作者：齐伟
页数：198页
出版社：电子工业出版社
出版日期：2020
ISBN：9787121382635
电子书格式：pdf/epub/txt
内容简介
本书详细地介绍了大数据、人工智能等项目中不可或缺的环节和内容：数据准备和特征工程。书中的每节首先以简明方式介绍了基本知识；然后通过实际案例演示了基本知识的实际应用，并提供了针对性练习项目，将“知识、案例、练习”融为一体；最后以“扩展探究”方式引导读者进入更深广的领域。本书既适合作为大学相关专业的教材，也适合作为大数据、人工智能等领域的开发人员的参考读物。
作者简介
齐伟，自称老齐，现居苏州，所著在线教程《零基础学Python》及《零基础学Python(第2版)》在业内引起非常大的反响。愿意和来自各方的朋友讨论技术问题，并能提供相关技术服务。
本书特色
画龙点睛的批注，让学习更加简单案例式教学，面向工程实践渗透技术发展，基础与前沿结合提供在线实验平台，学练融合在专属公众号与作者交互，教学相长配套视频和在线课程，边看边学
目录
第1 章 感知数据 ·································.001
1.0 了解数据科学项目 ································001
1.1 文件中的数据 ··································003
1.1.1 CSV文件 ····································003
1.1.2 Excel文件 ···································009
1.1.3 图像文件 ···································015
1.2 数据库中的数据 ·································019
1.3 网页上的数据 ··································029
1.4 来自API 的数据 ·································039
第2 章 数据清理 ··································044
2.0 基本概念 ····································045
2.1 转化数据类型 ··································046
2.2 处理重复数据 ··································054
2.3 处理缺失数据 ··································057
2.3.1 检查缺失数据 ·································058
2.3.2 用指定值填补 ·································063
2.3.3 根据规律填补 ·································069
2.4 处理离群数据 ··································076
第3 章 特征变换 ···································083
3.0 特征的类型 ···································084
3.1 特征数值化 ···································085
3.2 特征二值化 ···································088
3.3 OneHot编码 ···································093
3.4 数据变换 ····································098
3.5 特征离散化 ···································104
3.5.1 无监督离散化 ·································104
3.5.2 有监督离散化 ·································110
3.6 数据规范化 ···································113
第4 章 特征选择 ···································124
4.0 特征选择简述 ··································124
4.1 封装器法 ····································127
4.1.1 循序特征选择 ·································127
4.1.2 穷举特征选择 ·································135
4.1.3 递归特征消除 ·································140
4.2 过滤器法 ····································144
4.3 嵌入法 ·····································149
第5 章 特征抽取 ···································154
5.1 无监督特征抽取··································154
5.1.1 主成分分析 ··································154
5.1.2 因子分析 ···································161
5.2 有监督特征抽取 ·································167
附录A Jupyter简介 ·································173
附录B NumPy简介 ··································176
附录C Pandas简介 ··································185
附录D Matplotlib简介 ································194
后记 ········································199
