Machine Learning in Action --- Peter Harrington

Part 1 分类

Chapter1 　机器学习基础

https://blog.csdn.net/JachinMa/article/details/88703866

Chapter2 　 k-近邻算法

https://blog.csdn.net/JachinMa/article/details/88770841

Chapter3 　决策树

https://blog.csdn.net/JachinMa/article/details/88809544

Chapter4 　基于概率论的分类方法：朴素贝叶斯

https://blog.csdn.net/JachinMa/article/details/88832864

Chapter5　 Logistic回归

https://blog.csdn.net/JachinMa/article/details/88858049

Chapter6　 支持向量机

https://blog.csdn.net/JachinMa/article/details/88882463 原理

https://blog.csdn.net/JachinMa/article/details/88903527 simpleSMo

https://blog.csdn.net/JachinMa/article/details/88919987 优化smo

https://blog.csdn.net/JachinMa/article/details/88960054 核函数及手写测试

Chapter7　利用AdaBoost元算法提高分类性能

https://blog.csdn.net/JachinMa/article/details/88985870

Part 2 利用回归预测数值型数据

Chapter8　预测数值型数据：回归

https://blog.csdn.net/JachinMa/article/details/89036845 原理

https://blog.csdn.net/JachinMa/article/details/89198011 爬虫实践

Chapter9　树回归

https://blog.csdn.net/JachinMa/article/details/89353584

Part3 非监督分类

Chapter10 利用K-均值聚类算法对未标注数据分组

https://blog.csdn.net/JachinMa/article/details/89391000 自写简单k-均值代码

https://blog.csdn.net/JachinMa/article/details/89409184 书上代码

https://blog.csdn.net/JachinMa/article/details/89425808 二分k-均值

https://blog.csdn.net/JachinMa/article/details/89497933 利用高德API对城市进行聚类

Chapter11 使用Apriori算法进行关联分析

https://blog.csdn.net/JachinMa/article/details/89528519

Chapter12 使用FP-growth算法来高效发现频繁项集

https://blog.csdn.net/JachinMa/article/details/89812766 原理及构建FP树

https://blog.csdn.net/JachinMa/article/details/89852582 利用FP树挖掘频繁项集

Part 4 其它工具

Chapter13 利用PCA来简化数据

Chapter14 利用SVD简化数据

Chapter15 大数据与MapReduce