AI Lab

Artificial Intelligence and machine learning laboratory assignments developed with Python as part of university coursework.

Overview

This repository contains practical exercises and experiments related to Artificial Intelligence and Machine Learning.

The current repository includes Python programming assignments and a Jupyter Notebook used for machine learning coursework.

It is maintained as an academic archive of laboratory exercises and experimentation.

Contents

Assignment 01

Assignment01 contains a Python programming exercise focused on processing student grade data.

The assignment requires reading student records from a text file and calculating whether each student has passed or failed based on weighted assessment scores.

The exercise demonstrates:

• File handling
• String processing
• Data parsing
• Dictionaries
• Functions
• Conditional logic
• Numerical calculations

The assignment uses multiple assessment components, including quizzes, assignments, midterm, and final exam scores.

Machine Learning Notebook

The repository also contains:

ML_41.ipynb

This Jupyter Notebook represents a machine learning coursework experiment.

Project Structure

AI-Lab/
├── Assignment01/
│   ├── Assignment01.ipynb
│   ├── README.md
│   └── data.txt
│
├── ML_41.ipynb
└── README.md

Technologies

• Python
• Jupyter Notebook
• Data Processing
• Machine Learning

Learning Objectives

The exercises in this repository provide practical experience with:

• Python programming
• Functions
• Dictionaries
• File handling
• Data parsing
• Conditional statements
• Numerical calculations
• Jupyter Notebooks
• Machine learning experimentation

Academic Context

This repository is an academic laboratory archive created to practice programming, Artificial Intelligence, and Machine Learning concepts.

The projects are intended primarily for educational purposes rather than production use.

Project Status

This repository is maintained as a coursework and laboratory archive.

The exercises represent individual academic tasks and experiments rather than a single production application.

Future Improvements

Possible improvements include:

• Adding documentation for individual notebooks
• Adding dataset descriptions
• Standardizing assignment structure
• Adding reproducible environment configuration
• Adding visualizations for machine learning experiments
• Adding evaluation metrics where applicable
• Adding tests for Python assignments

Author

Ali Valizade

Python Developer | Django | AI, NLP & Automation | University Instructor

GitHub: https://github.com/AliValizade