Semantic search with vector databases | Udemy


Semantic search with vector databases | Udemy
English | Size: 4.48 GB
Genre: eLearning

From theory to practical implementation with Pinecone and AI

What you’ll learn
Understand the fundamentals of semantic search and vector databases
Create and manage vector indexes with Pinecone
Implement text processing pipelines for vector databases
Develop advanced queries and AI assistants for vector search

Semantic search and vector databases are transforming the way we deal with large volumes of information, making searches more precise and contextualized. If you want to understand this innovative technology and apply it to your projects, this course is for you.

In this course, you will learn everything from the fundamental concepts of semantic search to the practical implementation of vector databases using Pinecone, one of the leading platforms for vector storage and retrieval. We will explore how to transform text into vectors, create efficient indexes and build intelligent queries to find relevant information quickly.

What will you learn?

Fundamentals of Semantic Search and Vector Databases – Understand how vectors represent meaning and context in searches.

Creating and Managing Vector Indexes – Configure and manipulate vector databases using Pinecone.

Text Processing for Embeddings – Extract data from documents, strategically split text, and generate high-quality embeddings.

Building Advanced Query – Learn how to retrieve information efficiently and accurately.

Developing an AI Assistant – Create a system that answers questions based on vector search.

Who is this course for?

This course is ideal for developers, data scientists, AI professionals, and students who want to deepen their knowledge of semantic search and vector databases. Whether you want to build recommendation systems, search engines, or intelligent chatbots, this course will provide you with the knowledge you need.

No prior experience with vector databases is required.

Who this course is for:

  • Developers and data scientists who want to learn about semantic search and vector databases.
  • AI and Machine Learning professionals interested in optimizing information retrieval using embeddings.
  • Students and researchers working in natural language processing (NLP) who need to store and query large volumes of data efficiently.
  • Entrepreneurs and technology enthusiasts who want to build intelligent assistants, chatbots, and personalized search engines.
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