


Artificial Intelligence
Bootcamp for O/A Level
- In-person, NUST H-12 Campus, Islamabad.
- 20th June
- 2 Months
- Friday to Sunday 4:00 PM to 7:00 PM
- Eligibility criteria
- 14-18 Years of Age (Male/Female)
- Students currently enrolled or have recently done O/A Levels or Metric/FSc
- Basic computer literacy (no coding/AI experience required)
atomcamp & NUST have jointly launched AI bootcamp for Islamabad’s youth to train them in the most in-demand AI skills.
This bootcamp will facilitate students in launching their international freelancing careers, getting admissions abroad, and developing a sound technological foundation for any career.
Additionally, we will arrange extra curricular activities on-campus, sessions on soft skills, career-coaching, and industry visits.
Why should you enroll in
this bootcamp?
The Data Science & AI engineering market, currently at $8 billion, is set to grow at 35% annually from 2025 to 2032.
Key trends for 2025 include generative AI, sustainable AI, and augmented working, with significant growth expected in cloud-based AI applications, according to Gartner.
Given these metrics, we believe that it’s high time to train Pakistani youth in these technical skills
Standard Monthly
PKR 50,000
Monthly Payment
For 2 Months
Lumpsum
PKR 90,000
Advance Payment
For 2 Months
Why choose this bootcamp?
Get yourself ready for future of work and here is how we help you do that:
- Career counseling
- Global freelancing career
- Facilitation for university application
- Industry tours
- Teamwork and networking
- Professional development
What’s Inside
Extra-Curricular Activities
Explore passions, build skills, and grow together.
1-on-1 Coaching & Mentorship
Receive individualized mentorship to navigate your way through your AI career.
Speaker Sessions
Regular speaker series with industry professionals and hiring managers
Young Innovators: Startup Discovery at NSTP H12
Young Innovators is a half-day visit where K–12 students from our bootcamps tour the NSTP startup at H12, engage with the team, and participate in a hands-on mini-workshop.
- Students see real-world applications of low-code/no-code tools, data analytics, and AI in a startup environment.
- They connect classroom concepts to entrepreneurial problem-solving and product development.
- Direct interaction with innovators inspires creativity, boosts motivation, and builds professional confidence.
Objective
This two-month summer bootcamp is the perfect opportunity for students to not only explore AI but also make the most out of their summer. In these 2 months, they’ll be gaining practical, hands-on skills that will set them apart from their peers.
It’s an intensive but incredibly rewarding experience. Students will not only learn the most in-demand skills like Python programming, AI agent building, and prompt engineering, but they’ll also gain clarity on their future career paths. In just two months, they’ll be exposed to a range of tech fields, from building AI models to deploying products, and everything in between. This exposure will help them figure out what excites them the most, whether that’s building products, working with AI in a creative space, or becoming a leader in tech.
The summer isn’t just about learning – it’s about exploring different career possibilities, getting inspired by industry trips, and interacting with peers who share similar interests. This is a transformative chance for students to not only develop technical skills but also get a clear sense of the direction they want their futures to take. They’ll finish the bootcamp not only with a stronger resume but with a roadmap of where they want to go next in their careers.
It’s about making this summer count. Instead of just passing the time, students will walk away with tangible skills, clear insights into the future of AI, and a sense of purpose for what they want to achieve next.
Previous Bootcamp Highlights
Course Curriculum
Objective: Introduce core AI concepts and how machines understand human language.
Topics Covered:
- What is AI? Types of AI (with examples like Siri, ChatGPT)
- AI vs. GenAI vs. AI Agents
- Intro to LLMs (in simple terms)
- Chatbot anatomy & workflows
- Hands-on: Play with ChatGPT (basic Q&A, fun facts, jokes)
Objective: Teach students to “talk” to AI and build first bots without code.
Topics Covered:
- Prompt anatomy: roles, context, instructions
- Chain-of-Thought prompting
- Persona prompts & formatting
- Flowise drag-and-drop flows
- Quick-start on Poe or Superagent
Project Idea: “Daily Motivation” or “Quiz Master” bot in Flowise
Objective: Teach basic Python programming to enable AI implementation.
