IntroToML

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Introduction to Machine Learning Tutorial

IntroToML 최신버젼 다운로드

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# IntroToML
Inroduction to Machine Learning Tutorial repository
===========================================================
This github repository contains code for the Introduction to Machine Learning Tutorial.  These examples allow you to explore
different algorithms including:
  Decision Trees
  Random Forests
  Neural Networks

Please see the following web page for meetings that have used this repository:
  * http://www.sepnet.ac.uk/sepnet-graduate-network/training-events-201920/machine-learning-and-ai-workshop-15-16-january-2020/
  * https://indico.stfc.ac.uk/event/175/
  
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Getting Started:
----------------
Clone the repository with the following command to get started with working with these examples. git clone --branch master https://github.com/adrianbevan/IntroToML.git

The examples pertaining to a given algorithm can be found in the different directories:

BDT - decision trees using SciKitLearn

NN  - Neural Networks using TensorFlow

Software Requirements:

The Python environment that these tutorials has been tested on is packaged in the Anaconda framework: https://www.anaconda.com

Install Anaconda and then obtain the following packages:
* scikit-plot (`conda install -c conda-forge scikit-plot` OR `pip install scikit-plot`)  
* opencv (`conda install -c conda-forge opencv` OR `pip install opencv-python`)  
* keras (`conda install -c conda-forge keras` OR `pip install keras`)
* tensorflow (`conda install -c conda-forge tensorflow` OR `pip install tensorflow`)
* pydotplus (`conda install -c conda-forge pydotplus` OR `pip install pydotplus`)

If you run into any `ModuleNotFound` errors and cannot work out which packages you are missing please call over someone to help you.

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Authors: Adrian Bevan (a.j.bevan@qmul.ac.uk)
         Joe Davies (j.m.m.davies@qmul.ac.uk)

Copyright (C) Queen Mary University of London

This code is distributed under the terms and conditions of the GNU Public License
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