Machine Learning (ML) Projects - AIUB

This repository contains a collection of machine learning projects developed as part of the Machine Learning course at American International University - Bangladesh (AIUB). It includes various Jupyter Notebooks demonstrating the implementation of machine learning algorithms and techniques, with a final project housed in the /Final/Project folder.

Course Information

For more details on the course, refer to the AIUB Undergraduate Course Catalog.
Note: Search for "Machine Learning" for specific course information.

Project Overview

The repository explores a range of machine learning concepts from basic models to more advanced techniques. The projects are implemented using popular libraries like scikit-learn, pandas, NumPy, and Matplotlib for model training, evaluation, and visualization.

Final Project:

The /Final/Project folder contains the capstone project for this course, which involves the implementation of a machine learning pipeline to solve a real-world problem using appropriate algorithms, data preprocessing, and model evaluation metrics.

Repository Structure

• 📂Final:

  • 📂Codes:
    • ANN_MNIST.ipynb
    • CNN_MNIST.ipynb
  • 📂Project:
    • ML-Project-Report.pdf
    • ML_Project.ipynb
    • README.txt
    • qt_dataset.csv

• 📂Mid:

  • 📂Codes:
    • Naive_Bayes.ipynb
    • 📂Linear Regression using Gradient Descent:
      • Linear_Regression_using_Gradient_Descent.ipynb
      • data.csv

• README.md

Key Topics Covered

• Data Preprocessing
• Model Evaluation
• Feature Selection
• Hyperparameter Tuning
• Cross-validation

How to Use

1. Clone the repository:

   git clone https://github.com/Raihan4520/ML.git

2. Install dependencies: Ensure you have Python installed along with the required libraries. You can install the dependencies using the following command:

   pip install <library_name>

3. Run Jupyter Notebooks: Open Jupyter Notebook and explore the individual notebooks or the final project.

   jupyter notebook

4. Final Project: Navigate to the /Final/Project folder and open ML_Project.ipynb to explore the final project implementation.

Technologies Used

• Python (Programming Language)
• Jupyter Notebook (Interactive Environment)
• scikit-learn (Machine Learning Library)
• pandas (Data Manipulation)
• NumPy (Numerical Computing)
• Matplotlib (Data Visualization)

Contact

If you have any questions or suggestions, feel free to reach out through the repository's issues or contact me directly.