https://github.com/reata/machinelearning

Machine Learning Jupyter Notebook
https://github.com/reata/machinelearning

Last synced: 18 days ago
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Machine Learning Jupyter Notebook

• Host: GitHub
• URL: https://github.com/reata/machinelearning
• Owner: reata
• License: mit
• Created: 2017-06-15T05:59:21.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-12-23T12:56:24.000Z (about 7 years ago)
• Last Synced: 2024-12-04T20:40:45.553Z (3 months ago)
• Language: Jupyter Notebook
• Homepage: http://nbviewer.jupyter.org/github/reata/MachineLearning/tree/master/
• Size: 1.69 MB
• Stars: 0
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Support: Support Vector Machines.ipynb

Awesome Lists containing this project

README

        
MachineLearning

=========

本项目包含机器学习的Jupyter Notebook

### 目录

1. 监督学习（Supervised Learning）

    - [线性回归 Linear Regression](https://github.com/reata/MachineLearning/blob/master/Linear%20Regression.ipynb)

    - [逻辑回归 Logistic Regression](https://github.com/reata/MachineLearning/blob/master/Logistic%20Regression.ipynb)

    - [广义线性模型 Generalized Linear Models](https://github.com/reata/MachineLearning/blob/master/Generalized%20Linear%20Models.ipynb)

    - [生成学习算法 Generative Learning Algorithms](https://github.com/reata/MachineLearning/blob/master/Generative%20Learning%20Algorithms.ipynb)

    - [支持向量机 Support Vector Machines](https://github.com/reata/MachineLearning/blob/master/Support%20Vector%20Machines.ipynb)

    

2. 学习理论（Learning Theory）

    - [学习理论 Learning Theory](https://github.com/reata/MachineLearning/blob/master/Learning%20Theory.ipynb)

    - [正则化和模型选择 Regularization and Model Selection](https://github.com/reata/MachineLearning/blob/master/Regularization%20and%20Model%20Selection.ipynb)

    - [应用机器学习 Advice on Applying Machine Learning](https://github.com/reata/MachineLearning/blob/master/Advice%20on%20Applying%20Machine%20Learning.ipynb)

3. 非监督学习（Unsupervised Learning）

    - [k均值聚类 k-Means Clustering](https://github.com/reata/MachineLearning/blob/master/k-Means%20Clustering%20Algorithm.ipynb)

    - [高斯混合模型 Gaussian Mixture Models](https://github.com/reata/MachineLearning/blob/master/Gaussian%20Mixture%20Models.ipynb)

    - [EM算法 The EM Algorithm](https://github.com/reata/MachineLearning/blob/master/The%20EM%20Algorithm.ipynb)

    - [因子分析 Factor Analysis](https://github.com/reata/MachineLearning/blob/master/Factor%20Analysis.ipynb)

    - [主成分分析 Principal Components Analysis](https://github.com/reata/MachineLearning/blob/master/Principal%20Components%20Analysis.ipynb)

    - [独立成分分析 Independent Components Analysis](https://github.com/reata/MachineLearning/blob/master/Independent%20Components%20Analysis.ipynb)

4. 强化学习（Reinforcement Learning）

    - [强化学习和控制 Reinforcement Learning and Control](https://github.com/reata/MachineLearning/blob/master/Reinforcement%20Learning%20and%20Control.ipynb)

### 参考资料

1. [Andrew Ng在斯坦福大学工学院的机器学习公开课CS229](https://see.stanford.edu/Course/CS229)

2. [Scikit Learn官方文档](http://scikit-learn.org/stable/documentation.html)

3. [Udemy--Machine-Learning](https://github.com/jmportilla/Udemy---Machine-Learning)

4. [机器学习实战](https://book.douban.com/subject/24703171/)

5. [使用Matplotlib可视化优化算法](http://louistiao.me/posts/notebooks/visualizing-and-animating-optimization-algorithms-with-matplotlib/)