PYTHON机器学习与实践——从无到有的KAGGLE竞赛之路结构如下：第一章介绍...............................................................................11.1机器学习概述.....................11.1。1任务....................................................31.1.2体验......................................51.1.3性能....................................................................51.2Python编程库...........................................................81.2.1为什么要使用Python..........................................81.2.2Python机器学习的优点.........................................................................91.2.3NumPy和SciPy........................................................101.2.4Matplotlib.................................................................1.2.5Scikit-learn.........................................111.2.6熊猫.....................................................................111.2.7蟒蛇.....................................................121.3Python环境配置...............................................121.3.1Windows系统环境...........................................................121.3.2MacOS系统环境.....................................................................................171.4Python编程基础.......................................181.4.1基本Python语法................................................191.4.2Python数据类型.......................................................201.4.3Python数据操作.......................................................................221.4.4Python流控......................................261.4.5Python函数（模块）设计......................................................281.4.6导入Python编程库（包）.......................................................291.4.7Python基础综合实践...........................................................................301.5章末小结...............................................................................33第2章基础.......................................................342.1监督学习的经典模型....................................................342.1.1分类学习...............................................352.1.1.1线性分类器2.1.1.2支持向量机（分类）2.1.1.3朴素贝叶斯2.1.1.4K最近邻（分类）2.1.1.5决策树2.1.1.6集成模型（分类）2.1.2回归预测......................................................642.1.2.1线性回归器2.1.2.2支持向量机（回归）2.1.2.3K最近邻（回归）2.1.2.4回归树2.1.2.5集成模型（回归）2.2无监督学习经典模型......................812.2.1数据聚类..........................................................................................812.2.1.1K-means算法2.2.2特征降维............................................................................................912.2.2.1主成分分析2.3小结本章结束.................................................................97第3章进阶..................................................983.1实战技巧建模......................................................................983.1.1特征提升.....................993.1.2模型正则化。...................................1113.1.3模型检查................................................................................1213.1.4超参数搜索。.......................................1223.2流行图书馆/模型实践...........................1293.2.1自然语言处理包（NLTK）.....................1313.2.2词向量（Word2Vec）技术。...............1333.2.3XGBoost模型.....................1383.2.4Tensorflow框架............................1403.3章末小结......................................152第四章实战......................................1534.1Kaggle平台介绍.........................................1534.2泰坦尼克号乘客预测...............................................1574.3IMDB影评评分预估......1654.4MNIST手写数字图像识别......1744.5总结在本章的结尾.......................180结语...............................................181
