks after the course ends.
Learning is free, but the certificate costs money.
Some parts (graded work, certificate) are paid.

Some points apply to every course of this kind; see how we rank.

Free to audit

Free: Choose "Audit this course" when you enrol: lectures, readings and practice are free.
Paid: Graded assignments and the verified certificate (Certificate $45). Audit access may end after the course closes.

Before you start

Beginner: Database Schema and Data Storage (Codex, FastAPI, PostgreSQL, pytest, Python) Beginner: Server-Side Programming (Codex, FastAPI, PostgreSQL, pytest, Python)

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