热门标签 | HotTags
当前位置:  开发笔记 > 编程语言 > 正文

Python教程网盘下载

教程网盘下载地址:https://u18103887.ctfile.com/fs/18103887-335537484教程目录:第1章Python机器学习的生态系统

教程网盘下载地址:https://u18103887.ctfile.com/fs/18103887-335537484
教程目录:
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
2.2 检查和准备数据 ·····························································38
2.2.1 分析数据 ·····································································46
2.2.2 可视化数据 ··································································50
2.3 对数据建模 ·····································································51
2.3.1 预测 ················································································54
2.3.2 扩展模型 ·········································································57
2.4 小结 ··················································································57
第 3 章构建应用程序,发现低价的机票 ··································58
3.1 获取机票价格数据 ·························································59
3.2 使用高级的网络爬虫技术检索票价数据 ·····························60
3.3 解析DOM 以提取定价数据 ··············································62
通过聚类技术识别异常的票价 ··············································66
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
5.2 使用 embed.ly API 下载故事的内容 ····································119
5.3 自然语言处理基础 ····························································120
5.4 支持向量机··········································································123
5.5 IFTTT 与文章源、Google 表单和电子邮件的集成 ·······················125
通过 IFTTT 设置新闻源和Google 表单 ········································125
5.6 设置你的每日个性化新闻简报 ··············································133
5.7 小结 ················································································137
第 6 章预测你的内容是否会广为流传 ··········································138
6.1 关于病毒性,研究告诉我们了些什么 ······································139
6.2 获取分享的数量和内容 ·························································140
6.3 探索传播性的特征 ································································149
6.3.1 探索图像数据 ····································································149
6.3.2 探索标题 ··········································································152
6.3.3 探索故事的内容 ································································156
6.4 构建内容评分的预测模型 ··················································157
6.5 小结 ·················································································162
第 7 章使用机器学习预测股票市场 ···············································163
7.1 市场分析的类型 ··································································164
7.2 关于股票市场,研究告诉我们些什么 ········································165
7.3 如何开发一个交易策略 ·························································166
7.3.1 延长我们的分析周期 ····························································172
7.3.2 使用支持向量回归,构建我们的模型 ······································175
7.3.3 建模与动态时间扭曲································································182
7.4 小结 ·······················································································186
第 8 章建立图像相似度的引擎 ··························································187
8.1 图像的机器学习 ············································································188
8.2 处理图像 ·····················································································189
8.3 查找相似的图像 ···································································191
8.4 了解深度学习 ········································································195
8.5 构建图像相似度的引擎 ···························································198
8.6 小结 ···························································································206
第 9 章打造聊天机器人 ······································································207
9.1 图灵测试 ·····················································································207
9.2 聊天机器人的历史 ············································································208
9.3 聊天机器人的设计 ·········································································212
9.4 打造一个聊天机器人 ······································································217
9.5 小结 ·······························································································227
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

Python 教程 网盘下载 - 文章图片

 


