Vector Embeddings Courses and Certifications

Vector Databases: from Embeddings to Applications

12 ratings at Coursera

Build applications on vector databases: form embeddings, apply similarity search techniques, use fast search algorithms over large datasets, and create RAG and multilingual search apps.

• Coursera
• 1 hour 3 minutes
• Self-Paced
• Free Online Course

Create Embeddings, Vector Search, and RAG with BigQuery

Build a RAG pipeline in BigQuery: create embeddings, search a vector space, and generate grounded answers with Gemini to reduce AI hallucinations.

• Coursera
• 2 hours 30 minutes
• Self-Paced
• Paid Course

Coursera Career Certificate

Vector DB Foundations, Embeddings & Search Algorithms

Build semantic search systems: generate text and image embeddings, tune HNSW parameters, benchmark FAISS and Annoy ANN indices, and implement RAG and multimodal search with Weaviate.

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

Packt

Getting Started with Vector Databases and AI Embeddings

Learn how embeddings turn data into vectors, apply similarity metrics and vector search, and evaluate vector databases for RAG, semantic search, recommendations, and anomaly detection.

• Coursera
• 5 weeks, 1 hour a week
• Self-Paced
• Paid Course

Samuel Chan

Understanding Embeddings in Large Language Models - LlamaIndex and Chroma DB

A deeper look at what embeddings represent and how they function in LLM applications, with examples using LlamaIndex and Chroma.

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

Create Embeddings, Vector Search, and RAG with BigQuery

Learn how to build Retrieval Augmented Generation (RAG) pipelines in BigQuery to reduce AI hallucinations. Create embeddings, search vector spaces, and generate more accurate answers using Gemini and

• edX
• 2 weeks, 1-2 hours a week
• Self-Paced
• Free Certificate

Vector Search and Embeddings

Learn to build intelligent search with vector embeddings, hybrid semantic-keyword retrieval, and grounded RAG using Vertex AI Vector Search.

• Google Skills
• 4 hours
• Self-Paced
• Paid Course

Create Embeddings, Vector Search, and RAG with BigQuery

Build a BigQuery-based RAG pipeline that uses embeddings and vector search to improve generative AI answers and reduce hallucinations.

• Google Skills
• 2 hours
• Self-Paced
• Paid Course

James Briggs

Intro to Dense Vectors for NLP and Vision

An introduction to dense vectors for NLP and vision, with Python examples of sentence embeddings, question answering, and image-text matching.

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

Code with Ania Kubów

Vector Embeddings for Beginners - OpenAI

Learn to create and utilize vector embeddings for AI applications, including text analysis, database integration, and building an AI assistant using OpenAI and Langchain.

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

Conf42

Vector Ops: Running Vector Embedding-Powered Apps in Production

Explore vector embedding-powered applications in production, from search and recommendations to generative AI, agents, and memory systems. Learn implementation strategies and practical considerations.

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

Conf42

Hallucination by Design - How Embedding Models Misunderstand Language

Uncover how embedding models create hallucinations and misinterpret language, with practical examples, evaluation techniques, and fine-tuning strategies to improve accuracy.

• YouTube
• 29 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

Career Certificate

Learn Retrieval Augmented Generation

150 ratings at Boot.dev

Build a RAG pipeline in Python: clean corpora, rank with TF-IDF and BM25, chunk documents, search embeddings in vector databases, rerank results, and evaluate retrieval quality.

• Boot.dev
• 40 hours
• Self-Paced
• Paid Course

Associate AI Engineer for Developers

32 ratings at DataCamp

## Become an AI Developer with Practical Skills Start your journey to becoming an AI Engineer by learning how to integrate AI into software applications.

• DataCamp
• 1 day 2 hours
• Self-Paced
• Free Trial Available