{"id":15043294,"url":"https://github.com/luwill/machine_learning_code_implementation","last_synced_at":"2025-05-15T12:03:02.715Z","repository":{"id":37407036,"uuid":"170454911","full_name":"luwill/Machine_Learning_Code_Implementation","owner":"luwill","description":"Mathematical derivation and pure Python code implementation of machine learning algorithms.","archived":false,"fork":false,"pushed_at":"2024-09-18T01:50:11.000Z","size":3658,"stargazers_count":1533,"open_issues_count":4,"forks_count":597,"subscribers_count":30,"default_branch":"master","last_synced_at":"2025-05-15T12:03:01.349Z","etag":null,"topics":["jupyter-notebook","machine-learning","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","readme":"# 机器学习 公式推导与代码实现\n李航老师的《统计学习方法》和周志华老师的西瓜书《机器学习》一直国内机器学习领域的经典教材。本书在这两本书理论框架的基础上，补充了必要的代码实现思路和逻辑过程。\n\n本书在对全部机器学习算法进行分类梳理的基础之上，分别对监督学习单模型、监督学习集成模型、无监督学习模型、概率模型4个大类26个经典算法进行了相对完整的公式推导和必要的代码实现，旨在帮助机器学习入门读者完整地掌握算法细节、实现方法以及内在逻辑。本书可作为《统计学习方法》和西瓜书《机器学习》的补充材料。\n\n---\n### 使用说明\n本仓库为《机器学习 公式推导与代码实现》一书配套代码库，相较于书中代码而言，仓库代码随时保持更新和迭代。目前仓库只开源了全书的代码，全书内容后续也会在仓库中开源。本仓库已经根据书中章节将代码分目录整理好，读者可直接点击相关章节使用该章节代码。\n\n---\n### 纸质版\n\u003cimg \nsrc=\"https://github.com/luwill/louwill-python-learning/raw/master/cover.jpg\"\nwidth = \"280\" height = \"350\"\u003e\n\u003cbr\u003e\n\u003cdiv style=\"color: #999;\nfont-size:11px;\npadding: 2px;\"\u003e\u003c/div\u003e\n\n购买链接：[京东](https://item.jd.com/13581834.html) | [当当](http://product.dangdang.com/29354670.html)\n\n---\n### 配套PPT\n为方便大家更好的使用本书，本书也配套了随书的PPT，购买过纸质书的读者可以在机器学习实验室公众号联系作者获取。\n\n\u003cimg \nsrc=\"https://github.com/luwill/Machine_Learning_Code_Implementation/blob/master/pic/ppt_1.png\"\nwidth = \"534\" height = \"300\"\u003e\n\u003cbr\u003e\n\u003cdiv style=\"color: #999;\nfont-size:11px;\npadding: 2px;\"\u003e第1章示例\u003c/div\u003e\n\n\n\u003cimg \nsrc=\"https://github.com/luwill/Machine_Learning_Code_Implementation/blob/master/pic/ppt_2.png\"\nwidth = \"534\" height = \"300\"\u003e\n\u003cbr\u003e\n\u003cdiv style=\"color: #999;\nfont-size:11px;\npadding: 2px;\"\u003e第2章示例\u003c/div\u003e\n\n\n\u003cimg \nsrc=\"https://github.com/luwill/Machine_Learning_Code_Implementation/blob/master/pic/ppt_3.png\"\nwidth = \"534\" height = \"300\"\u003e\n\u003cbr\u003e\n\u003cdiv style=\"color: #999;\nfont-size:11px;\npadding: 2px;\"\u003e第7章示例\u003c/div\u003e\n\n\u003cimg \nsrc=\"https://github.com/luwill/Machine_Learning_Code_Implementation/blob/master/pic/ppt_4.png\"\nwidth = \"534\" height = \"300\"\u003e\n\u003cbr\u003e\n\u003cdiv style=\"color: #999;\nfont-size:11px;\npadding: 2px;\"\u003e第12章示例\u003c/div\u003e\n\n\n\u003cimg \nsrc=\"https://github.com/luwill/Machine_Learning_Code_Implementation/blob/master/pic/ppt_5.png\"\nwidth = \"534\" height = \"300\"\u003e\n\u003cbr\u003e\n\u003cdiv style=\"color: #999;\nfont-size:11px;\npadding: 2px;\"\u003e第23章示例\u003c/div\u003e\n\n\n---\n### 配套视频讲解（更新中）\n为了帮助广大读者更好地学习和掌握机器学习的一般理论和方法，笔者在PPT基础上同时在为全书配套讲解视频。包括模型的公式手推和代码的讲解。\n\n第一章：[机器学习入门](https://www.bilibili.com/video/BV1jR4y1A7aH#reply112207884144)\n\n---\n### 全书勘误表\n勘误表：[勘误表](https://github.com/luwill/Machine_Learning_Code_Implementation/blob/master/Errata/Errata.md)\n\n---\n### LICENSE\n本项目采用[知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议](https://creativecommons.org/licenses/by-nc-sa/4.0/)进行许可。\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fluwill%2Fmachine_learning_code_implementation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fluwill%2Fmachine_learning_code_implementation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fluwill%2Fmachine_learning_code_implementation/lists"}