Introducing AI with Python

Live: In-Person at ITU Lahore and Online

Rs 9,500/- PKR

Duration: 4 Weeks
Starting: 02 June 2025
Mode: In-Person and Online
Age: 11 and Above
Online Classes: Tuesday & Friday
Timing: 10:45 am to 12:00 pm

About Course

This course provides an engaging introduction to programming, data science, and AI through Python and PyTorch. Students will explore fundamental programming concepts, visualize data, work on real-world mini projects, and delve into object detection and chatbot development using industry-standard tools. The hands-on nature of the course encourages creativity, experimentation, and collaboration, preparing students for deeper learning in AI and machine learning.

Class 1: Python Basics & Introduction to PyTorch

Variables, data types, and simple operations
Conditional statements (if, else)
Loops (for, while)
Functions (definition, parameters, return)
Lists and basic data structures
Introduction to PyTorch tensors:
- What are tensors?
- Creating and manipulating tensors
- Playground setup: Google Colab or Jupyter Notebooks

Class 2: Practicing with Loops, Functions + Mini Project Prep

Recap and deeper practice with loops and functions
Using PyTorch to generate random tensors and apply basic operations
Mini Project: Simulate a Dice Rolling Game
- Generate random numbers using PyTorch
- Track stats (e.g., average rolls, frequency)
- Create a modular structure using functions
- Collaborative playground creation + code snippets
- Encourage creative extensions (e.g., multi-dice simulation)

Class 3: Data Visualization Basics

Introduction to Matplotlib and Seaborn
Types of graphs: Line plot, Bar chart, Histogram, Pie chart
Plotting datasets (e.g., temperature, test scores)
Aesthetic customization (titles, labels, legends, colors)

Class 4: Real-World Visualizations + Final Project Discussion

Applications:
- COVID-19 daily cases
- Visualizing students' marks or performance trends
- Simulated data for expenses and budgeting
- Brief intro to interactivity using Plotly
Project: Create a Dashboard of Your Day
- Collect 1-day time usage data (sleep, study, screen time, etc.)
- Visualize it with pie charts, bar graphs
- Display insights like “most productive hour”
- Optional: Compare multiple days, integrate basic PyTorch stats

Class 5: Concept of Object Detection + MMDetection Setup

What is object detection? How is it different from classification?
Introduction to MMDetection framework
Using pretrained models (YOLO, Faster R-CNN)
Installation and setup (Colab/Runtime notes)
Loading an image and detecting objects
Visualizing detection results with bounding boxes

Class 6: Real-World Applications of Object Detection

Applications:
- Object detection in security cameras
- Detecting traffic signs or vehicles
- Custom example: Book detection on a shelf
- Minor tweaking with confidence thresholds and image sources
Project: Object Detector for Everyday Items
- Upload your own room image
- Detect known objects
- Present findings and reflection
- Discuss challenges and limitations (e.g., model size, accuracy)

Class 7: Chatbots & Conversational AI

Introduction to Chatbots
Rule-based vs. AI-powered
Using a simple transformer-based model (e.g., DialoGPT or GPT-2)
Build a chatbot that can respond to basic queries
Exploring model inference using Hugging Face Transformers
Limitations and ethical considerations

Class 8: Project + RAG Discussion

Project: Build Your Own Chatbot
- Create a chatbot that can answer FAQ-style questions (e.g., about a topic like space, animals, school subjects)
- Train/infer using predefined context
Intro to RAG (Conceptual)
- Why large models struggle with specific info
- How RAG uses external data sources to improve responses
- Real-world examples (chatbots with document support)

What Will You Learn?

Writing Python programs using variables, conditionals, loops, and functions.
Creating and manipulating tensors with PyTorch for simulations and experiments.
Building simulations like dice games using randomness and modular code.
Visualizing data with Matplotlib and Seaborn, including pie charts and bar graphs.
Understanding object detection and applying pretrained models on real-world images.

Exploring use cases of AI in everyday life—like detecting books or traffic signs.
Building and customizing your own chatbot with transformer models like GPT-2.
Gaining insights into Retrieval-Augmented Generation (RAG) and how AI can access external knowledge to improve responses.
Developing a solid foundation in programming, data visualization, AI applications, and ethical tech usage.