Topics Covered:
- Python Basics: Variables, data types, operators
- Input/output (print, input functions)
- Control Structures: Conditionals (if-else) Loops (for, while)
- Functions & Lists: Defining and calling functions, Storing and manipulating data in lists
- Simple NLP: word count, keyword spotting
Project Idea: Vibe Checker” script: counts positive/negative words in a sentence
Objective: Combine Python logic with LLMs via API calls to build interactive agents.
Topics Covered:
- Understanding APIs
- What are APIs?
- How APIs Work
- Why APIs are Useful for AI
- Installing & importing OpenAI or Hugging Face client libraries
- Sending prompts & receiving completions
- Dialogue loops & basic error handling
- Secure API key management (environment variables)
Project: “Fun Facts Bot” CLI: user asks a question → Python sends to LLM → prints a fun fact.
Objective: Explore how AI agents use memory and external knowledge with simplified LangChain and RAG concepts.
Topics Covered:
- Concept of memory: short-term vs. long-term
- Embeddings & vector stores (intuitive demos)
- RAG basics: retrieving docs, building context
- Connecting documents to bots for Q&A
Basic LLM pipelines (user → prompt → retrieval → response)
Project: Build a “Notes Q&A Bot” that answers questions from your uploaded notes or a short document.
Objective: Build teams of AI agents using roles for collaborative problem solving
Topics Covered:
- Multi-agent systems & role definitions (Planner, Researcher, Writer)
- CrewAI setup & configuration
- Task coordination between agents
- Ethics refresher: avoiding conflicting directives
Project: Build a “Project Team” of 3 agents: plan a school event or solve a challenge
Objective: Teach students how to build multi-step, automated workflows using AI and external apps, without writing code.
Topics Covered:
What is workflow automation? Why use tools like n8n?
Introduction to n8n: nodes, triggers, actions, and data flow
Connecting n8n to ChatGPT using the OpenAI node
Creating simple logic flows: conditions, loops, and delays
Using triggers: Google Sheets, Webhooks, Time-based events
Sending and receiving data between nodes
Debugging workflows and managing failed executions
Example use cases: auto-email responders, daily reminders, form processors
Project: “AI Workflow Assistant”
Students will build a multi-step workflow that uses AI to process input and trigger useful outputs A “Homework Help Bot” that takes a Google Form submission → sends it to ChatGPT → emails back a simplified explanation
Objective: Package your capstone agent into a portable container with a user interface.
Topics Covered:
- What is containerization? Why use Docker?
- Writing a Dockerfile for a Python/Streamlit (or Gradio) app
- Exposing ports & environment variables
- Building & running your container locally
- Basic UI design with Streamlit or Gradio components
Project:
- Wrap your Phidata/CrewAI/LangChain agent in a Streamlit or Gradio UI.
- Write a Dockerfile, build a local container image, and run it to verify functionality.
Objective: How to use GitHub for version control, project management, and deployment integration.
Topics Covered:
- What is Git and GitHub? Why developers use them
- Creating and cloning repositories
- Basic Git commands: add, commit, push, pull
- Using .gitignore and managing project files
- Writing good README files and commit messages
- Connecting GitHub to Vercel/Replit for deployment
- Understanding open-source and collaboration basic
Project Idea:
Push your AI assistant project to GitHub with a clear README
Objective: Deploy your containerized agent online so anyone can access it.
Topics Covered:
- Cloud vs. local apps: why cloud deployment matters
- Overview of Vercel
- Setting up a Vercel account and connecting GitHub
- Deploying your agent with serverless functions or static hosting
- Environment variables and deployment settings in Vercel
- Sharing live URLs and monitoring usage
What you will learn
- Explain core AI concepts and how NLP and Generative AI technologies like LLMs work
- Design and evaluate effective prompts for interacting with AI models
- Build AI agents using both no-code tools (e.g., Flowise, n8n) and Python-based code
- Implement memory-augmented workflows and coordinate multi-agent systems
- Package, containerize, and deploy AI agents with user interfaces to the cloud
Tools You Will Master








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Pricing Plan
Pick a plan that fits your needs and budget.
Standard Monthly
PKR 50,000
Monthly Payment
For 2 Months
Lumpsum
PKR 90,000
Advance Payment
For 2 Months