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

3. install packages to get data

    pip install bs4
    pip install feedparser
    pip install jieba
    pip install python-votesmart (do not support python3.6)

4. 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

5. install GraphViz

6. use IDE like pycharm to open the project

Algorithms

Cheet Sheet Sklearn

Cheet Sheet SAS

Reference

https://github.com/apachecn/AiLearning

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