Welcome to the AI Summer Bootcamp GitHub repository! This repo contains all module resources for the faculty training program delivered at COMSATS University Islamabad, Sahiwal Campus.
The course covers a wide range of AI topics, starting from Python fundamentals to advanced python concepts, data analysis with Pandas and NumPy, web scraping with BeautifulSoup (BS4), and computer vision using OpenCV and YOLOv8. It also includes modules on Generative AI (GenAI) overview, Ollama overview, LLMOps and fine-tuning with Hugging Face, LangChain & LangGraph, and Streamlit for building interactive AI dashboards.
This repository is structured to provide hands-on experience through code examples, assignments, and real-world applications.
➡️ Click here to download Python
- Official website: python.org
- Choose the version that matches your operating system:
- Windows
- macOS
- Linux
- ✅ “Add Python to PATH”
➡️ Click here to download Miniconda
- Miniconda is a minimal installer for Conda
- Works on all platforms (Windows, macOS, Linux)
➡️ Click here to download VS Code
- Lightweight, fast, and powerful code editor
- Supports:
- Python
- Conda
- Jupyter
- Recommended Extensions:
- Python
- Jupyter
- IDE Setup
- Python Virtual Environment
- VS Code Shortcuts
- Python Basics
- Variables, data types, String Formatting, Operators, User Input
- Control structures: if-else, loops
- Lists, Tuples, Sets, Dictionaries, Arrays
- Assignments (Lists, Tuples, Sets, Dictionaries)
- Functions with Functions' Examples
- Lambda Functions, Map Functions, Filter Functions
- Functions Assignment
- Python Imports (Modules and Packages, Standard Library Overview)
- Packages Assignment
- File Handling (File Operations, File Paths)
- File Handling Assignment
- Exception Handling (Try, except, finally)
- Exception Handling Assignment
- Classes and Objects (OOPs)
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction
- Magic Methods
- Operator Overloading
- Custom Exceptions
- OOPs Assignment
- Dataset Analysis
- Reading data from CSV files.
- Overview of Dataset
- Data Types and Structure
- Descriptive Statistics
- Correlation Analysis
- Identifying Missing Values
- Strategies for Handling Missing Data
- Sorting DataFrames
- Filtering Data Based on Conditions
- Grouping Data
- Aggregating Functions
- Importance of Visualization
- Visualization Techniques
- Matplot Fundamentals
- Line Chart
- Pie Chart
- Scatter Charts
- Heatmaps
- Bubble Charts
- Histogram
- Numpy Fundamentals
- Installing and Importing NumPy
- Creating NumPy Arrays
- Comparing NumPy Arrays and Python Lists
- Performance Comparison
- Input Handling and Checking Variable Types
- Converting the List to a NumPy Array
- Weekly Temperature Analysis Using NumPy
- Weekly Sales Data Analysis Using NumPy
- Introduction to 2D Arrays
- Grocery Store Inventory Analysis
- Pandas Fundamentals
- Requirements.txt File Explanation
- Introduction to Kaggle Datasets
- Downloading Datasets from Kaggle
- Exploring and Visualizing Data
- Basic Data Preprocessing
- Applying Machine Learning Models on Kaggle Datasets
- Conditional Filtering: Filtering data based on conditions.
- Handling Missing Data: Managing and identifying missing data.
- Sorting and Grouping: Techniques for organizing data.
- Data Manipulation:
- Renaming Columns
- Adding New Columns
- Summing and Averaging Column Values
- Temperature Analysis for Sahiwal and Okara
- Reading the Uploaded CSV File
- Summary of Statistics for Numerical Columns in Dataset
- Dataset Dimensions (Number of Rows and Columns)
- Dataset Filtering
- Data Visualization
- Hottest Days
- Average Temperature Line
- Plot the Graph with the Highest Temperature Highlighted
- Monthly Temperature Trends
- Box Plot to Visualize Temperature Spread
- Plotting the Hottest Days
- OpenCV Overview
- RGB to Grayscale
- Guassian Blue
- Canny Edge Detection
- Object Detection through Template and Frame using CV2
- YOLO Algorithm Overview
- Object Localization
- Training of a Neural Network
- Why do we need YOLO?
- Intersection over Union (IOU)
- Introduction to YOLOv8
- Roboflow Overview
- Open Image Datasets
- YOLOv8 Lab Tasks
- YOLOv8 Lab
- Object Detection from Image
- Object Detection from Video
- Object Detection from Camera
- Object Segmentation
- Confidence Score
- Running Segmentation on Video
- Web Scrapping using BeatifulSoup
- Basic Workflow of Web Scraping
- How to Add a User-Agent Header
- HTML Parse Tree
- Accessing the Childs of the Tree
- Fetching Data from Nested Childs
- BeatifulSoup find_all() function
- Extracting the Original Text from the HTML Elements and Selectors
- Extracting the data from Elements and Selectors
- Introduction to GenAI
- Generative Models
- Hallucinations
- Model Garden by Google
- Vertex AI Studio
- Ollama Overview
- Run Ollama Models on Local Machine
- Run Ollama Models with Python GUIs
- Ollama and Postman
- LLMs Theory (Helping book is also uploaded.)
- LLMOps Fundamentals
- Transformers Fundamentals
- Introduction to Hugging Face
- Using pre-trained models for text summarization
- Implementing Text Summarization using Hugging Face Transformers
- Fine-tuning models for better performance
- Evaluating summarization results
- Langchain APIs
- Data Ingestion
- Data Transformer
- Embeddings
- Vector Storage
-
Introduction to Streamlit
- What is Streamlit?
- Key features and benefits
- Comparison with other Python web frameworks
- Real-world use cases
-
Getting Started with Streamlit
- Installing Streamlit using
pip - Running your first app with
streamlit hello - Basic command-line usage
- Understanding file structure and app execution
- Installing Streamlit using
-
Streamlit Layouts
- Using
st.title,st.header,st.subheader, andst.text - Creating columns with
st.columns - Using
st.sidebarfor controls or navigation - Displaying images, charts, and dataframes
- Organizing layout with
st.containerandst.expander
- Using
-
Streamlit Widgets
- Input widgets:
st.text_input,st.number_input,st.slider,st.selectbox - Interaction controls:
st.button,st.checkbox,st.radio - Handling user input and dynamic display
- Forms and submission using
st.formandst.form_submit_button
- Input widgets:
- Clone this repository to your local machine:
git clone https://github.com/Jamil226/AI-Training-Summe
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
For queries please email me at: jamil138.amin@gmail.com
FOSS - Free and Open Source Licenses
MIT - Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so.