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mlstudy's Introduction

About

This is just a project of ML demos. Most demos are from here :

https://github.com/apachecn/AiLearning

https://github.com/Madhu009/Deep-math-machine-learning.ai

Supervised

Regression

  • least square
  • locally weighted linear regression
  • ridge regression
  • isotonic regression
  • kernel ridge regression
  • support vector regression
  • gradient descent
  • linear unit
  • model tree
  • regression tree

Classification

  • adaboost
  • decision tree
  • k nearest neighbors
  • logistic regression
  • naive bayes
  • random forest
  • support vector machines

Unsupervised

Clustering

  • k-means
  • bisecting k-means

Correlation Analysis

  • apriori
  • frequent patten growth

Dimensionality Reduction

  • principal component analysis
  • singular value decomposition

Reinforcement

Model Based

  • dynamic programming

Model Free

  • monte carlo
  • temporal difference

Deep Reinforcement

  • deep q-learning

NeuralNetwork

  • ann
  • cnn
  • rnn
  • lstm
  • pytorch

How to build

  1. install python 3.6+

  2. install packages

    pip install gym
    pip install gym[atari] (linux is better)
    pip install keras
    pip install matplotlib
    pip install numpy
    pip install pydotplus
    pip install scikit-image
    pip install sklearn
    pip install tensorflow
  1. install packages to get data
    pip install bs4
    pip install feedparser
    pip install jieba
    pip install python-votesmart (do not support python3.6)
  1. install pytorch
    # windows python3.6
    # 工具下载 http://download.pytorch.org/whl/cu90/torch-0.4.1-cp36-cp36m-win_amd64.whl
    pip3 install torch-0.4.1-cp36-cp36m-win_amd64.whl
    pip3 install torchvision
  1. install GraphViz

  2. use IDE like pycharm to open the project

Algorithms

CheatSheet_sklearn

Cheet Sheet Sklearn

CheatSheet_sklearn

Cheet Sheet SAS

CheatSheet_sas

Reference

https://github.com/apachecn/AiLearning

https://medium.com/deep-math-machine-learning-ai

mlstudy's People

Contributors

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Watchers

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