Foundations of Vector Database

  • BeginnerLevel

  • 133+Students Enrolled

  • 40 MinsDuration

  • 4.7Average Rating

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About this Course

  • Build strong Vector Database Fundamentals as you learn what a vector database is, how it works, and why it powers modern AI search in this practical Foundations of vector Database.
  • Understand embeddings, vectors, and similarity search in the context of modern Vector Databases, exploring cosine similarity, word vs sentence embeddings, and Python workflows.
  • Get hands-on with FAISS & Chroma as you create, index, and query your own Vector Database, learning to run fast, accurate semantic search pipelines that are ready to plug into AI.
  • Build a mini RAG MVP that connects LLMs with Vector Databases so you can chat with your documents—an ideal next step if you’re exploring an agentic AI course online.

Learning Outcomes

Understand vector basics

Learn what vector databases are, why they matter, and key use cases.

Use embeddings in search

Work with embeddings, similarity search, and Python-based workflows.

Build a mini RAG system

Build a mini RAG MVP using FAISS, Chroma, and document chat flows.

Who Should Enroll

  • Data scientists, ML engineers, AI learners who want an intro to vector databases, embeddings, and similarity search.
  • Developers building RAG apps or chatbots who need skills with embeddings, FAISS, Chroma, and basic semantic search.
  • Students and beginners in AI or data science who want a hands-on start with vectors, embeddings, and a simple RAG MVP.

Course Curriculum

Learn vector database fundamentals, embeddings, and similarity search, then go hands-on with FAISS and Chroma to build a mini RAG MVP that lets you chat with your own documents.

tools

  1. 1. Course Introduction

  2. 2. What is a Vector Database?

  3. 3. Embeddings 101

  4. 4. Similarity Search Basics

  1. 1. Hands-On with FAISS

  2. 2. Hands-On with Chroma

  3. 3. Mini RAG Project

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Huzefa Lohawala

Machine Learning Engineer- Avalara

Huzefa Lohawala is a Data Scientist with 5+ years of experience, specializing in NLP and end-to-end ML products. As an educator with 4+ years and 1,000+ training hours, he has trained 500+ learners in data science and analytics.

Get this Course Now

With this course you’ll get

  • 40 Mins

    Duration

  • Huzefa Lohawala

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

A vector database stores high-dimensional vectors generated from text, images, or other data. It enables fast similarity search, which is crucial for applications like semantic search, recommendations, and Retrieval-Augmented Generation (RAG) in modern AI systems.

Traditional databases store structured rows and columns, ideal for exact matches and filters. A vector database stores embeddings and focuses on similarity search, helping AI systems find “meaningfully similar” items rather than just exact keyword or value matches.

Embeddings are numerical representations of data, such as text or images, in a high-dimensional vector space. Vector databases store and index these embeddings, enabling efficient similarity search and powering use cases like semantic search and intelligent chatbots.

Similarity search lets you retrieve the most relevant documents or data points based on meaning, not just keywords. This is essential for RAG systems, where an LLM needs high-quality, contextually similar information to generate accurate, grounded responses.

FAISS and Chroma are popular tools for building vector databases and performing vector search. They handle indexing, storing, and querying embeddings efficiently, making them ideal for learning the foundations of vector databases and RAG systems.

Vector databases power semantic search, personalized recommendations, document search, chat-with-your-data apps, and Retrieval-Augmented Generation (RAG). Understanding what is a vector database and how it works helps you design smarter, context-aware AI solutions.

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