Machine Learning with Python

This repository contains resources, code samples, and tutorials for learning and applying machine learning techniques using Python. It is designed for beginners and intermediate users who want to understand the fundamentals and practical aspects of machine learning.

Contents

• Introduction to Machine Learning: Overview of key concepts and terminology.
• Python Setup: Instructions for installing Python and required libraries.
• Data Preprocessing: Techniques for cleaning and preparing data.
• Supervised Learning: Examples using algorithms like Linear Regression, Decision Trees, and Support Vector Machines.
• Unsupervised Learning: Clustering and dimensionality reduction methods.
• Model Evaluation: Metrics and validation strategies.
• Projects: End-to-end machine learning projects with code and explanations.

Getting Started

1. Clone the repository:

   git clone https://github.com/sheikh-mohammad-rakib/Machine_leaning_with_python.git

2. Install dependencies:

   pip install -r requirements.txt

3. Explore the notebooks and scripts in the repository.

Requirements

• Python 3.7+
• NumPy
• pandas
• scikit-learn
• matplotlib
• Jupyter Notebook (optional)

Contributing

Contributions are welcome! Please open issues or submit pull requests for improvements.

License

This project is licensed under the MIT License.

Acknowledgements

Inspired by open-source machine learning resources and the Python community.