# CS229-ML-Implementation

**Repository Path**: limbercode/CS229-ML-Implementation

## Basic Information

- **Project Name**: CS229-ML-Implementation
- **Description**: Implementation of cs229(Machine Learning taught by Andrew Ng) in python.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-16
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[2019-11-23] Update: Add Conditional Generative Adversarial Nets on MNIST in 01-UnsupervisedLerning
<p align="center">
    <img src="GIF/19999.png" width="480"\>
</p>

[2019-11-7] Update: Add Generative Adversarial Nets on MNIST in 01-UnsupervisedLerning
<p align="center">
    <img src="GIF/31000_generated.jpg" width="480"\>
</p>

[2019-11-5] Update: Add 99.76% accuracy mnist model
<p>
    <img src="05-Mnist/data_vis.jpg" width="480">
</p>

# CS229-ML-Implements(CS229机器学习算法的Python实现)
Implements of cs229(Machine Learning taught by Andrew Ng) in python.

# I must pay all my attention to my papers, therefore the repository won't update soon.

# CS229 Machine Learning Xmind:
![](https://github.com/Sierkinhane/CS229-ML-Implements/blob/master/GIF/ml-xmind.png)

# CS229 Machine Learning course and notes:
[OpenCourse](http://open.163.com/special/opencourse/machinelearning.html)

[cs229-Notes](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/CS229-Notes)

# Syllabus：
  * [Linear Regression](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/01-LinearRegression)
  
  * [Normal Equation](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/01-LinearRegression)
  
  * [Locally Weighted Regression](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/01-LinearRegression)
  
  * [Logistic Regression](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/02-Classification)
  
  * [Perceptron Algorithm](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/02-Classification)
  
  * [Newton Method](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/02-Classification)
 
  * [Softmax Regression](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/03-GeneralizedLinearModels)
  
  * [Gaussian Discriminant Analysis](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/04-GenerativeLearningAlgorithms)
  
  * [Naive Bayes](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/04-GenerativeLearningAlgorithms/naive_bayes) spam-filter

# Examples

## [Linear Regression](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/01-LinearRegression)
![](https://github.com/Sierkinhane/CS229-ML-Implements/blob/master/GIF/regression.gif)

## [Locally Weighted Regression](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/01-LinearRegression)
![](https://github.com/Sierkinhane/CS229-ML-Implements/blob/master/GIF/LWR.gif)

## [Logistic Regression](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/02-Classification)
![](https://github.com/Sierkinhane/CS229-ML-Implements/blob/master/GIF/logisticR.gif)

## [Softmax Regression](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/03-GeneralizedLinearModels)
![](https://github.com/Sierkinhane/CS229-ML-Implements/blob/master/GIF/softmaxR.gif)

## [Gaussian Discriminant Analysis](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/04-GenerativeLearningAlgorithms)
![](https://github.com/Sierkinhane/CS229-ML-Implements/blob/master/GIF/GDA.png)
![](https://github.com/Sierkinhane/CS229-ML-Implements/blob/master/GIF/GDA2.png)

## [Naive Bayes](https://github.com/Sierkinhane/CS229-ML-Implements/tree/master/00-SupervisedLearning/04-GenerativeLearningAlgorithms/naive_bayes) spam-filter

## TO BE CONTINUED