《机器学习全解》阅读札记目录一、前言....................................................3二、机器学习基础............................................32.1机器学习定义.........................................52.2机器学习分类.........................................62.2.1监督学习.........................................72.2.2无监督学习.......................................82.2.3强化学习.........................................92.3机器学习算法........................................11三、监督学习...............................................143.1线性回归............................................163.1.1理解线性回归....................................173.1.2线性回归算法实现................................183.1.3线性回归优缺点..................................193.2逻辑回归............................................203.2.1理解逻辑回归....................................213.2.2逻辑回归算法实现................................233.2.3逻辑回归优缺点..................................243.3支持向量机..........................................253.3.1理解支持向量机..................................263.3.2支持向量机算法实现..............................273.3.3支持向量机优缺点................................29四、无监督学习.............................................304.1聚类分析............................................314.1.1了解聚类分析....................................324.1.2常见聚类算法....................................344.1.3聚类分析优缺点..................................354.2降维技术............................................374.2.1主成分分析......................................384.2.2线性判别分析....................................394.2.3奇异值分解......................................40五、强化学习...............................................41六、机器学习工具和库.......................................42七、机器学习实践...........................................447.1数据预处理..........................................457.2模型评估与选择......................................477.3特征工程............................................49八、机器学习应用案例.......................................51九、总结与展望.............................................52一、前言随着科技的飞速发展，