Machine Learning Tasks

Welcome to the Machine Learning Tasks repository! This repository contains a collection of lab tasks, projects, and assignments related to Machine Learning (ML). It serves as a resource for learning and practicing various ML concepts and techniques.

Table of Contents

1. About the Repository
2. Project Structure
3. Prerequisites
4. Setup and Usage
5. License

About the Repository

This repository is structured to provide:

• Lab Tasks: Hands-on practice with key ML concepts and algorithms.
• Assignments: Challenging problems to deepen understanding.
• Projects: End-to-end implementations of Machine Learning solutions.

It is ideal for students, educators, and self-learners interested in exploring ML through practical examples.

Project Structure

The repository is organized into the following directories:

• ML Labs: Contains lab tasks that focus on fundamental ML techniques and algorithms. I have not included labs of all topics, you can find missing topics in assignments folder(files).
• ML Assignments: Includes assignments designed to reinforce theoretical concepts with practical implementation.
• ML Project: A comprehensive ML project on Clinical Insight Prediction System showcasing the application of various techniques like Artifical Neural Network (ANN), Support Vector Machine (SVM), Random Forest, and Logistic Regression to predict diseases eg. skin cancer, asthama, heart disease, and kidney disease.

Each directory contains detailed Jupyter notebooks and supporting files to help users understand and implement ML concepts.

Prerequisites

To work with this repository, you need:

• Python 3.11 or higher
• Jupyter Notebook or JupyterLab
• Basic understanding of Machine Learning

Setup and Usage

Required Libraries

Make sure you have the following Python libraries installed:

• numpy
• pandas
• matplotlib
• seaborn
• scikit-learn

Navigate the Repository

1. Open the desired directory (e.g., ML Labs, ML Assignments).
2. Open the Jupyter notebooks using Jupyter Notebook or JupyterLab.
3. Follow the instructions provided in each notebook.

License

This repository is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

Special thanks to instructors (Dr. Hashim Yaseen and Mr. Asif Ameer) , peers (Muhammad Saif and Muskan Ghani), and online resources that have guided the creation of these tasks and projects.