Specialized Python is a comprehensive and structured repository that offers well-documented code snippets for Python libraries widely used in Data Science, Machine Learning, Deep Learning, and AI. Designed for progressive learning, this repository serves as an invaluable reference for both beginners and experienced practitioners.
This repository provides:
- Concise, well-explained code snippets for essential Python libraries.
- Real-world applications to bridge the gap between theory and practice.
- Continuously updated content to keep up with emerging technologies.
- Optimized implementations for better efficiency and performance.
This repository includes structured tutorials and examples on the following libraries:
- Pandas – Advanced data manipulation and analysis.
- NumPy – High-performance numerical computations.
- Scikit-Learn – Core machine learning algorithms and model evaluation.
- XGBoost & LightGBM – Optimized gradient boosting techniques.
- Statsmodels – Statistical modeling and hypothesis testing.
- Matplotlib – Low-level data visualization library.
- Seaborn – Statistical visualization built on Matplotlib.
- TensorFlow & PyTorch – State-of-the-art deep learning frameworks.
- Keras – High-level neural network API built on TensorFlow.
- NLTK & SpaCy – Text preprocessing, tokenization, and NLP tasks.
- OpenCV – Image processing and computer vision techniques.
- FastAPI – High-performance API development with automatic documentation.
- REST API – RESTful services and integration methodologies.
- Flask & Django – Web frameworks for deploying ML models.
And many more...
Specialized-Python/
│-- Pandas/ NOT IN ACTION
│-- NumPy/ NOT IN ACTION
│-- Scikit-Learn/ NOT IN ACTION
│-- Seaborn/ NOT IN ACTION
│-- Matplotlib/ NOT IN ACTION
│-- FastAPI/ NOT IN ACTION
│-- REST API/ NOT IN ACTION
│-- TensorFlow/ NOT IN ACTION
│-- PyTorch/ NOT IN ACTION
│-- NLTK/ NOT IN ACTION
│-- SpaCy/ NOT IN ACTION
│-- OpenCV/ NOT IN ACTION
│-- Statsmodels/ NOT IN ACTION
│-- XGBoost/ NOT IN ACTION
│-- LightGBM/ NOT IN ACTION
│-- Flask/ NOT IN ACTION
│-- Django/ NOT IN ACTION
│-- README.md
Each folder contains structured code snippets, practical examples, and documentation.
git clone https://github.com/yourusername/Specialized-Python.gitcd Specialized-Python/PandasYou can execute the code snippets in Jupyter Notebook, VS Code, or PyCharm.
Ensure Python 3.x is installed. Install all necessary libraries with:
pip install pandas numpy scikit-learn seaborn matplotlib fastapi tensorflow torch nltk spacy opencv-python statsmodels xgboost lightgbm flask djangoFor optimal performance, consider setting up a virtual environment:
python -m venv env
source env/bin/activate # macOS/Linux
env\Scripts\activate # WindowsWe welcome contributions from the community! You can contribute by:
- Adding new code snippets and optimized implementations.
- Improving documentation with better explanations.
- Providing real-world use cases and examples.
- Fixing bugs and outdated code.
- Fork the repository.
- Create a new branch:
git checkout -b feature-branch
- Make your changes and commit:
git commit -m "Added optimized NumPy functions" - Push the changes:
git push origin feature-branch
- Create a Pull Request (PR) for review.
This repository is distributed under the MIT License, allowing open-source contributions and modifications.
For discussions, questions, or feedback, feel free to reach out:
- GitHub Issues – Report bugs or suggest improvements.
- Email – rayyan.connects@gmail.com
- LinkedIn – Rayyan Ashraf