A machine learning algorithm library in pure Python with mini project included for every algorithm.
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Adaboost
Bayes
KNN
KTrees
LogisticRegression
SVM
.DS_Store
README.md

README.md

MyMachineLearning

Repo Intro

This repo is to construct a basic Machine Learning Algorithm library for learning and testing, each algorithm comes with a classic miniproject application.

File Structure

Each directory include an algorithm with a mini project using this algorithm(data included), the projects as well as the algorithms are listed in a recommended reading sequence:

-- KNN

    -- kNeibohood.py
      """ core algorithm implementation"""
    -- imageRecognizer.py
      """ mini project, MNIST image recognizer"""
    -- digits
      """ MNIST dataset"""
    -- basicFunction.py
      """ Helper function"""
      
-- KTrees

    -- ktrees.py
      """ core algorithm implementation"""
    -- lenseproject.py
      """ mini project, lense recognizer"""
    -- lenses.txt
      """ dataset for lense project"""
    -- plottree.py 
      """ helper method to help you plot your ktrees data structure."""
      
-- Bayes
 
   -- bernoullibayers.py
      """ core algorithm implementation"""
    -- spamproject.py
      """ mini project, spam recognizer"""
    -- email
      """ dataset for spam project"""
      
-- Logistic Regression
 
   -- logisticRegression.py
      """ core algorithm implementation"""
    -- horseproject.py
      """ mini project, spam recognizer"""
    -- horseClinicTest.txt & horseClinicTraining.txt
      """ dataset for spam project"""
      
-- SVM
 
   -- svm.py
      """ core algorithm implementation"""

-- Adaboost
 
   -- adaboost.py
      """ core algorithm implementation"""      

How to use

1, download the repo to local, a star to the repo is appreciated

2, make sure Python2 is installed, a virtual env is recommended

3, pip install -r requirement.txt

4, run core algorithm file or miniproject file directly.

More to do

A lot of algorithm are coming soon, include:

[x] SVM

[x] Adaboost

[] Regression and Tree Regression

[] Kmeans

[] EM

[] PCA

[] SVD

Special Thanks

This repo has referenced some content and dataset of the book Machine Learning in Action(https://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181/ref=sr_1_1?ie=UTF8&qid=1508746100&sr=8-1&keywords=Machine+Learning+in+Action), Thanks a lot for this great handbook.

This repo also referenced from stanford CS229 Machine Learningcourse, Link:

http://cs229.stanford.edu/

Thanks a lot for the great materialis.

Contact Me

Email: nick_fandingwei@outlook.com

Twitter: https://twitter.com/nick_fandingwei

For Chinese user, zhihu is the fastest way to get response from me: https://www.zhihu.com/people/NickWey

You can also check my tech blog for more: http://nickiwei.github.io/

Consider to follow me on Zhihu, Twitter and Github, thanks!