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