Welcome to the Learn-Machine-Learning repository!
This repository is intended to provide a comprehensive and practical approach to learning machine learning from scratch. It is designed for beginners who have little to no experience with machine learning and want to start exploring this exciting field.
The content in this repository is organized in a structured manner, starting with the basics of Python programming language and gradually building up to more advanced topics such as neural networks, deep learning, and natural language processing. Each section contains detailed explanations, code snippets, and exercises to help reinforce the concepts learned.
Whether you're looking to kickstart your career in data science or simply curious about the world of machine learning, this repository provides a solid foundation to get started. So, grab your laptop, install the necessary software, and let's dive into the world of machine learning together!
Here are some additional resources you may find helpful in learning and practicing machine learning:
- Machine Learning by Andrew Ng on Coursera
- Deep Learning Specialization by Andrew Ng on Coursera
- Introduction to Machine Learning with Python on Coursera
- Applied Data Science with Python Specialization on Coursera
- Machine Learning Crash Course with TensorFlow on Google Developers
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Python Machine Learning
- Deep Learning with Python
- Kaggle - A platform for data science and machine learning competitions.
- GitHub - A code hosting platform that allows version control and collaboration for machine learning projects.
- TensorFlow - An open-source software library for machine learning and artificial intelligence.
- Scikit-learn - A machine learning library for Python.
- PyTorch - An open-source machine learning library based on the Torch library.
Keep learning and have fun!
Thank you for considering contributing to Learn-Machine-Learning! This guide will help you understand how to make your contribution to the project.
There are several ways to contribute to this project:
Report a bug or request a new feature Submit a pull request to improve the existing content Improve the documentation Reporting Bugs and Requesting Features If you find a bug in the code or want to request a new feature, please open a new issue on GitHub. Be sure to describe the bug or feature in detail, and provide any relevant code or error messages.
Before submitting a pull request, please ensure that your code meets the following requirements:
- Code should be well-commented and follow PEP 8 guidelines.
- Any new code or changes should be well-documented.
- Any new functionality should be tested with appropriate test cases.
To submit a pull request, please follow these steps:
- Fork the repository.
- Create a new branch with a descriptive name.
- Make your changes.
- Commit your changes with a clear message explaining the changes you made.
- Push your changes to your forked repository. 6.Open a pull request on the original repository.
- Improving Documentation If you notice any errors or omissions in the documentation, or if you have suggestions for how to improve it, please open a new issue on GitHub or submit a pull request with your changes.
Thank you for your contributions to Learn-Machine-Learning!