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Introductory Probability and Statistics for Machine Learning and Data Science

Description

These notebooks are for an introductory course covering the fundamental concepts of probability and statistics essential for machine learning and data science.

Prerequisites

- python
- numpy
- pandas
- matplotlib

Course Content

This course consists of a collection of Jupyter notebooks created by a LLM.

Additional Resources

- Books
 - Practical Statistics For Data Science
 - Statistics Robbert S.Witte, John S.Witte

Contributing

We welcome contributions to improve the existing notebooks or to create new notebooks for topics that are currently not covered. There are two ways to contribute:

  1. Improving the Notebooks - If you notice errors or have suggestions to make existing notebooks better, feel free to contribute to them. Simply fork the repository, make your changes, and submit a pull request with a clear description of the changes made.

  2. Creating New Notebooks - If you have a topic that you would like to create a notebook for, please contact us at tabari.mahyar@gmail.com first to verify the subject and ensure there is no overlap with existing notebooks. Once the topic is approved, please follow the same process as above and submit a pull request with the new notebook.

We appreciate your contributions to make our course materials better!

License

All course materials are licensed under the Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license. This means that the materials are completely free to use without any restrictions and no attribution is required. For more information about the license, please visit the Creative Commons website.

To-Do

- mesures of central tendency
- histogram
- permutation test
- bootstrapping

Ongoing

- outlier
- permutation test

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Lecture Notes for Probability and Statistics for Machine Learning

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