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

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.