Free Udemy Course __ Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps

Learn to build intelligent, retrieval-powered AI systems using LangChain, LlamaIndex, and real-world RAG workflows

4.5 (5,442 students students enrolled) English
data-science Machine Learning
Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps

What You'll Learn

  • Design and Build a Retrieval-Augmented Generation (RAG) System Understand how to integrate large language models (LLMs) with retrieval pipelines
  • Implement Embeddings and Vector Databases for Semantic Search Learn how to generate and store embeddings using tools like OpenAI, ChromaDB, or Pinecone
  • Develop an End-to-End AI Knowledge Assistant Build and deploy a functional AI chatbot using frameworks like LangChain, Streamlit, and FastAPI
  • Evaluate and Optimize AI Performance Metrics Measure your assistant’s accuracy, relevance, and user experience using key performance metrics

Requirements

  • Basic Python Programming Skills Familiarity with Python syntax and libraries (like pandas, requests, or json) will make it easier to follow along with code demonstrations.
  • Curiosity About AI and LLMs A foundational understanding of how Large Language Models (LLMs) like ChatGPT or Llama work conceptually will be helpful, but not mandatory — everything is explained in simple terms.
  • Access to a Computer with Internet You’ll need a computer capable of running Python and Jupyter notebooks or VS Code, plus an internet connection to install packages and access APIs.
  • Free or Trial Accounts for Tools Some hands-on labs will use free-tier APIs or tools such as OpenAI, LangChain, ChromaDB, and Streamlit — setup instructions are provided in the course.

Who This Course is For

  • Developers and Programmers who want to integrate Large Language Models (LLMs) with real-time data, APIs, and enterprise workflows.
  • Data Scientists and Machine Learning Enthusiasts looking to master embeddings, vector databases, and semantic search for practical AI deployment.
  • AI/ML Students and Researchers eager to build a complete RAG-based knowledge assistant project to strengthen their portfolio or academic work.
  • Educators and Knowledge Managers interested in automating information retrieval, FAQs, and content summarization within organizations
  • Entrepreneurs and Innovators aiming to create AI assistants for business domains — from healthcare to finance, support, or education.

Your Instructor

Data Science Academy

Bridging knowledge to industry with Data & AI education

4.4 Instructor Rating

670 Reviews

34,855 Students

14 Courses

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