Semantic Search Courses and Certifications

Building a Semantic Book Recommender with Python, OpenAI, LangChain, and Gradio

Build a semantic book recommender in Python using LLMs, vector search, classification, sentiment analysis, LangChain, and a Gradio dashboard.

• freeCodeCamp
• 2 hours 15 minutes
• Self-Paced
• Free Video

Introduction to Embeddings with the OpenAI API

3166 ratings at DataCamp

Enable Powerful AI Applications Embeddings allow us to represent text numerically, capturing the context and intent behind the text.

• DataCamp
• 3 hours
• Self-Paced
• Free Trial Available

Building AI Applications with Pinecone

2171 ratings at DataCamp

Unlock the Power of Embeddings with Pinecone's Vector Database In the introductory chapters, you'll delve into the fundamentals of Pinecone, understanding its core capabilities, benefits, and key concepts such as pods, indexes, and projects.

• DataCamp
• 3 hours
• Self-Paced
• Free Trial Available

Using Vector Search for Semantic Search

Build a semantic search feature with Atlas Vector Search, combining it with Atlas Search to surface the most relevant results.

• MongoDB University
• 2 hours
• Self-Paced
• Free Certificate

James Briggs

Spotify's Podcast Search Explained

Build a Spotify-style semantic podcast search system with sentence transformers, vector search, synthetic queries, fine-tuning, and recall evaluation.

• YouTube
• 50 minutes
• Self-Paced
• Free Video

James Briggs

Cohere vs. OpenAI Embeddings - Multilingual Search

Build multilingual semantic search with Cohere embeddings, compare them with OpenAI, and index cross-language queries in Pinecone.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

James Briggs

How to Build an AI-Powered Video Search App

Build a semantic video search app that returns relevant YouTube clips at the exact timestamp using embeddings and a vector database.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

Code with Ania Kubów

Build a Semantic Search App Using Next.js and SingleStoreDB

Learn to create a semantic search app with Next.js and SingleStoreDB, focusing on vector embeddings and efficient data retrieval for enhanced search functionality.

• YouTube
• 56 minutes
• Self-Paced
• Free Video

Aleksa Gordić - The AI Epiphany

Will LLMs Kill Search? The Future of Information Retrieval

Nils Reimers explains how retrieval-augmented generation, multilingual embeddings, and reranking are reshaping search.

• YouTube
• 1 hour 7 minutes
• Self-Paced
• Free Video

OpenSource Connections

Personalizing Search Using Multimodal Latent Behavioral Embeddings

Explore personalized search using multimodal latent behavioral embeddings. Learn to integrate user behavior into modern search retrieval pipelines for RAG and end-user search, enhancing relevance and user experience.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

OpenSource Connections

A Cheap Trick for Semantic Question Answering for GPU-Challenged Systems

Explore a cost-effective approach to semantic question answering using LLMs during indexing, combining text and vector matching for efficient search without GPU requirements.

• YouTube
• 46 minutes
• Self-Paced
• Free Video

Abhishek Thakur

Build Your Own Hybrid Search - BM25 and Vector Embeddings with Reciprocal Rank Fusion

Discover how to upgrade a BM25 search engine into a hybrid system combining BM25 with vector embeddings using reciprocal rank fusion for enhanced semantic search capabilities.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

OpenSource Connections

From Traditional Keyword Search to AI-Powered Search

Discover how to evolve from traditional keyword search to AI-powered solutions, covering semantic expansion, KNN search, Learning to Rank, and practical implementation challenges.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Career Certificate

Prompt Design, Grounding, and RAG with Vertex AI

13981 ratings at Coursera

Build generative AI applications with Vertex AI by designing prompts, grounding Gemini with embeddings and your data, and implementing semantic, hybrid, and multimodal RAG.

• Coursera
• 4 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 10 courses

John Savill's Technical Training

Understanding Generative AI: RAG, Semantic Search, Embeddings, and Vectors

Explore key Generative AI concepts like RAG, semantic search, embeddings, and vectors. Understand their importance in leveraging AI with your data for improved information retrieval and processing.

• YouTube
• 29 minutes
• Self-Paced
• Free Video