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

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:

1. AI Assistant

2. Prompt Engineering Playground

3. AI Resume Analyzer

4. PDF Chat Assistant with RAG

5. Autonomous Research Agent

6. Multi-Agent Content Team

7. 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 who want to learn practical AI

• Developers who want to build AI applications

• Students building portfolio projects

• Analysts and managers who want hands-on AI skills

• Entrepreneurs exploring AI product ideas

• Professionals transitioning into AI

• Instructors who want a practical AI project roadmap

Requirements

• Basic Python knowledge is helpful

• Basic command line knowledge is helpful

• No machine learning background required

• No advanced math required

• A computer with Python installed

• Optional: OpenAI API key

• Optional: Ollama for local LLM usage

Course Outcome

By the end of this course, you will not just understand AI concepts — you will have built real AI applications that you can show in your portfolio, resume, interviews, GitHub, or business demos.

Final Course Promise

In one week, you will go from basic AI concepts to building and packaging practical AI applications using LLMs, prompt engineering, RAG, agents, multi-agent systems, and Docker.