https://github.com/playeredlc/datascience-learnings

Some projects developed during my studies with the intention to cover some of the fundamental concepts and techniques used in Data Science and Machine Learning.
https://github.com/playeredlc/datascience-learnings

Last synced: 8 days ago
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Some projects developed during my studies with the intention to cover some of the fundamental concepts and techniques used in Data Science and Machine Learning.

• Host: GitHub
• URL: https://github.com/playeredlc/datascience-learnings
• Owner: playeredlc
• Created: 2021-03-03T05:16:45.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-03-07T14:56:58.000Z (over 2 years ago)
• Last Synced: 2024-10-20T09:16:12.218Z (about 1 month ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 27.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        


	Data Science Learnings





[About](#about)   |   

[Table of content](#table-of-contents)   |   

[Tech](#technologies--frameworks--packages)



### About

The purpose of this repository is to keep together projects developed during my Data Science and Machine Learning study journey. Thus, it does not contain a single project and folders may be not related with each other at all. These projects have the intention to cover some of the fundamental concepts and techniques known in this field.

For the sake of comprehension each relevant topic has its own Readme file, which can be found browsing the directories in the root or following the table of contents below.

### Table of contents

* [Linear Regression](https://github.com/playeredlc/DataScience-Learnings/tree/master/Linear-Regression#linear-regression)

	* [Simple Linear Regression](https://github.com/playeredlc/DataScience-Learnings/tree/master/Linear-Regression#simple-linear-regression)

	* [Multivariable Linear Regression](https://github.com/playeredlc/DataScience-Learnings/tree/master/Linear-Regression#multiple-linear-regression)

* [Gradient Descent](https://github.com/playeredlc/DataScience-Learnings/tree/master/Gradient-Descent#gradient-descent)

    * [Mean Squared Error with Gradient Descent](https://github.com/playeredlc/DataScience-Learnings/tree/master/Gradient-Descent#mean-squared-error-with-gradient-descent)

    * [Learning Rate](https://github.com/playeredlc/DataScience-Learnings/tree/master/Gradient-Descent#the-learning-rate)

    * [Multiple Minima vs Initial Guess](https://github.com/playeredlc/DataScience-Learnings/tree/master/Gradient-Descent#the-learning-rate)

* [Naive Bayes Classifier](https://github.com/playeredlc/DataScience-Learnings/tree/master/Naive-Bayes#naive-bayes-classifier)

	* [Spam-Filter](https://github.com/playeredlc/DataScience-Learnings/tree/master/Naive-Bayes#spam-filter)

* [Artificial Neural Networks](https://github.com/playeredlc/DataScience-Learnings/tree/master/Neural_Networks#neural-networks)

	* [Image Recognition (CIFAR10 dataset)](https://github.com/playeredlc/DataScience-Learnings/tree/master/Neural_Networks#image-recognition)

	* [Handwritten Digits Recognition (MNIST dataset)](https://github.com/playeredlc/DataScience-Learnings/tree/master/Neural_Networks#handwritten-digits-recognition)

### Technologies / Frameworks / Packages

* [Numpy](https://numpy.org/)

* [Pandas](https://pandas.pydata.org/)

* [Scikit-learn](https://scikit-learn.org/)

* [Matplotlib](https://matplotlib.org/)

* [Searborn](https://seaborn.pydata.org/)

* [Statsmodel](https://www.statsmodels.org/)

* [Pillow](https://pillow.readthedocs.io/)

* [Tensorflow](https://www.tensorflow.org/)

* [Tensorboard](https://www.tensorflow.org/tensorboard)

* [Keras](https://keras.io/)

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 >  Developed by edlc. [Get in touch!](https://github.com/playeredlc) :metal: