Udemy - 7-Day Practical AI Bootcamp - Build AI Apps, RAG, and Agents

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size3.2 GB
  • Uploaded Byfreecoursewb
  • Downloads96
  • Last checkedJul. 03rd '26
  • Date uploadedJul. 02nd '26
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  • Leechers29

Infohash : E3BCDB3BA265E6C5B909DA7EC96402DEF10B19C2

7-Day Practical AI Bootcamp: Build AI Apps, RAG, and Agents

https://WebToolTip.com

Published 6/2026
Created by Arjun Vaid, School of AI
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 55 Lectures ( 8h 51m ) | Size: 3.3 GB

Learn AI by building projects with Python, LLMs, Streamlit, prompt engineering, RAG, AI Agents, Multi-Agent Workflows

What you'll learn
⚡ Build practical AI applications using Python, Streamlit, and Large Language Models.
⚡ Understand modern AI concepts including Generative AI, LLMs, tokens, prompts, context windows, and hallucinations.
⚡ Write effective prompts using roles, instructions, constraints, examples, and structured output formats.
⚡ Create a Prompt Engineering Playground to test, compare, and save reusable prompts.
⚡ Build an AI Resume Analyzer that reviews resumes, scores them, and suggests improvements.
⚡ Extract text from PDFs and documents for use in AI applications.
⚡ Build a PDF Chat Assistant using Retrieval-Augmented Generation, also known as RAG.
⚡ Understand embeddings, semantic search, document chunking, and vector databases.
⚡ Use ChromaDB as a local vector database for document search and retrieval.
⚡ Build an autonomous AI Research Agent that can plan, search, analyze, write, review, and save reports.
⚡ Create a multi-agent workflow with Planner, Researcher, Writer, Editor, and QA agents.
⚡ Package an AI application with Docker and prepare it for portfolio or deployment.
⚡ Apply responsible AI practices including privacy, accuracy, guardrails, and human oversight.
⚡ Create portfolio-ready AI projects suitable for GitHub, resumes, interviews, and s.

Requirements
❗ No advanced AI, machine learning, or data science background is required.
❗ Basic Python knowledge is helpful, but the course is beginner-friendly and explains the code step by step.
❗ Basic command line or terminal knowledge is helpful for running Python apps and installing packages.
❗ Students should have a computer with internet access.
❗ An OpenAI API key is optional. Students can also use Ollama to run local models where supported.
❗ Basic familiarity with APIs, web apps, or software development is helpful, but not required.
❗ No advanced math is required.
❗ No prior experience with RAG, AI agents, vector databases, Streamlit, ChromaDB, or Docker is required. These topics are introduced from the ground up through hands-on labs.
❗ Most importantly, students should be curious and ready to build practical AI projects step by step.

Files:

