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个人机器学习(Machine Learning, ML)笔记 参考多个学习资源 包含个人理解与总结 不能保证完全专业正确性 如有错误欢迎提出 期待您的补充贡献！

<img src="https://i.loli.net/2018/07/25/5b589d4121cea.png" width="70%">

目录

<table> <tr> <th>章节</th> <th>小节</th> <th>数学知识补充</th> <th>参与贡献人</th> </tr> <tr> <td>介 绍</td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/%E7%AE%80%E4%BB%8B%20%E4%BB%80%E4%B9%88%E6%98%AF%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/%E7%AE%80%E4%BB%8B%20%E4%BB%80%E4%B9%88%E6%98%AF%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0.md">什么是机器学习</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td>第 1 章: 机器学习基础</td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/1.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80/1.1%20%E8%AE%A4%E8%AF%86%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%AD%E7%9A%84%E6%95%B0%E6%8D%AE/1.1%20%E8%AE%A4%E8%AF%86%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%AD%E7%9A%84%E6%95%B0%E6%8D%AE.md">1.1: 认识机器学习中的数据</a></td> <td>线性代数: 矩阵</td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td></td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/1.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80/1.2%20%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9A%84%E4%B8%BB%E8%A6%81%E4%BB%BB%E5%8A%A1/1.2%20%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9A%84%E4%B8%BB%E8%A6%81%E4%BB%BB%E5%8A%A1.md">1.2: 机器学习的主要任务</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td></td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/1.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80/1.3%20%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E7%9A%84%E5%87%A0%E5%A4%A7%E5%88%86%E7%B1%BB/1.3%20%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E7%9A%84%E5%87%A0%E5%A4%A7%E5%88%86%E7%B1%BB.md">1.3: 机器学习算法的几大分类</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td>第 2 章: 机器学习相关工具安装与使用</td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/2.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9B%B8%E5%85%B3%E5%B7%A5%E5%85%B7%E5%AE%89%E8%A3%85%E4%B8%8E%E4%BD%BF%E7%94%A8/2.1%20Python3%E7%9A%84%E5%AE%89%E8%A3%85/2.1%20Python3%E7%9A%84%E5%AE%89%E8%A3%85.md">2.1: Python3的安装</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td></td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/2.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9B%B8%E5%85%B3%E5%B7%A5%E5%85%B7%E5%AE%89%E8%A3%85%E4%B8%8E%E4%BD%BF%E7%94%A8/2.2%20%E7%9B%B8%E5%85%B3IDE%E7%9A%84%E5%AE%89%E8%A3%85%E4%B8%8E%E4%BD%BF%E7%94%A8/2.2%20%E7%9B%B8%E5%85%B3IDE%E7%9A%84%E5%AE%89%E8%A3%85%E4%B8%8E%E4%BD%BF%E7%94%A8.md">2.2: 相关IDE的安装与使用</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td></td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/2.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9B%B8%E5%85%B3%E5%B7%A5%E5%85%B7%E5%AE%89%E8%A3%85%E4%B8%8E%E4%BD%BF%E7%94%A8/2.3%20Numpy%E7%9A%84%E5%9F%BA%E6%9C%AC%E4%BD%BF%E7%94%A8%EF%BC%88%E4%B8%80%EF%BC%89/2.3%20Numpy%E7%9A%84%E5%9F%BA%E6%9C%AC%E4%BD%BF%E7%94%A8%EF%BC%88%E4%B8%80%EF%BC%89.md">2.3: Numpy的基本使用（一）</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td></td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/2.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9B%B8%E5%85%B3%E5%B7%A5%E5%85%B7%E5%AE%89%E8%A3%85%E4%B8%8E%E4%BD%BF%E7%94%A8/2.4%20Numpy%E7%9A%84%E5%9F%BA%E6%9C%AC%E4%BD%BF%E7%94%A8%EF%BC%88%E4%BA%8C%EF%BC%89/2.4%20Numpy%E7%9A%84%E5%9F%BA%E6%9C%AC%E4%BD%BF%E7%94%A8%EF%BC%88%E4%BA%8C%EF%BC%89.md">2.4: Numpy的基本使用（二）</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td></td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/2.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9B%B8%E5%85%B3%E5%B7%A5%E5%85%B7%E5%AE%89%E8%A3%85%E4%B8%8E%E4%BD%BF%E7%94%A8/2.5%20Numpy%E7%9A%84%E5%9F%BA%E6%9C%AC%E4%BD%BF%E7%94%A8%EF%BC%88%E4%B8%89%EF%BC%89/2.5%20Numpy%E7%9A%84%E5%9F%BA%E6%9C%AC%E4%BD%BF%E7%94%A8%EF%BC%88%E4%B8%89%EF%BC%89.md">2.5: Numpy的基本使用（三）</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td></td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/2.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9B%B8%E5%85%B3%E5%B7%A5%E5%85%B7%E5%AE%89%E8%A3%85%E4%B8%8E%E4%BD%BF%E7%94%A8/2.6%20Matplotlib%E7%9A%84%E5%9F%BA%E6%9C%AC%E4%BD%BF%E7%94%A8/2.6%20Matplotlib%E7%9A%84%E5%9F%BA%E6%9C%AC%E4%BD%BF%E7%94%A8.md">2.6: Matplotlib的基本使用</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td></td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/2.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9B%B8%E5%85%B3%E5%B7%A5%E5%85%B7%E5%AE%89%E8%A3%85%E4%B8%8E%E4%BD%BF%E7%94%A8/2.7%20Scikit-learn%E7%9A%84%E7%AE%80%E5%8D%95%E6%8E%A2%E7%B4%A2/2.7%20Scikit-learn%E7%9A%84%E7%AE%80%E5%8D%95%E6%8E%A2%E7%B4%A2.md">2.7: Scikit-learn的简单探索</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td>第 3 章: 最简单的分类算法：k近邻算法</td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/3.KNN/3.1%20KNN%E7%AE%97%E6%B3%95%E5%8E%9F%E7%90%86%E4%B8%8E%E7%AE%80%E5%8D%95%E5%AE%9E%E7%8E%B0/3.1%20KNN%E7%AE%97%E6%B3%95%E5%8E%9F%E7%90%86%E4%B8%8E%E7%AE%80%E5%8D%95%E5%AE%9E%E7%8E%B0.md">3.1: KNN算法原理与简单实现</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td></td> <td><a href="https://github.com/Exrick/Machine-Learning/blob/master/3.KNN/3.2%20Scikit-learn%E4%B8%ADKNN%E7%AE%97%E6%B3%95%E7%9A%84%E5%B0%81%E8%A3%85/3.2%20Scikit-learn%E4%B8%ADKNN%E7%AE%97%E6%B3%95%E7%9A%84%E5%B0%81%E8%A3%85.md">3.2: Scikit-learn中KNN算法的封装</a></td> <td></td> <td><a href="https://github.com/Exrick">Exrick</a></td> </tr> <tr> <td></td> <td>3.3: 如何判断算法的好坏与性能</td> <td></td> <td><a href="https://github.com/Exrick" >Exrick</a></td> </tr> </table>

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参考