Complete-NumPy 🚀

A comprehensive guide to mastering NumPy with practical examples and applications in machine learning. Perfect for learners and developers looking to deepen their knowledge of numerical computations in Python.

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📚 What's Inside

• Tutorials: Step-by-step explanations of NumPy concepts.
• Hands-On Notebook: Interactive Jupyter Notebook (Numpy ~ Himel.ipynb) with examples.
• Dataset: Real-world dataset (population.csv) for practice.
• Machine Learning Applications: Additional resources for ML enthusiasts.

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🛠️ Installation

Get started in three easy steps:

1. Clone this repository:

   git clone /~https://github.com/Himel-Sarder/Complete-NumPy.git

2. Navigate to the folder:

   cd Complete-NumPy

3. Install the dependencies:

   pip install numpy pandas jupyter

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🚀 Quick Start

1. Launch the Jupyter Notebook:

   jupyter notebook "Numpy ~ Himel.ipynb"

2. Follow the examples and experiment with the provided code.
3. Use the population.csv dataset to explore NumPy's data manipulation capabilities.

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📁 Repository Structure

• Numpy ~ Himel.ipynb: The main notebook with NumPy tutorials and examples.
• population.csv: A sample dataset for practice.
• LICENSE: Project license details.
• README.md: Overview of the repository.
• Complete Numpy ~ ML+: Supplementary files for advanced topics.

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🤝 Contributing

Want to contribute? Follow these steps:

1. Fork this repository.
2. Create a new branch:

   git checkout -b feature/your-feature-name

3. Commit your changes:

   git commit -m "Add your feature or fix"

4. Push to your branch:

   git push origin feature/your-feature-name

5. Submit a pull request.

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📜 License

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

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💡 Author

Created and maintained by Himel Sarder. Feel free to reach out for questions, suggestions, or collaborations!

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Happy Coding! 😊