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

Name: 7-Day Practical AI Bootcamp: Build AI Apps, RAG, and Agents
Availability: InStock
Rating: 1 (45 reviews)
Author: Arjun Vaid

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

 1/5

(45) Ratings

210 students

Development

Created by Arjun Vaid

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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.

This course includes:

9 total hours on-demand video

0 articles

14 downloadable resources

55 lessons

Full lifetime access

Access on mobile and TV

Certificate of completion

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Course content

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

Post Views: 24

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.

Courze

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.