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Complete-NumPy 🚀

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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.

📚 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.

🛠️ 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

🚀 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.

📁 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.

🤝 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.

📜 License

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

💡 Author

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

Happy Coding! 😊