Machine Learning Exercises

Welcome to the Machine Learning Exercises repository! This repository contains various exercises, tutorials, and projects that cover key concepts in machine learning. The aim is to help learners deepen their understanding through hands-on coding and problem-solving.

Table of Contents

1. Introduction
2. Prerequisites
3. Installation
4. Directory Structure
5. Exercises
6. Contributing
7. License

Introduction

This repository is a collection of machine learning exercises designed to cover different aspects of machine learning, from data preprocessing to model evaluation. It is ideal for learners who are looking to strengthen their skills in:

• Supervised learning
• Unsupervised learning
• Feature engineering
• Model evaluation and tuning
• Data visualization

Prerequisites

To follow along with the exercises, you need a basic understanding of:

• Python programming
• Fundamental machine learning concepts
• Libraries like NumPy, pandas, scikit-learn, and matplotlib

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/machine-learning-exercises.git

2. Navigate to the project folder:

   cd machine-learning-exercises

3. Install the required dependencies:

   pip install -r requirements.txt

Directory Structure

The repository is organized as follows:

machine-learning-exercises/
│
├── data/                       # Datasets for exercises
├── notebooks/                  # Jupyter notebooks for exercises
├── scripts/                    # Python scripts for exercises
├── models/                     # Saved machine learning models
├── README.md                   # This README file
└── requirements.txt            # List of dependencies

Exercises

Here is a list of some of the exercises included in this repository:

1. Data Preprocessing
   Learn how to clean, normalize, and preprocess datasets.

2. Linear Regression
   Build a simple linear regression model to predict continuous outcomes.

3. Classification Algorithms
   Implement classification algorithms like Logistic Regression, Decision Trees, and Random Forest.

4. Clustering
   Work with unsupervised learning techniques such as K-Means clustering.

5. Model Evaluation
   Evaluate model performance using techniques like cross-validation, confusion matrices, and ROC curves.

Each exercise folder contains a detailed Jupyter notebook explaining the steps, along with the required datasets.

Contributing

Contributions are welcome! If you'd like to improve the exercises or add new ones, feel free to submit a pull request. Please make sure to:

• Follow the repository’s structure
• Add clear instructions for new exercises
• Ensure your code is well-documented

License

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