李航《统计学习方法》书的每一章节核心提炼以及Python代码实现，可以直接运行在Anaconda的Jupyter里面，从即日起抽时间不断更新.
This is a set of tutorials and implementations about Li Hang's Book "Statistical-learning-method". You can download the ipynb type files and run direactly on the Anaconda Python environment like Jupyter-lab or Jupyter-notebook. This tutorials will be continuously updated.
教程和代码实现按照如下列表安排，基本按照《统计学习方法》
The tutorials and implements are sorted and named according to the original book Statistical-learning-method
| Chapter | English Name | Chinese Name | Link | Completion |
|---|---|---|---|---|
| Chapter1 | Summary | 摘要 | Open | 100% |
| Chapter2 | Perceptron | 感知机算法 | Open | 100% |
| Chapter3 | KNN | K近临算法 | Open | 100% |
| Chapter4 | Naive Bayes | 朴素贝叶斯算法 | Open | 100% |
| Chapter5 | Decision Tree | 决策树算法 | Open | 100% |
| Chapter6 | Logical regression and maximum entropy model | 逻辑回归和最大熵模型 | Open | 50% |
| Chapter7 | Support vector machine | 支持向量机算法 | Open | 50% |
| Chapter8 | Boosting | 提升算法 | ||
| Chapter9 | EM | 期望最大化算法 | ||
| Chapter10 | HMM | 隐马尔可夫算法 | ||
| Chapter11 | CRF | 条件随机场 |
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