building machine learning system with Python 学习笔记--从零开始机器学习（2）第一章

最新推荐文章于 2024-05-12 09:46:35 发布
原创 最新推荐文章于 2024-05-12 09:46:35 发布 · 746 阅读 · 0 ·
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#python #数据分析 #机器学习

本文介绍了Python机器学习的基本流程，强调数据预处理的重要性，特别是特征工程。推荐了相关资源，如问答网站和博客，并详细讲解了numpy的基础操作，包括数组创建、索引访问、数据修剪以及与Python列表的性能对比。同时提到了scipy在机器学习中的作用，展示了scipy与numpy的密切关系及一些常用方法。

Python机器学习入门

ps：想了解机器学习发展历史、使命、面临的问题这些的可以看百度BOSS李彦宏新书《智能革命》，挺通俗易懂的。


机器学习的目标就是通过若干示例让机器学会完成人物，例如电子邮件分类。

工作流程绝大部分的时间花在：

1）读取和清洗数据

2）探索和理解输入数据

3）分析如何最好地将数据呈现给学习算法

4）选择正确的模型和学习算法

5）正确地评估性能

通常在训练前要对部分数据进行提炼，一个简单算法在提炼后数据上的表现，甚至能够超过一个非常复杂的算法在原始数据上的结果。提炼数据的流程叫做特征工程--feature engineering。

可以看到算法只是五个环节中的一个，1）、2）、3）都和数据有关。

推荐的问答网站：http://metaoptimize.com/qa 专注机器学习主题

http://stats.stackexchange.com 专注统计问题

http://stackoverflow.com 这个不用多说，百度过编程问题的同学应该都见过

Freenode的#machinelearning频道 互联网中转聊天频道，专

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