Vector Search and Embeddings

Google Cloud via Coursera

Coursera based on 17 ratings

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Overview

"Vector Search and Embeddings" is a foundational course designed to introduce learners to the core concepts of semantic search powered by vector representations and embeddings. As traditional keyword-based search becomes less effective in handling complex queries, vector search offers a modern, intelligent solution by using embeddings to capture the semantic meaning of text, images, or other data types. This course explains how embeddings convert data into high-dimensional vectors that can be compared based on similarity rather than exact matches. You'll explore...

Syllabus

Vector Search and Embeddings

This course introduces Vertex AI Vector Search and describes how it can be used to build a search application with large language model (LLM) APIs for embeddings. The course consists of conceptual lessons on vector search and text embeddings, practical demos on how to build vector search on Vertex AI, and a hands-on lab.