Welcome to the everything data. repository! This repository serves as a collection of Jupyter notebooks summarizing the contents from various courses I have learned, covering a wide range of topics from statistics to machine learning. Whether you're a beginner or an experienced data enthusiast, you'll find valuable resources and insights within this repository.
In today's data-driven world, knowledge of data analysis and machine learning has become increasingly essential. This repository aims to provide a comprehensive collection of Jupyter notebooks that summarise the concepts, techniques, and algorithms learned from various courses that I have taken and completed. These notebooks are designed to be self-contained, providing explanations, code snippets, and examples for easy understanding and practical application.
Here's a list of the notebooks available in this repository to date:
| Notebooks | Description of use |
|---|---|
| Intro to Statistics | These notebooks covers the fundamental concepts of statistics, including descriptive statistics, probability, hypothesis testing, and statistical distributions. |
| Vectorization and Broadcasting | This notebook dives deep into some of NumPy's most powerful features, Vectorization and Broadcasting |
| PCA (Principal Component Analysis) | This notebook aims to explain PCA for dummies like myself :D |
| TF-IDF (Text Frequency-Inverse Document Frequency | This notebook explains the text encoding method TF-IDF commonly used for Natural Language Processing |
To get started with the notebooks in this repository, follow these steps:
- Clone this repository to your local machine using the following command:
git clone https://github.com/ssim3/everything-data.git
- Ensure you have Jupyter Notebook or JupyterLab installed on your system.
- Open Jupyter Notebook or JupyterLab and navigate to the cloned repository directory.
- Open the desired notebook using the Jupyter interface, and start exploring the content.
I aim to upload 1 new notebook every month (1 new course a month). However, please do understand if there is a delay in release.
Contributions are welcome and encouraged! If you'd like to contribute to this repository, please follow these guidelines:
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Fork the repository and clone it to your local machine.
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Create a new branch for your feature or bug fix.
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Make your changes and ensure the notebooks run successfully.
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Commit your changes and push them to your forked repository.
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Submit a pull request, describing the changes you've made.