Certified 7-Day AI Bootcamp: AI Apps, RAG, and AI Agents, Learn AI by building projects with Python, LLMs, Streamlit, prompt engineering, RAG, AI Agents, Multi-Agent Workflows.
Course 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 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.
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.
