{"id":13672079,"url":"https://github.com/RedstoneWill/MachineLearningInAction-Camp","last_synced_at":"2025-04-27T21:31:55.822Z","repository":{"id":49655982,"uuid":"149727933","full_name":"RedstoneWill/MachineLearningInAction-Camp","owner":"RedstoneWill","description":null,"archived":false,"fork":false,"pushed_at":"2018-12-18T17:07:17.000Z","size":41483,"stargazers_count":275,"open_issues_count":0,"forks_count":183,"subscribers_count":38,"default_branch":"master","last_synced_at":"2024-08-03T09:11:26.729Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","readme":"# 《机器学习实战》训练营\n\n## 课程资料\n\n- 《机器学习实战》书籍：[英文版](https://pan.baidu.com/s/1rVWUcPZscdE27lBQwTpoBA)，[中文版](https://pan.baidu.com/s/1a1wN3RKHQFP8GFKywVaHwQ)\n\n- [ApacheCN](http://ml.apachecn.org/mlia/)\n\n- [廖雪峰 Python3 教程](https://www.liaoxuefeng.com/wiki/0014316089557264a6b348958f449949df42a6d3a2e542c000)\n\n- 李航《统计学习方法》(链接：https://pan.baidu.com/s/1MSx407RuPCJt5KSej0Yqlg 密码：h74l)\n\n- 周志华《机器学习》（链接：https://pan.baidu.com/s/1wyqhvJHkI1zHph8RRsm9iw 密码：1475）\n\n- [机器学习该怎么入门？](https://www.zhihu.com/question/20691338/answer/446610161)\n\n\n## 课程安排\n\n**总课时：12 周**\n\n基础：第 1 章\n\n分类： 第 1～7 章\n\n预测：第 8～9 章\n\n无监督式学习：第 10 章\n\n降维与分布式：第 13～14 章\n\n### 第一周\n\n**0 自测与比赛**\n\n- 0.1 机器学习笔试100题\n\n- 0.2 天池比赛流程解析\n\n**1 机器学习基础**\n\n- 1.1 Python 基础知识，Numpy、pandas、Matplotlib 等库的简介\n\n- 1.2 开发环境的搭建：Python3 + Anaconda + Jupyter Notebook\n\n- 1.3 Jupyter Notebook 使用简介\n\n**2 k-近邻算法**\n\n- 2.1 k-近邻算法概述\n\n- 2.2 示例：使用 k-近邻算法改进网站的配对效果\n\n- 2.3 示例：手写识别系统\n\n### 第二周\n\n**3 决策树**\n\n- 3.1 决策树的构造\n\n- 3.3 测试和存储分类器\n\n- 3.4 示例：使用决策树预测隐形眼镜类型\n\n### 第三周\n\n**4 基于概率论的分类方法：朴素贝叶斯**\n\n- 4.1 基于贝叶斯决策理论的分类方法\n\n- 4.2 条件概率\n\n- 4.3 使用条件概率来分类\n\n- 4.4 使用朴素贝叶斯进行文档分类\n\n- 4.5 使用 Python 进行文本分类\n\n- 4.6 示例：使用朴素贝叶斯过滤垃圾邮件\n\n### 第四周\n\n**5 Logistic回归**\n\n- 5.1 基于 Logistic 回归和 Sigmoid 函数的回归\n\n- 5.2 基于最优化方法的最佳回归系数确定\n\n- 5.3 示例：从疝气病症预测病马的死亡率\n\n### 第五周\n\n- 天池o2o预测赛（初级）\n\n### 第六周\n\n**6 支持向量机**\n\n- 6.1 基于最大间隔分隔数据\n\n- 6.2 寻找最大间隔\n\n- 6.3 SMO 高效优化算法\n\n- 6.4 利用完整 Platt SMO 算法加速优化\n\n- 6.5 在复杂数据上应用核函数\n\n- 6.6 手写识别问题\n\n### 第七周\n\n**7 利用 AdaBoost 元算法提高分类性能**\n\n- 7.1 基于数据 多重抽样的分类器\n\n- 7.2 训练算法：基于错误提升分类器的性能\n\n- 7.3 基于单层决策树构建弱分类器\n\n- 7.4 完整 AdaBoost 算法的实现\n\n- 7.5 测试算法：基于 AdaBoost 的分类\n\n- 7.6 示例：在一个难数据集上应用 AdaBoost\n\n- 7.7 非均衡分类问题\n\n### 第八周\n\n**8 预测数值型数据：回归**\n\n- 8.1 用线性回归找到最佳拟合直线\n\n- 8.2 局部加权线性回归\n\n- 8.3 示例：预测鲍鱼的年龄\n\n- 8.4 缩减系数来“理解”数据\n\n- 8.5 权衡偏差和方差\n\n### 第九周\n\n**9 树回归**\n\n- 9.1 复杂数据的局部性建模\n\n- 9.2 连续和离散型特征的树的构建\n\n- 9.3 将 CART 算法用于回归\n\n- 9.4 树减枝\n\n- 9.5 模型树\n\n- 9.6 示例：树回归于标准回归的比较\n\n### 第十周\n\n- 天池o2o预测赛（进阶）\n\n### 第十一周\n\n**10 利用 K-均值聚类算法对未标注数据分组**\n\n- 10.1 K-均值聚类算法\n\n- 10.2 使用后处理来提高聚类性能\n\n- 10.3 二分 K-均值算法\n\n- 10.4 示例：对地图上的点进行聚类\n\n### 第十二周\n\n**13 利用PCA来简化数据**\n\n- 13.1 降纬技术\n\n- 13.2 PCA\n\n- 13.3 示例：利用 PCA 对半导体制造数据降维\n\n**14 利用SVD简化数据**\n\n- 14.1 SVD 的应用\n\n- 14.2 矩阵分解\n\n- 14.3 利用 Python 实现 SVD\n\n- 14.4 基于协调过滤的推荐引擎\n\n- 14.5 示例：餐馆菜肴推荐引擎\n\n- 14.6 示例：基于 SVD 的图像压缩\n","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FRedstoneWill%2FMachineLearningInAction-Camp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FRedstoneWill%2FMachineLearningInAction-Camp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FRedstoneWill%2FMachineLearningInAction-Camp/lists"}