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Machine_Learning_Sklearn_Examples

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机器学习Sklearn入门指南。Machine Learning Sklearn API and Examples with Python3 and Jupyter Notebook.

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# 机器学习Sklearn入门指南
机器学习入门指南，基于SKlearn讲解如何学习《机器学习》，更新中。

## 注意
如果通过Github站内超链接打开Jupyter Notebook文件发生错误，可以点击根据 https://nbviewer.org 生成的“备用链接”间接访问对应文件。  
或者通过以下链接访问整个项目的站外备用链接，注意点击站外备用链接里的非Jupyter Notebook格式文件会跳转回到Github仓库内：  
●  [**Machine_Learning_Sklearn_Examples**](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/tree/master/)

## 机器学习Python基础  
●  [**Numpy科学计算**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/00_Python_basics/00_01_Numpy_basic.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/00_Python_basics/00_01_Numpy_basic.ipynb)]    
●  [**Pandas数据分析**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/00_Python_basics/00_02_Pandas_basic.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/00_Python_basics/00_02_Pandas_basic.ipynb)]      
●  [**Matplotlib可视化**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/00_Python_basics/00_03_Matplotlib_basic.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/00_Python_basics/00_03_Matplotlib_basic.ipynb)]       
●  [**数据探索EDA**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/00_Python_basics/00_04_EDA.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/00_Python_basics/00_04_EDA.ipynb)]

## 监督学习
●  [**线性回归**](https://github.com/solidglue/Machine_Learning_Sklearn_Jupyter_Demo/blob/master/01_Supervised_learning/01_01_Linear_regression.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/01_Supervised_learning/01_01_Linear_regression.ipynb)]      
●  [**逻辑回归**](https://github.com/solidglue/Machine_Learning_Sklearn_Jupyter_Demo/blob/master/01_Supervised_learning/01_02_Logistic_regression.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/01_Supervised_learning/01_02_Logistic_regression.ipynb)]      
●  [**支持向量机SVM**](https://github.com/solidglue/Machine_Learning_Sklearn_Jupyter_Demo/blob/master/01_Supervised_learning/01_03_Svm.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/01_Supervised_learning/01_03_Svm.ipynb)]       
●  随机梯度下降SGD  
●  [**K近邻（KNN）**](https://github.com/solidglue/Machine_Learning_Sklearn_Jupyter_Demo/blob/master/01_Supervised_learning/01_05_Knn.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/01_Supervised_learning/01_05_Knn.ipynb)]      
●  朴素贝叶斯  
●  [**决策树**](https://github.com/solidglue/Machine_Learning_Sklearn_Jupyter_Demo/blob/master/01_Supervised_learning/01_07_Decision_trees.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/01_Supervised_learning/01_07_Decision_trees.ipynb)]      
●  [**随机森林**](https://github.com/solidglue/Machine_Learning_Sklearn_Jupyter_Demo/blob/master/01_Supervised_learning/01_08_Random_forests.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/01_Supervised_learning/01_08_Random_forests.ipynb)]      
●  多分类与多输出  
●  特征选择  
●  神经网络

## 无监督学习
●  [**Kmeans聚类**](https://github.com/solidglue/Machine_Learning_Sklearn_Jupyter_Demo/blob/master/02_Unsupervised_learning/02_01_Kmeans_clustering.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/02_Unsupervised_learning/02_01_Kmeans_clustering.ipynb)]        
●  神经网络模型（无监督）

## 模型选择与评估
●  [**交叉验证**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/03_Model_selection_and_evaluation/03_01_Cross_validation.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/03_Model_selection_and_evaluation/03_01_Cross_validation.ipynb)]      
●  [**调整估算器超参数**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/03_Model_selection_and_evaluation/03_02_hyper_parameters_estimator.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/03_Model_selection_and_evaluation/03_02_hyper_parameters_estimator.ipynb)]      
●  [**指标和评分**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/03_Model_selection_and_evaluation/03_03_Metrics_and_scoring.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/03_Model_selection_and_evaluation/03_03_Metrics_and_scoring.ipynb)]      
●  [**验证曲线：绘制分数以评估模型**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/03_Model_selection_and_evaluation/03_04_Validation_curves.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/03_Model_selection_and_evaluation/03_04_Validation_curves.ipynb)]

## 可视化
●  可视化

## 数据集转换
●  [**流水线和复合估算器**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/05_Dataset_transformations/05_01_Pipelines_and_composite_estimators.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/05_Dataset_transformations/05_01_Pipelines_and_composite_estimators.ipynb)]      
●  [**特征提取**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/05_Dataset_transformations/05_02_Feature_extraction.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/05_Dataset_transformations/05_02_Feature_extraction.ipynb)]      
●  [**数据预处理**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/05_Dataset_transformations/05_03_Preprocessing_data.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/05_Dataset_transformations/05_03_Preprocessing_data.ipynb)]      
●  [**缺失值补充**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/05_Dataset_transformations/05_04_Imputation_of_missing_values.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/05_Dataset_transformations/05_04_Imputation_of_missing_values.ipynb)]      
●  无监督降维  
●  随机投影

## 数据集加载
●  [**玩具数据集**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/06_Dataset_loading/06_01_Toy_datasets.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/06_Dataset_loading/06_01_Toy_datasets.ipynb)]      
●  [**真实世界数据集**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/06_Dataset_loading/06_02_Real_world_datasets.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/06_Dataset_loading/06_02_Real_world_datasets.ipynb)]      
●  [**生成的数据集**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/06_Dataset_loading/06_03_Generated%20datasets.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/06_Dataset_loading/06_03_Generated%20datasets.ipynb)]      
●  [**加载其他数据集**](https://github.com/solidglue/Machine_Learning_Sklearn_Examples/blob/master/06_Dataset_loading/06_04_load_files.ipynb)       [~~*(备用链接)*~~](https://nbviewer.org/github/solidglue/Machine_Learning_Sklearn_Examples/blob/master/06_Dataset_loading/06_04_load_files.ipynb)]

## 模型持久化
●  模型持久化

## *扩展

1.**推荐系统**  
王树森推荐系统公开课 - 基于小红书的场景讲解工业界真实的推荐系统。  
●  [**Recommender_System**](https://github.com/solidglue/Recommender_System)

2.**YouTuBe推荐系统排序模型**  
以"DNN_for_YouTube_Recommendations"模型和电影评分数据集（ml-1m）为基础，详尽的展示了如何基于TensorFlow2实现推荐系统排序模型。  
●  [**YouTube深度排序模型(多值embedding、多目标学习)**](https://github.com/solidglue/DNN_for_YouTube_Recommendations)

3.**推荐系统推理服务**  
基于Goalng、Docker和微服务思想实现了高并发、高性能和高可用的推荐系统推理微服务，包括多种召回/排序服务，并提供多种接口访问方式（REST、gRPC和Dubbo）等，每日可处理上千万次推理请求。   
● [**推荐系统推理微服务Golang**](https://github.com/solidglue/Recommender_System_Inference_Services)

4.**深度学习TensorFlow入门教程**  
●  [**深度学习TensorFlow入门教程**](https://github.com/solidglue/Deep_Learning_TensorFlow2_Examples)

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