《7-Day Practical AI Bootcamp: Build AI Apps, RAG, and Agents》是一门聚焦实战的 7 天密集课程，致力于教授学员使用 Python、Streamlit 及大模型 API 构建 7 个企业级 AI 应用。课程覆盖从基础 AI 助手、RAG 知识库到自主智能体与多智能体工作流的开发，并包含 Docker 容器化部署，适合希望快速掌握 AI 应用工程的开发者。
Published 6/2026
Created by Arjun Vaid, School of AI
MP4 | Video: h264, 1920×1080 | 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 demos.
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.
Description
Artificial Intelligence is changing how software, business, research, education, and productivity tools are built. But many AI courses spend too much time on theory and not enough time building practical applications.
This course is different.
In this hands-on 7-day AI bootcamp, you will learn modern AI by building real projects from scratch. You will start with a simple AI assistant, then move into prompt engineering, AI-powered applications, PDF chat using RAG, autonomous agents, multi-agent workflows, and a final deployable AI knowledge base assistant.
This course is designed for beginners to intermediate learners who want practical AI skills without getting lost in heavy machine learning theory. You do not need a deep math or data science background. The focus is on building working AI applications using tools that developers, students, analysts, managers, and entrepreneurs can use immediately.
Throughout the bootcamp, you will build seven practical projects
✨ AI Assistant
✨ Prompt Engineering Playground
✨ AI Resume Analyzer
✨ PDF Chat Assistant with RAG
✨ Autonomous Research Agent
✨ Multi-Agent Content Team
✨ AI Knowledge Base Assistant with Docker
By the end of this course, you will understand how modern AI applications are structured, how to work with LLMs, how to write better prompts, how to build RAG pipelines, how AI agents work, and how to package an AI app for deployment.
This is not just a theory course. Every day includes a hands-on lab and a portfolio-ready deliverable.
What You Will Learn
✨ Understand modern AI, Generative AI, and Large Language Models
✨ Build your first AI assistant using Python and Streamlit
✨ Write stronger prompts using roles, rules, context, and output formats
✨ Build a prompt engineering playground
✨ Create an AI Resume Analyzer
✨ Extract text from PDF, TXT, and DOCX files
✨ Build a PDF Chat Assistant using RAG
✨ Use ChromaDB as a local vector database
✨ Understand embeddings and semantic search
✨ Build an autonomous research agent
✨ Create a multi-agent content workflow
✨ Package an AI application with Docker
✨ Prepare a portfolio-ready GitHub project
✨ Understand responsible AI basics and safety guardrails
Who this course is for
⭐ This course is for beginners and intermediate learners who want to build practical AI applications instead of only learning AI theory.
⭐ It is ideal for software developers who want to add AI, LLMs, RAG, and AI agents to their skill set.
⭐ It is also useful for students who want portfolio-ready AI projects for GitHub, resumes, internships, interviews, or job applications.
⭐ Analysts, managers, consultants, and business professionals who want to understand how AI applications are built will also benefit from this course.
⭐ Entrepreneurs and creators who want to prototype AI-powered products, productivity tools, research assistants, resume tools, document chatbots, or business assistants will find the projects practical and reusable.
⭐ This course is also a good fit for Python learners who want to move beyond basic scripts and start building real AI-powered applications with Streamlit, LLM APIs, RAG, ChromaDB, agents, and Docker.
⭐ This course is not designed for learners looking for deep machine learning theory, advanced mathematics, or model training from scratch. The focus is practical AI application development.
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