https://github.com/himel-sarder/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.
https://github.com/himel-sarder/complete-numpy
data-science dataanalysis machine-learning ml numpy numpy-exercises numpy-library numpy-python numpy-tutorial pythonlibrarires
Last synced: 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/himel-sarder/complete-numpy
- Owner: Himel-Sarder
- License: mit
- Created: 2024-12-17T04:31:54.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-21T10:28:40.000Z (4 months ago)
- Last Synced: 2024-12-31T04:15:35.030Z (4 months ago)
- Topics: data-science, dataanalysis, machine-learning, ml, numpy, numpy-exercises, numpy-library, numpy-python, numpy-tutorial, pythonlibrarires
- Language: Jupyter Notebook
- Homepage:
- Size: 3.56 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
---
## 📚 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:
```bash
git clone https://github.com/Himel-Sarder/Complete-NumPy.git
```
2. Navigate to the folder:
```bash
cd Complete-NumPy
```
3. Install the dependencies:
```bash
pip install numpy pandas jupyter
```
---
## 🚀 Quick Start
1. Launch the Jupyter Notebook:
```bash
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:
```bash
git checkout -b feature/your-feature-name
```
3. Commit your changes:
```bash
git commit -m "Add your feature or fix"
```
4. Push to your branch:
```bash
git push origin feature/your-feature-name
```
5. Submit a pull request.
---
## 📜 License
This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.
---
## 💡 Author
Created and maintained by **Himel Sarder**. Feel free to reach out for questions, suggestions, or collaborations!
---
Happy Coding! 😊