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Vector Databases: From Embeddings to Applications

This course teaches how data vectorization and vector databases enable context-based search over keyword matching, multimodal data search, enhance recommendation systems, and power LLMs.

4.5

19 Lessons

3h 15min

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LEARNING OBJECTIVES

An understanding of vector databases and their significance in modern-world AI applications
An understanding of embeddings in vector databases
The ability to build efficient, intelligent applications using the power of vector databases and embeddings
Hands-on experience generating unimodal and multimodal embeddings
The ability to find similar embeddings within a vector space
Hands-on experience using the Chroma vector database
Hands-on experience building unimodal and multimodal semantic search applications
Hands-on experience building embeddings and vector database-powered music recommendation system
An understanding of HNSW, the most widely used vector indexing technique used in vector databases for performance optimization

Learning Roadmap

19 Lessons4 Quizzes

Before Getting Started

Get familiar with vector databases, embeddings, and their applications in AI systems.

Introduction to the Course

Getting Started with Vector Databases and Embeddings

Look at the essentials of vector databases, embeddings, similarity measures, and multimodal integration.

Introduction to Vector Databases and Embeddings

Methods for Measuring Similarity between Embeddings

Embedding Models for Different Data Types

Generating Text Data Embeddings with BERT

Generating Image, Video, and Audio Embeddings

Multimodal Embeddings

Embeddings vs. Fine-Tuning

Quiz: Getting Started with Vector Databases and Embeddings

Working with Vector Databases

5 Lessons

Work your way through leveraging open-source vector databases, hands-on practices, and optimization techniques.

Developing a Music Recommendation System

4 Lessons

Apply your skills to develop and optimize a music recommendation system using vector databases.

Wrapping Up

Take a closer look at vector databases, embedding techniques, and their practical applications.

Conclusion

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

ABOUT THIS COURSE

Vector databases transform how we search, analyze, and recommend data in today’s AI-driven world. These databases are at the heart of modern applications like semantic search, multimodal search, recommendation systems, and retrieval augmented generation (RAG) for large language models. By using embeddings, which are numerical representations that capture the meaning of data, vector databases allow us to find similar information quickly and accurately, even across vast datasets. This makes them essential for building intelligent systems that understand data beyond simple keyword matching. In this vector databases course, you’ll learn to generate embeddings for various data types and use vector databases to store and query them. Using the power of embeddings and vector databases, you’ll build semantic search apps, recommendation systems, and multimodal search solutions. After completing this course, you’ll have the skills to determine when and how to effectively apply vector databases to different projects.

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