Welcome to the Machine Learning Beginning Repo! This repository serves as a starting point for individuals new to the world of machine learning. Whether you're a beginner looking to learn the basics or an experienced developer exploring new concepts, we hope you find this repository helpful.

• Introduction
• Getting Started
• Prerequisites
• Installation
• Project Structure
• Usage
• Contributing
• License

In this repository, we aim to provide simple and easy-to-understand examples of fundamental machine learning concepts, along with hands-on exercises and code implementations. Our goal is to help you grasp the essential aspects of machine learning and build a solid foundation for your journey into this exciting field.

To get started with the examples and exercises in this repository, follow these steps:

1. Clone this repository to your local machine using the following command:

2. Install the required dependencies. See the Installation section for detailed instructions.

3. Explore the project structure and code files to understand the different concepts covered in this repository.

Before you begin, ensure you have the following prerequisites:

• Basic understanding of programming concepts (Python knowledge is preferred but not mandatory).
• Familiarity with linear algebra and calculus (for advanced concepts).
• Knowledge of basic machine learning terminology is a plus but not required.

To set up the environment for running the examples and exercises, follow these steps:

1. Ensure you have Python 3.x installed on your machine. You can download it from the official Python website: https://www.python.org/downloads/

2. Install the required Python packages using pip by running the following command:

The repository is organized as follows:

The examples directory contains Python scripts that demonstrate the implementation of various machine learning algorithms. The exercises directory contains starter code for exercises that you can try on your own. The data directory includes example datasets for some of the exercises.

You can use the examples and exercises in this repository as a starting point for your machine learning projects. Study the code, modify it, and experiment with different datasets to solidify your understanding.

To run an example script, use the following command:

To attempt an exercise, open the corresponding exercise_XX.py file and follow the instructions provided.

We welcome contributions from the community! If you find any issues or want to improve the existing examples or add new ones, please submit a pull request. Before making significant changes, open an issue to discuss your ideas and ensure they align with the project's objectives.

This project is licensed under the MIT License. You are free to use, modify, and distribute the code as long as you retain the original license header.