January 2018
Intermediate to advanced
301 pages
8h 54m
Chinese
Content preview from Python机器学习基础教程
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x
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前言
位。然而，能够完全涵盖在实践中实现机器学习算法的所有重要内容，而又不需要先修高
等数学课程，这样的学习资源少之又少。对于那些想要使用机器学习算法而又不想花费大
量时间研读微积分、线性代数和概率论的人来说，我们希望本书能够有所帮助。
本书概览
本书的结构大致如下。
•
第
1
章介绍机器学习的基本概念及其应用，并给出本书会用到的基本设置。
•
第
2
章和第
3
章介绍实践中最常用的机器学习算法，并讨论这些算法的优缺点。
•
第
4
章介绍在机器学习中待处理数据的呈现方式的重要性，以及应重点关注数据的哪些
方面。
•
第
5
章介绍模型评估和调参的高级方法，重点讲解交叉验证和网格搜索。
•
第
6
章解释管道的概念。管道用于串联多个模型并封装工作流。
•
第
7
章介绍如何将前面各章讲述的方法应用到文本数据上，还介绍了一些文本特有的处
理方法。
•
第
8
章对全书进行总结，还介绍了有关更高级主题的参考资料。
虽然第
2
章和第
3
章给出了实际算法，但对于初学者来说，并不需要理解所有这些算法。
如果你想要尽快构建一个机器学习系统，我们建议你首先阅读第
1
章和第
2
章的开始部
分，里面介绍了所有的核心概念。然后你可以翻到
2.5
节，里面提到了我们介绍的所有监
督学习模型。从中选择最适合你需求的模型，然后翻回到对应小节阅读其详细内容。之后
你可以使用第
5
章中的方法对你的模型进行评估和调参。
在线资源
在学习本书时，一定要参考
scikit-learn
官方网站（
http://scikit-learn.org
），查阅关于类和
函数的更详细的文档，以及很多示例。此外 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.