Ilyass Ezzam
(KanekiEzz)•AI Engineer & Full Stack Developer
(KanekiEzz)•AI Engineer & Full Stack Developer
(KanekiEzz)•AI Engineer & Full Stack Developer
The top repositories I've been working on, sorted by open-source traction.
Full-stack machine learning project for predicting building energy consumption using FastAPI, Next.js, and reproducible Jupyter notebooks.
Track your journey through 42 Cursus with milestones, projects, and achievements. Stay motivated and see how far you've come! 🚀
AI-powered Question Answering system for the 1337 Coding School handbook using FastAPI, LangChain, Ollama, and RAG.
A machine learning project that implements the Random Forest algorithm completely from scratch using Python and NumPy, including decision tree construction, bootstrapping, random feature selection, training, and prediction without external ML libraries.
Designed for absolute beginners, this Introduction to Pandas study plan covers the very basic topics of Pandas. Before starting, you should understand basic Python and common data structures such as syntax and data types.
A lightweight Python web framework built from scratch to understand how modern frameworks like FastAPI work under the hood. It includes routing, middleware, request/response handling, exceptions, and automatic API documentation support.
A Cub3D raycasting project parser and basic renderer built in C using the MiniLibX (mlx) graphics library. This project parses .cub map files, validates map data and textures, and sets up the data structures required for a raycasting engine similar to the classic Wolfenstein 3D style.