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DeepLearning.AI

Building Applications with Vector Databases

  • up to 1 hour
  • Beginner

Learn to create six exciting applications of vector databases and implement them using Pinecone. This course covers a range of applications from semantic search to anomaly detection, equipping you with the skills to build advanced applications with minimal coding.

  • Semantic search
  • Retrieval augmented generation (RAG)
  • Recommender systems
  • Hybrid search
  • Facial similarity

Overview

In this course, you will explore the implementation of six applications using vector databases. You will learn how to create a semantic search tool, enhance LLM applications with RAG, develop a recommender system, build a hybrid search app, create a facial similarity app, and implement an anomaly detection app. By the end of the course, you will have new ideas for building applications with any vector database.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Beginner Python Developers

Anyone with beginner Python knowledge who wants to learn applications of vector databases.

Machine Learning Enthusiasts

Individuals with basic machine learning knowledge looking to expand their skills in vector databases.

Large Language Model Users

People familiar with large language models who want to enhance their applications using vector databases.

This course offers key benefits such as learning to build advanced applications with vector databases and Pinecone. It covers essential topics like semantic search, RAG, and anomaly detection, making it ideal for beginners and professionals looking to enhance their skills. By taking this course, learners can advance their careers and achieve their goals in the field of AI and machine learning.

Pre-Requisites

1 / 3

  • Beginner Python knowledge

  • Basic machine learning understanding

  • Familiarity with large language models

What will you learn?

Semantic Search
Create a search tool that goes beyond keyword matching, focusing on the meaning of content for efficient text-based searches on a user Q/A dataset.
Retrieval Augmented Generation (RAG)
Enhance your LLM applications by incorporating content from sources the model wasn’t trained on, like answering questions using the Wikipedia dataset.
Recommender System
Develop a system that combines semantic search and RAG to recommend topics, and demonstrate it with a news article dataset.
Hybrid Search
Build an application that finds items using both images and descriptive text, using an eCommerce dataset as an example.
Facial Similarity
Create an app to compare facial features, using a database of public figures to determine the likeness between them.
Anomaly Detection
Learn how to build an anomaly detection app that identifies unusual patterns in network communication logs.

Meet your instructor

  • Tim Tully

    Partner, Menlo Ventures

    Tim Tully has served as Splunk’s Senior Vice President and Chief Technology Officer since 2018, and previously joined Splunk as Chief Technology Officer in 2017. Before Splunk, Tim spent 14 years in various roles at Yahoo! Inc., including Vice President, Engineering; Distinguished Engineer and Chief Data Architect.

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