推荐阅读
  • 生成式对抗网络模型综述摘要生成式对抗网络模型(GAN)是基于深度学习的一种强大的生成模型,可以应用于计算机视觉、自然语言处理、半监督学习等重要领域。生成式对抗网络 ... [详细]
  • 使用Ubuntu中的Python获取浏览器历史记录原文: ... [详细]
  • 本文由编程笔记#小编为大家整理,主要介绍了logistic回归(线性和非线性)相关的知识,包括线性logistic回归的代码和数据集的分布情况。希望对你有一定的参考价值。 ... [详细]
  • 开发笔记:加密&json&StringIO模块&BytesIO模块
    篇首语:本文由编程笔记#小编为大家整理,主要介绍了加密&json&StringIO模块&BytesIO模块相关的知识,希望对你有一定的参考价值。一、加密加密 ... [详细]
  • javascript  – 概述在Firefox上无法正常工作
    我试图提出一些自定义大纲,以达到一些Web可访问性建议.但我不能用Firefox制作.这就是它在Chrome上的外观:而那个图标实际上是一个锚点.在Firefox上,它只概述了整个 ... [详细]
  • Voicewo在线语音识别转换jQuery插件的特点和示例
    本文介绍了一款名为Voicewo的在线语音识别转换jQuery插件,该插件具有快速、架构、风格、扩展和兼容等特点,适合在互联网应用中使用。同时还提供了一个快速示例供开发人员参考。 ... [详细]
  • Java学习笔记之面向对象编程(OOP)
    本文介绍了Java学习笔记中的面向对象编程(OOP)内容,包括OOP的三大特性(封装、继承、多态)和五大原则(单一职责原则、开放封闭原则、里式替换原则、依赖倒置原则)。通过学习OOP,可以提高代码复用性、拓展性和安全性。 ... [详细]
  • 本文介绍了机器学习手册中关于日期和时区操作的重要性以及其在实际应用中的作用。文章以一个故事为背景,描述了学童们面对老先生的教导时的反应,以及上官如在这个过程中的表现。同时,文章也提到了顾慎为对上官如的恨意以及他们之间的矛盾源于早年的结局。最后,文章强调了日期和时区操作在机器学习中的重要性,并指出了其在实际应用中的作用和意义。 ... [详细]
  • 本文介绍了绕过WAF的XSS检测机制的方法,包括确定payload结构、测试和混淆。同时提出了一种构建XSS payload的方法,该payload与安全机制使用的正则表达式不匹配。通过清理用户输入、转义输出、使用文档对象模型(DOM)接收器和源、实施适当的跨域资源共享(CORS)策略和其他安全策略,可以有效阻止XSS漏洞。但是,WAF或自定义过滤器仍然被广泛使用来增加安全性。本文的方法可以绕过这种安全机制,构建与正则表达式不匹配的XSS payload。 ... [详细]
  • 背景应用安全领域,各类攻击长久以来都危害着互联网上的应用,在web应用安全风险中,各类注入、跨站等攻击仍然占据着较前的位置。WAF(Web应用防火墙)正是为防御和阻断这类攻击而存在 ... [详细]
  • 本文介绍了贝叶斯垃圾邮件分类的机器学习代码,代码来源于https://www.cnblogs.com/huangyc/p/10327209.html,并对代码进行了简介。朴素贝叶斯分类器训练函数包括求p(Ci)和基于词汇表的p(w|Ci)。 ... [详细]
  • 在本教程中,我们将看到如何使用FLASK制作第一个用于机器学习模型的RESTAPI。我们将从创建机器学习模型开始。然后,我们将看到使用Flask创建AP ... [详细]
  • 提升Python编程效率的十点建议
    本文介绍了提升Python编程效率的十点建议,包括不使用分号、选择合适的代码编辑器、遵循Python代码规范等。这些建议可以帮助开发者节省时间,提高编程效率。同时,还提供了相关参考链接供读者深入学习。 ... [详细]
  • CSS3选择器的使用方法详解,提高Web开发效率和精准度
    本文详细介绍了CSS3新增的选择器方法,包括属性选择器的使用。通过CSS3选择器,可以提高Web开发的效率和精准度,使得查找元素更加方便和快捷。同时,本文还对属性选择器的各种用法进行了详细解释,并给出了相应的代码示例。通过学习本文,读者可以更好地掌握CSS3选择器的使用方法,提升自己的Web开发能力。 ... [详细]
  • 本文介绍了Java工具类库Hutool,该工具包封装了对文件、流、加密解密、转码、正则、线程、XML等JDK方法的封装,并提供了各种Util工具类。同时,还介绍了Hutool的组件,包括动态代理、布隆过滤、缓存、定时任务等功能。该工具包可以简化Java代码,提高开发效率。 ... [详细]
author-avatar
幸福的xinwangnanfei_736
这个家伙很懒,什么也没留下!
PHP1.CN | 中国最专业的PHP中文社区 | DevBox开发工具箱 | json解析格式化 |PHP资讯 | PHP教程 | 数据库技术 | 服务器技术 | 前端开发技术 | PHP框架 | 开发工具 | 在线工具
Copyright © 1998 - 2020 PHP1.CN. All Rights Reserved | 京公网安备 11010802041100号 | 京ICP备19059560号-4 | PHP1.CN 第一PHP社区 版权所有