https://github.com/tingnie/coursera-ml-using-matlab-python

coursera吴恩达机器学习课程作业自写Python版本+Matlab原版
https://github.com/tingnie/coursera-ml-using-matlab-python

coursera jupyter-notebook machine-learning python2-7

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

• Host: GitHub
• URL: https://github.com/tingnie/coursera-ml-using-matlab-python
• Owner: TingNie
• Created: 2017-10-15T07:36:07.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2018-01-11T11:47:51.000Z (almost 7 years ago)
• Last Synced: 2024-12-13T13:06:10.873Z (9 days ago)
• Topics: coursera, jupyter-notebook, machine-learning, python2-7
• Language: Jupyter Notebook
• Homepage:
• Size: 25.5 MB
• Stars: 898
• Watchers: 29
• Forks: 361
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# 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：

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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](https://github.com/kaleko/CourseraML)整理的jupyter notebooks版本：对应 coursera_ml_ipynb 文件夹

3.[mstampfer](https://github.com/mstampfer/Coursera-Stanford-ML-Python)对照**原版作业格式**整理的Python版本，可以尝试自己实现

4.[AceCoooool](https://github.com/AceCoooool/ML-Andrew-Ng)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](https://github.com/TingNie/Machine-learning-in-action)

6.本人自写的，关于吴恩达（Andrew Ng）开设的深度学习课程[deeplearning.ai](https://github.com/TingNie/deeplearning.ai-coursera)的课程答案