Vector Databases Courses and Certifications

Coursera

Building RAG Systems with Open Models

1 rating

Build production-ready RAG pipelines: compare FAISS, ChromaDB, Milvus and Pinecone, tune chunking and indexing, and assemble hybrid retrieval with LangChain and open LLMs.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Scrimba

Build AI Apps with LangChain.js

54 ratings at Coursera

Build an internal company knowledge base with LangChain.js: store embeddings in Supabase, craft prompt templates, chain retrieval with RunnableSequence, and ship a personal assistant app.

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

Coursera

Introduction to Retrieval Augmented Generation (RAG)

40 ratings at Coursera

Build RAG applications with LangChain: write effective prompts, work with embeddings and vector databases, and use document loaders, splitters, agents, tools, and output parsers.

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

RAG and Agentic AI Capstone Project

34 ratings at Coursera

Build an end-to-end multimodal RAG and multi-agent system: structured JSON datasets, text and image embeddings, vector retrieval, MCP tool integration, and a Gradio chatbot.

• Coursera
• 5 weeks, 3 hours a week
• Self-Paced
• Paid Course

Vector Database Projects: AI Recommendation Systems

16 ratings at Coursera

Build two ChromaDB recommendation systems: generate embeddings for a collection and run similarity searches over user search terms with NLP algorithms.

• Coursera
• 2 weeks, 3 hours a week
• Self-Paced
• Paid Course

Vector Database Projects: AI Recommendation Systems

16 ratings at Coursera

Build two recommendation systems — food ordering and job search — using Chroma DB and Hugging Face NLP models to generate embeddings and run similarity searches.

• Coursera
• 2 weeks, 3 hours a week
• Self-Paced
• Paid Course

Fractal Analytics Career Certificate

Modern Data Strategy for Enterprise Generative AI

14 ratings at Coursera

Build a modern enterprise data strategy for generative AI: structured and unstructured data, metadata tagging, vector databases, unified architectures, data lineage, and responsible AI governance.

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

Advanced Retrieval for AI with Chroma

12 ratings at Coursera

Improve RAG relevance: apply LLM query expansion, cross-encoder reranking, and trained embedding adapters to retrieval results in a Chroma vector database.

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

LangChain Course for LLM Application Development

10 ratings at Coursera

Build retrieval-augmented LLM applications with LangChain: document loaders and text splitters, embeddings and vector stores, Sequential/Stuff/Refine/Map Reduce chains, memory, agents, and local Falcon models.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

LearnKartS Career Certificate

Full Stack Agentic AI

5 ratings at Coursera

Build production-ready agentic AI: create full-stack RAG apps with Angular and Node.js, design MCP servers and tool-calling agents, and wire vector databases and MongoDB pipelines.

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

Foundations of AI Native Data Engineering

Redesign data pipelines for AI workloads: work with embeddings, vector retrieval, RAG design and failure diagnosis, LLM hosting tradeoffs, and build a semantic search workflow.

• Coursera
• 9 weeks, 2 hours a week
• Self-Paced
• Paid Course

Board Infinity

RAG Systems & Agentic Workflows with Pinecone and LangGraph

Build end-to-end RAG pipelines: create vector indexes in Chroma, Pinecone, and FAISS, design state-based conversational agents with memory, and debug workflows with LangFuse and OpenTelemetry.

• Coursera
• 4 weeks, 5 hours a week
• Self-Paced
• Paid Course

Board Infinity

AI Systems Design: RAG Pipelines and LLM Architecture

Build an end-to-end retrieval-augmented generation prototype with a Streamlit UI: scope AI use cases, design ingestion, chunking and embedding pipelines, and compare RAG with fine-tuning.

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

Board Infinity

Advance Multi-Agent Systems & Production AI with LangSmith

Build RAG pipelines with ChromaDB, Pinecone, and FAISS, design conversational agents with memory, coordinate multi-agent workflows using MCP and tool-calling, and monitor them with LangFuse.

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

LearnKartS Career Certificate

Agentic AI Engineering: RAG, MCP & MERN

Build autonomous AI agents with RAG, MCP tool calling, and the MERN stack, integrating OpenAI and Gemini models with vector databases like ChromaDB and pgVector.

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