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<h1 id="coursera-machine-learning">Coursera: Machine Learning</h1>
<h2 id="_1">课程简介</h2>
<ul>
<li>所属大学：Stanford</li>
<li>先修要求：AI 入门 + 熟练使用 Python</li>
<li>编程语言：Python</li>
<li>课程难度：🌟🌟🌟</li>
<li>预计学时：100 小时</li>
</ul>
<p>说起吴恩达，在 AI 届应该无人不晓。他是著名在线教育平台 <a href="https://www.coursera.org">Coursera</a> 的创始人之一，同时也是 Stanford 的网红教授。这门机器学习入门课应该算得上是他的成名作之一（另一个是深度学习课程），在 Coursera 上拥有数十万的学习者（注意这是花钱买了证书的人，一个证书几百刀），白嫖学习者数量应该是另一个数量级了。</p>
<p>这门课对新手极其友好，吴恩达拥有把机器学习讲成 1+1=2 一样直白的能力。你将会学习到线性回归、逻辑回归、支持向量机、无监督学习、降维、异常检测和推荐系统等等知识，并且在编程实践中夯实自己的理解。作业质量自然不必多言，保姆级代码框架，作业背景也多取自生活，让人学以致用。</p>
<p>当然，这门课作为一个公开慕课，难度上刻意放低了些，很多数学推导大多一带而过，如果你有志于从事机器学习理论研究，想要深究这些算法背后的数学理论，可以参考 <a href="../CS229/">CS229</a> 和 <a href="../CS189/">CS189</a>。</p>
<h2 id="_2">课程资源</h2>
<ul>
<li>课程网站：<a href="https://www.coursera.org/learn/machine-learning">https://www.coursera.org/learn/machine-learning</a></li>
<li>课程视频：参见课程网站</li>
<li>课程教材：无</li>
<li>课程作业：参见课程网站</li>
</ul>
<h2 id="_3">资源汇总</h2>
<p>当时重装系统误删了文件，我的代码实现消失在了磁盘的 01 串中。不过这门课由于太过出名，网上想搜不到答案都难，相关课程资料 Coursera 上也一应俱全。</p>
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