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<div id='write' class = 'is-node'><p><strong>斯坦福大学2014机器学习教程中文笔记</strong></p><p>课程目录：<a href='index.html'>目录</a></p><p>课程地址：<a href='https://www.coursera.org/course/ml' target='_blank' >https://www.coursera.org/course/ml</a></p><p>Machine Learning(机器学习)是研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。它是人工智能的核心，是使计算机具有智能的根本途径，其应用遍及人工智能的各个领域，它主要使用归纳、综合而不是演译。在过去的十年中，机器学习帮助我们自动驾驶汽车，有效的语音识别，有效的网络搜索，并极大地提高了人类基因组的认识。机器学习是当今非常普遍，你可能会使用这一天几十倍而不自知。很多研究者也认为这是最好的人工智能的取得方式。在本课中，您将学习最有效的机器学习技术，并获得实践，让它们为自己的工作。更重要的是，你会不仅得到理论基础的学习，而且获得那些需要快速和强大的应用技术解决问题的实用技术。最后，你会学到一些硅谷利用机器学习和人工智能的最佳实践创新。</p><p>本课程提供了一个广泛的介绍机器学习、数据挖掘、统计模式识别的课程。主题包括：（一）监督学习（参数/非参数算法，支持向量机，核函数，神经网络）。（二）无监督学习（聚类，降维，推荐系统，深入学习推荐）。（三）在机器学习的最佳实践（偏差/方差理论；在机器学习和人工智能创新过程）。本课程还将使用大量的案例研究，您还将学习如何运用学习算法构建智能机器人（感知，控制），文本的理解（Web搜索，反垃圾邮件），计算机视觉，医疗信息，音频，数据挖掘，和其他领域。</p><p>本课程需要10周共18节课，相对以前的机器学习视频，这个视频更加清晰，而且每课都有ppt课件，推荐学习。</p><p>本人2014年下半年开始翻译本课程字幕，并写了课程的中文笔记。笔记被下载了几万次，应该帮助了不少人，也有很多人一直在帮助我，现在我把笔记的word原稿和markdown原稿分享给大家。</p><p>markdown的笔记和课程中英文字幕我将放在github和码云，希望大家能继续完善。为方便数学公式的在线显示，在线观看的是html文件，公式已经被转为图片，公式源码在markdown文件。</p><p><strong>最后想对各位朋友说：</strong>
<strong>赠人玫瑰，手有余香！</strong>
<strong>在人工智能的道路上，你不是一个人在战斗！</strong></p><p>黄海广</p><p>2017-11-3 夜</p><p><a href='https://www.zhihu.com/people/fengdu78/activities'>我的知乎</a></p><hr /><p>文件夹说明：</p><p>docx：笔记的word版本</p><p>media：笔记的图片</p><p>ppt：课程的原版课件</p><p>srt：课程的中英文字幕（mp4文件需要在百度云下载）</p><p>code：课程的python代码（有一部分是国外大牛写的）</p><p>机器学习视频下载链接：<a href='http://pan.baidu.com/s/1dFCQvxZ' target='_blank' >http://pan.baidu.com/s/1dFCQvxZ</a> 密码：dce8</p><p>包含mp4视频和字幕</p><p><a href='http://www.ai-start.com/ml2014'>笔记在线阅读</a></p><p><a href='http://fengdu78.gitee.io/coursera-ml-andrewng-notes/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%AA%E4%BA%BA%E7%AC%94%E8%AE%B0%E5%AE%8C%E6%95%B4%E7%89%88v5.pdf'>笔记pdf版本下载</a></p><p>机器学习qq群：554839127</p><p></p></div>
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