Welcome to the Data Science Notes Repository! This repository serves as a collection of my personal notes on data science concepts, techniques, and books that I have explored. The goal is to create a comprehensive resource for fellow data enthusiasts and provide insights into various data science topics.
In this repository, you will find Jupyter Notebook files (.ipynb) and pdf files containing my notes on different data science subjects. These notes are the result of my learning journey, encompassing a variety of data science books, courses, and practical projects. By organizing and sharing these notes, I aim to provide a valuable resource for anyone interested in data science.
The repository covers a wide range of data science topics, including but not limited to:
- Forecasting Practices and Principals 3rd ed.
- Interpretable Machine Learning - A Guide for Making Black Box Models Explainable
- Drift Detection in Time-Series Data
- Short Summaries of ML algorithms
- And much more!
Feel free to explore and delve into the notebooks to expand your knowledge and gain insights into these topics.
I welcome contributions from the data science community to make this repository more comprehensive and valuable. If you would like to contribute, please follow these steps:
- Fork the repository.
- Create a new branch for your contribution.
- Make your changes and enhancements.
- Test your changes thoroughly.
- Submit a pull request, describing the changes you made.
By contributing to this repository, you acknowledge that your contributions will be licensed under the same license as this project.
This project is licensed under the MIT License.