【学习】(Python)白手起家机器学习

Python implementations of various Machine Learning models and algorithms from scratch.

While some of the matrix operations that are implemented by hand (such as calculation of covariance matrix) are  available in numpy I have decided to add these as well to make sure that I understand how the linear algebra is applied.

The purpose of this project is purely self-educational.

Current implementations:

Supervised Learning:

• Adaboost

• Decision Tree

• K Nearest Neighbors

• Linear Discriminant Analysis

• Linear Regression

• Logistic Regression

• Multi-class Linear Discriminant Analysis

• Multilayer Perceptron

• Naive Bayes

• Perceptron

• Random Forest

• Ridge Regression

• Support Vector Machine

Unsupervised Learning:

• Gaussian Mixture Model

• Principal Component Analysis

• K-Means

链接：

https://github.com/eriklindernoren/ML-From-Scratch

原文链接：

http://weibo.com/1402400261/ExjMl8LVP?from=page_1005051402400261_profile&wvr=6&mod=weibotime&type=comment#_rnd1488114829015