Vector Databases for Modern AI (Python / Taught in Arabic)

Vector databases have become a core building block of modern AI systems — powering semantic search, recommendation engines, multimodal applications, and retrieval-augmented generation (RAG). As AI systems move beyond keywords and structured data, understanding how to store, index, and search high-dimensional embeddings is no longer optional — it’s essential.In this course, you’ll learn how vector databases work from the ground up, combining strong conceptual foundations with hands-on, practical implementation using Python - from first principles to production-ready systems. We start by building strong intuition around vectors, embeddings, and similarity, then progressively move into indexing techniques, system design, and real-world implementations used in modern AI applications.This is a hands-on, end-to-end course. You won’t just learn concepts — you’ll implement vector indexes, build a vector database, and work with a production-grade vector database (Pinecone) to design scalable AI systems.Why take this courseBy the end of this course, you’ll have a deep, practical understanding of vector databases — not just how to use them, but how they work internally, from low-level indexing concepts to production-ready implementations. You’ll gain hands-on experience building and using vector databases with tools like Pinecone, and learn how they power real-world AI systems.This course gives you a strong foundation to design, build, and scale intelligent, production-ready AI applications that rely on semantic search, multimodal retrieval, and retrieval-augmented generation (RAG).