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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.

Intermediate

19 Lessons

3h 15min

Certificate of Completion

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.
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This course includes

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Course Overview
What You'll Learn
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Course Overview

Vector databases are transforming 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 leveraging 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 e...Show More
Vector databases are transforming how we search, analyze, and recommend data in today’s AI-driven world. These databases are at ...Show More

What You'll Learn

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
Hand-on experience building unimodal and multimodal semantic search applications
Hand-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
An understanding of vector databases and their significance in modern-world AI applications

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Course Content

1.

Before Getting Started

1 Lessons

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

3.

Working with Vector Databases

5 Lessons

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

4.

Developing a Music Recommendation System

4 Lessons

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

5.

Wrapping Up

1 Lessons

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

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