[ WebToolTip.com ] Udemy - 7-Day Practical AI Bootcamp - Build AI Apps, RAG, and Agents
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Course Introduction and Setup
    • 1. Teaser Project - Complete End to End Knowledge and Research Assistant.mp4 (552.0 MB)
    • 2. What You Will Build in This Course.mp4 (72.4 MB)
    • 3. Course Tech Stack Overview.mp4 (40.9 MB)
    • 4. Installing Python, VS Code, and Git.mp4 (56.4 MB)
    • 5. Setting Up OpenAI or Ollama.mp4 (90.0 MB)
    • 6. Creating the Bootcamp Project Folder.mp4 (75.8 MB)
      • __MACOSX teaser_ai_knowledge_assistant
      • _.DS_Store (0.1 KB)
      • _.env (0.2 KB)
      • _agents (0.2 KB)
      • _app.py (0.2 KB)
      • _config.py (0.2 KB)
      • _data (0.2 KB)
      • _prompts (0.2 KB)
      • _rag (0.2 KB)
      • _requirements.txt (0.2 KB)
      • _services (0.2 KB)
      • _utils (0.2 KB)
        • agents
        • _.DS_Store (0.1 KB)
        • ___init__.py (0.2 KB)
        • _research_team.py (0.2 KB)
        data
        • _.DS_Store (0.1 KB)
        prompts
        • _.DS_Store (0.1 KB)
        • ___init__.py (0.2 KB)
        • _templates.py (0.2 KB)
        rag
        • _.DS_Store (0.1 KB)
        • ___init__.py (0.2 KB)
        • _vector_store.py (0.2 KB)
        services
        • _.DS_Store (0.1 KB)
        • ___init__.py (0.2 KB)
        • _llm_service.py (0.2 KB)
        utils
        • _.DS_Store (0.1 KB)
        • ___init__.py (0.2 KB)
        • _pdf_utils.py (0.2 KB)
        teaser_ai_knowledge_assistant
        • DS_Store (6.0 KB)
          • agents
          • DS_Store (6.0 KB)
          • __init__.py (0.0 KB)
          • research_team.py (3.2 KB)
        • app.py (11.5 KB)
        • config.py (0.4 KB)
          • data
          • DS_Store (6.0 KB)
        • env (0.1 KB)
          • prompts
          • DS_Store (6.0 KB)
          • __init__.py (0.0 KB)
          • templates.py (2.6 KB)
          rag
          • DS_Store (6.0 KB)
          • __init__.py (0.0 KB)
          • vector_store.py (3.7 KB)
        • requirements.txt (0.0 KB)
          • services
          • DS_Store (6.0 KB)
          • __init__.py (0.0 KB)
          • llm_service.py (3.2 KB)
          utils
          • DS_Store (6.0 KB)
          • __init__.py (0.0 KB)
          • pdf_utils.py (2.4 KB)
          2 - Day 1 Build Your First AI Assistant
          • 10. Build the LLM Service Layer.mp4 (141.1 MB)
          • 11. Build a Terminal AI Chatbot.mp4 (112.9 MB)
          • 12. Build a Streamlit AI Chatbot.mp4 (124.6 MB)
          • 13. Day 1 Wrap-Up and Student Challenge.mp4 (36.1 MB)
          • 7. What Is AI, Generative AI, and an LLM.mp4 (34.1 MB)
          • 8. Understanding Prompts, Tokens, and Responses.mp4 (56.4 MB)
          • 9. Day 1 Lab Overview.mp4 (10.1 MB)
          3 - Day 2 Prompt Engineering Playground
          • 14. Why Prompt Engineering Matters.mp4 (31.3 MB)
          • 15. Weak Prompt vs Strong Prompt.mp4 (26.5 MB)
          • 16. Prompt Formula Role, Task, Context, Rules, Output Format.mp4 (25.9 MB)
          • 17. Day 2 Lab Overview.mp4 (12.1 MB)
          • 18. Build Prompt Templates.mp4 (50.1 MB)
          • 19. Build a Prompt Lab with Streamlit.mp4 (44.1 MB)
          • 20. Prompt Lab Demo Save, Select, and Run Prompts.mp4 (73.6 MB)
          • 21. Day 2 Wrap-Up and Student Challenge.mp4 (8.5 MB)
          4 - Day 3 AI Resume Analyzer
          • 22. Turning LLM Calls into AI Applications.mp4 (6.6 MB)
          • 23. AI Resume Analyzer Architecture.mp4 (7.6 MB)
          • 24. Day 3 Lab Overview.mp4 (10.7 MB)
          • 25. Upload and Extract Resume Text.mp4 (58.0 MB)
          • 26. Build the Resume Analysis Prompt.mp4 (27.8 MB)
          • 27. Build the Streamlit Resume Analyzer App.mp4 (25.4 MB)
          • 28. Demo Analyze a Resume from Upload to Report.mp4 (52.7 MB)
          • 29. Day 3 Wrap-Up and Student Challenge.mp4 (12.6 MB)
          5 - Day 4 PDF Chat Assistant with RAG
          • 30. What Is RAG.mp4 (21.4 MB)
          • 31. Embeddings, Chunks, and Semantic Search.mp4 (41.0 MB)
          • 32. ChromaDB Beginner Overview.mp4 (23.3 MB)
          • 33. Building a PDF Chat Assistant with RAG and ChromaDB.mp4 (171.2 MB)
          • 34. Chat with Your PDFs Build a RAG App Using ChromaDB and an LLM.mp4 (75.0 MB)
          • 35. Day 4 Wrap-Up and Student Challenge.mp4 (12.6 MB)
          6 - Day 5 Autonomous Research Agent
          • 36. What Is an AI Agent.mp4 (9.6 MB)
          • 37. Agent = LLM + Tools + Workflow + State.mp4 (23.1 MB)
          • 38. Build the Autonomous Research Agent.mp4 (186.3 MB)
          • 39. Demo Run the Research Agent from Topic to Report.mp4 (56.4 MB)
          • 40. Day 5 Wrap-Up and Student Challenge.mp4 (7.8 MB)
          7 - Day 6 Multi-Agent Content Team
          • 41. Single Agent vs Multi-Agent Systems.mp4 (14.5 MB)
          • 42. Understanding Agent Roles and Orchestration.mp4 (22.2 MB)
          • 43. Build the Multi-Agent Content Team.mp4 (225.7 MB)
          • 44. Run the Multi-Agent Content Team End-to-End.mp4 (75.4 MB)
          • 45. Day 6 Wrap-Up and Student Challenge.mp4 (7.5 MB)
          8 - Day 7 Capstone and Deployment
          • 46. Capstone Project Overview.mp4 (10.3 MB)
          • 47. AI Knowledge Base Assistant Architecture.mp4 (20.4 MB)
          • 48. Build the AI Knowledge Base Assistant.mp4 (204.7 MB)
          • 49. Dockerfile Containeri

Code:

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