NumPy Basics

This project contains a comprehensive Jupyter Notebook that introduces the core concepts of NumPy, one of the most powerful libraries for numerical computing in Python. It is perfect for beginners who are starting their journey in data science, machine learning, or scientific computing.

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Features

• Introduction to NumPy arrays
• Array creation techniques
• Indexing, slicing, and reshaping arrays
• Vectorized operations and broadcasting
• Useful NumPy functions for data manipulation
• Real code examples with clear outputs

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Contents

• Numpy Basics.ipynb – The main notebook file containing:
  • Step-by-step explanations
  • Code examples
  • Output visualizations
  • Notes and best practices

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Getting Started

Prerequisites

Make sure you have the following installed:

• Python 3.x
• Jupyter Notebook or JupyterLab
• NumPy

You can install the required packages using pip:

pip install numpy notebook

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Running the Notebook

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. Launch Jupyter Notebook:

   jupyter notebook

3. Open Numpy Basics.ipynb and start learning!

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Who is this for?

• Students new to Python or data analysis
• Aspiring data scientists and ML enthusiasts
• Researchers or engineers exploring numerical computing
• Anyone preparing for technical interviews involving NumPy

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Contributing

Have suggestions or want to add more examples? Contributions are welcome!

1. Fork the repo
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open 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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Let's Connect

For questions, feedback, or collaboration, feel free to connect:

• GitHub: @rajtilak-2020
• LinkedIn: K Rajtilak

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Happy Coding!