Creating a Movie Recommendation Backend using Codex and FastAPI
Software Engineering
3 courses
45 practices
6 hours
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.
Understanding and cleaning the movie dataset using Codex
4 lessons
16 practices
The learner will be given a csv with data about movies.
They will set up the environment, understand, clean and fill missing values using Codex and Python
Building the Movie Recommendation Base Backend using Codex
4 lessons
Course 3
Creating a Recommendation System using Codex
3 lessons
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16 practices
The learner will build the base backend by planning the database model, implementing Postgres tables and migrations, creating scripts to load and verify dataset imports, developing repository and endpoint layers, and adding tests/smoke checks to ensure reliable queries and stable behavior over time.
The learner will learn how a recommendation system works, and create useful endpoints with the implementation using a test-driven approach.
At the end of this course, the learner will get a fully working backend.
