Vector Databases Masterclass: From Zero to Advanced
- beginner
- 1h 40m
- 7 Video Lessons
Learn Vector Databases from scratch with hands-on examples. Understand vectors, embeddings, tokenization, semantic search, similarity algorithms, and build a real-world project using Google Gemini Embeddings and ChromaDB—no prior knowledge required.
What You'll Learn
- Understand what Vector Databases are, why they exist, and how they power modern AI applications.
- Learn vectors, vector similarity, tokenization, and embeddings from first principles.
- Generate vector embeddings using the Google Gemini Embedding model.
- Explore the internal architecture of Vector Databases, including indexing, storage, and similarity search algorithms.
- Build a complete semantic search application using ChromaDB and real vector embeddings.
- Gain the practical knowledge needed to develop AI agents, RAG systems, recommendation engines, and enterprise search applications.
About This Course
Vector Databases have become one of the most important technologies powering modern AI applications. Whether you're building AI agents, RAG applications, recommendation engines, semantic search systems, or enterprise knowledge bases, understanding Vector Databases is no longer optional—it's an essential skill for software engineers, AI engineers, and machine learning practitioners.
In this comprehensive course, you'll learn Vector Databases from the ground up. No prior knowledge is required. Every concept is explained step by step, with intuitive visual explanations and practical demonstrations.
We'll begin by understanding what Vector Databases are, why they were introduced, and why traditional databases struggle with semantic search. You'll then learn the mathematical foundations behind vectors and vector similarity before exploring tokenization and embeddings—the building blocks of modern AI systems.
Next, you'll generate embeddings using Google's Gemini Embedding model and dive deep into the internal architecture of Vector Databases, including storage, indexing, similarity search, and scalability techniques used in production systems.
Finally, you'll build a complete hands-on project where you'll generate embeddings, store them inside ChromaDB, and perform semantic search, giving you a practical understanding of the entire vector search pipeline.
By the end of this course, you'll not only understand how Vector Databases work conceptually but also gain the practical skills required to build AI-powered applications that leverage semantic search and embeddings.
Whether you're a Software Engineer, AI Engineer, Machine Learning Engineer, Data Engineer, or simply curious about modern AI infrastructure, this course will give you the confidence to work with one of the most important technologies in today's AI ecosystem.