Need help with Coursera-ML-using-matlab-python?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

TingNie
548 Stars 327 Forks 37 Commits 2 Opened issues

Description

coursera吴恩达机器学习课程作业自写Python版本+Matlab原版

Services available

!
?

Need anything else?

Contributors list

No Data

ML-code-using-matlab-and-python

coursera吴恩达机器学习课程作业自写Python2.7版本,使用jupyter notebook实现,使代码更有层次感,可读性强。

本repository实现算法包括如下:

线性回归: linear_regression.ipynb

多元线性回归:linear_multiple.ipynb

逻辑回归:logic_regression.ipynb

正则化用于逻辑回归: logic_regularization.ipynb

模型诊断+学习曲线: learnCurve.ipynb

一对多分类模型:oneVSall.ipynb

神经网络模型:neuralNetwork.ipynb

SVM分类器:svm.ipynb

kmeans聚类:kmeans.ipynb

pca降维:pca.ipynb

高斯分布用于异常检测:anomaly_detection.ipynb

协调过滤推荐算法:Collaborative_Filter.ipynb

PS:网上其他参考资料分享:

1.课程作业原版是MATLAB版本(填空式编码):对应 machine-learning-ex1——ex8 文件夹

2.kaleko整理的jupyter notebooks版本:对应 courseramlipynb 文件夹

3.mstampfer对照原版作业格式整理的Python版本,可以尝试自己实现

4.AceCoooool整理的Python版本,有中文注释

5.如果需要了解更多算法知识,本人使用jupyter notebook整理的peter的《机器学习实战》代码

6.本人自写的,关于吴恩达(Andrew Ng)开设的深度学习课程deeplearning.ai的课程答案

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.