https://github.com/jfmdev/simple_ml

Simple examples of Machine Learning notebooks
https://github.com/jfmdev/simple_ml

artificial-intelligence jupyter-notebook kaggle machine-learning python

Last synced: about 1 month ago
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Simple examples of Machine Learning notebooks

• Host: GitHub
• URL: https://github.com/jfmdev/simple_ml
• Owner: jfmdev
• License: mpl-2.0
• Created: 2020-03-27T20:02:29.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2021-01-20T19:24:32.000Z (about 4 years ago)
• Last Synced: 2024-11-14T10:12:56.690Z (3 months ago)
• Topics: artificial-intelligence, jupyter-notebook, kaggle, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 3.42 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md
  • License: license.txt

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README

        
# Simple ML

This repository contains examples of Jupyter notebooks and Python files that uses Machine Learning techniques to build simple models or agents.

 * The bots on `candy-cane` folder uses _reinforcement learning_ to solve a variant of the "multi-armed bandit" problem.

 * The notebook on `disaster` folder uses _natural language processing_ to predict if a Tweet is announcing an emergency.

 * The notebook on `houses` folder uses _linear regression_ and _ridge regression_ models to predict the sales prices of houses.

 * The notebook on `mushrooms` folder uses _descision tree_ and _random forest_ models to predict which mushrooms are edible and which are poisonous.

 * The bot on `rock-paper-scissors` folder uses _reinforcement learning_ to play the Rock Paper Scissors game.

 * The notebook on `santa-tour` folder uses _linear programming_ to find the best way to schedule tours to a site with capacity constraints. 

 * The notebook on `titanic` folder uses _descision tree_ and _random forest_ models to predict which passengers survived the Titanic sink. 

License

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All the code in this repository is free software; you can redistribute it and/or modify it under the terms of the Mozilla Public License v2.0.  

You should have received a copy of the MPL 2.0 along with this repository, otherwise you can obtain one at http://mozilla.org/MPL/2.0/.