博客目录——Machine Learning算法学习

怼烎@ 2022-05-26 03:52 161阅读 0赞

*  更新日期：2018/07/24

### 方法技巧 ###

[【ML Method】熵、联合熵、条件熵、互信息、相对熵、交叉熵][ML Method]  
[【ML Trick】核技巧][ML Trick]  
[【ML Trick】决策树剪枝][ML Trick 1]  
[【ML Trick】数据预处理的常用方法][ML Trick 2]  
[【ML Trick】机器学习特征工程处理（一）][ML Trick 3]

### 异同 ###

[【异同】信息增益、Gini、信息增益率][Gini]  
[【异同】决策树ID3和C4.5的异同][ID3_C4.5]  
[【异同】LR和SVM的异同][LR_SVM]  
[【异同】梯度提升决策树和随机森林的异同][Link 1]

### 算法学习 ###

[【ML算法】监督学习——线性回归][ML]  
[【ML算法】监督学习——逻辑回归][ML 1]  
[【ML算法】监督学习——决策树ID3算法][ML_ID3]  
\[【ML算法】监督学习——决策树C4.5算法\]  
[【ML算法】监督学习——支持向量机][ML 2]  
[【ML算法】监督学习——KNN算法][ML_KNN]  
\[【ML算法】无监督学习——K-means聚类\]  
\[【ML算法】无监督学习——层次聚类\]  
\[【ML算法】无监督学习——DBCAN聚类\]  
[【ML算法】无监督学习——关联规则Apriori][ML_Apriori]  
\[【ML算法】集成学习——随机森林（RF）\]  
\[【ML算法】集成学习——GBDT\]  
\[【ML算法】集成学习——Adaboost\]  
\[【ML算法】集成学习——xgboost\]  
\[【ML算法】集成学习——lightGBM\]

### Machine Learning算法实现（基于scikit-learn、Tensorflow） ###

[【scikit-learn】机器学习（一）——一元回归模型][scikit-learn]  
\[【scikit-learn】机器学习（二）——非线性回归\]  
\[【scikit-learn】机器学习（三）——多元回归\]  
\[【scikit-learn】机器学习（四）——logistics回归\]  
\[【scikit-learn】机器学习（五）——决策树ID3算法\]  
\[【scikit-learn】机器学习（六）——决策树C4.5算法\]  
\[【scikit-learn】机器学习（七）——回归树CART算法\]  
[【scikit-learn】机器学习（八）——K-means聚类][scikit-learn_K-means]  
\[【scikit-learn】机器学习（九）——层次聚类\]  
\[【scikit-learn】机器学习（十）——密度聚类DBSCAN\]  
\[【scikit-learn】机器学习（十一）——Apriori关联规则\]  
\[【scikit-learn】机器学习（十二）——SVM\]  
\[【scikit-learn】机器学习（十三）——时间序列模型\]  
\[【scikit-learn】机器学习（十四）——主成分分析\]  
\[【scikit-learn】机器学习（十五）——朴素贝叶斯\]  
\[【scikit-learn】机器学习（十六）——KNN算法\]  
\[【scikit-learn】机器学习（十七）——梯度提升决策树（GBDT）\]  
\[【scikit-learn】机器学习（十八）——随机森林\]  
\[【scikit-learn】机器学习（十九）——Adaboost算法\]  
\[【scikit-learn】机器学习（二十）——BP神经网络\]  
[【scikit-learn】机器学习（二十一）——图片聚类：k-means算法的python实现][scikit-learn_k-means_python]

[ML Method]: https://blog.csdn.net/roguesir/article/details/80947490
[ML Trick]: https://blog.csdn.net/roguesir/article/details/78803959
[ML Trick 1]: https://blog.csdn.net/roguesir/article/details/76690931
[ML Trick 2]: https://blog.csdn.net/roguesir/article/details/78639809
[ML Trick 3]: http://blog.csdn.net/roguesir/article/details/79183431
[Gini]: https://blog.csdn.net/roguesir/article/details/76619919
[ID3_C4.5]: https://blog.csdn.net/roguesir/article/details/76474709
[LR_SVM]: https://blog.csdn.net/roguesir/article/details/76174879
[Link 1]: https://blog.csdn.net/roguesir/article/details/76173879
[ML]: https://blog.csdn.net/roguesir/article/details/78637391
[ML 1]: https://blog.csdn.net/roguesir/article/details/79616328
[ML_ID3]: https://blog.csdn.net/roguesir/article/details/76628024
[ML 2]: https://blog.csdn.net/roguesir/article/details/78659645
[ML_KNN]: http://blog.csdn.net/roguesir/article/details/78580412
[ML_Apriori]: https://blog.csdn.net/roguesir/article/details/78742843
[scikit-learn]: https://blog.csdn.net/roguesir/article/details/77884128
[scikit-learn_K-means]: https://blog.csdn.net/roguesir/article/details/78273586
[scikit-learn_k-means_python]: http://blog.csdn.net/roguesir/article/details/74283268