《Python机器学习实践指南 》这可能是我见过最实用的书了，附PDF版分享！

全书共有10 章。第1 章讲解了Python 机器学习的生态系统，剩余9 章介绍了众多与机器学习相关的算法，包括各类分类算法、数据可视化技术、推荐引擎等，主要包括机器学习在公寓、机票、IPO 市场、新闻源、内容推广、股票市场、图像、聊天机器人和推荐引擎等方面的应用。
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本书适合Python 程序员、数据分析人员、对算法感兴趣的读者、机器学习领域的从业人员及科研人员阅读。

Python机器学习实践指南 适合的读者包括了解数据科学的Python程序员、数据科学家、架构师，以及想要构建完整的、基于Python的机器学习系统的人们。

通过阅读Python机器学习实践指南 ，你将能：

·了解Python机器学习的生态系统；

·了解如何执行线性回归；

·机器视觉概念的介绍；

·高级数据可视化技术；

·如何使用第三方API，部署机器学习模型；

·时间序列的建模技术；

·如何构建无监督模型。

目录

第1 章Python 机器学习的生态系统······1

1.1 数据科学/机器学习的工作

流程 ··································2

1.1.1 获取··························2

1.1.2 检查和探索·················2

1.1.3 清理和准备·················3

1.1.4 建模··························3

1.1.5 评估··························3

1.1.6 部署··························3

1.2 Python 库和功能···················3

1.2.1 获取··························4

1.2.2 检查··························4

1.2.3 准备························20

1.2.4 建模和评估···············26

1.2.5 部署························34

1.3 设置机器学习的环境···········34

1.4 小结·································34

第2 章构建应用程序，发现低价的

公寓·············································35

2.1 获取公寓房源数据··············36

使用import.io 抓取房源

数据 ·································36

3.4 使用IFTTT 发送实时提醒······75

3.5 整合在一起························78

3.6 小结·································82

第4 章使用逻辑回归预测IPO 市场·······83

4.1 IPO 市场····························84

4.1.1 什么是IPO ················84

4.1.2 近期IPO 市场表现·······84

4.1.3 基本的IPO 策略··········93

4.2 特征工程···························94

4.3 二元分类··························103

4.4 特征的重要性···················108

4.5 小结································111

第5 章创建自定义的新闻源··············112

5.1 使用Pocket 应用程序，创建一个

监督训练的集合················112

5.1.1 安装Pocket 的Chrome

扩展程序·················113

5.1.2 使用Pocket API 来检索

故事·······················114

·····································································

第10 章构建推荐引擎·····················228

10.1 协同过滤························229

10.1.1 基于用户的过滤······230

10.1.2 基于项目的过滤······233

10.2 基于内容的过滤···············236

10.3 混合系统························237

10.4 构建推荐引擎··················238

10.5 小结······························251

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