[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-BindiChen--machine-learning":3,"tool-BindiChen--machine-learning":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":70,"readme_en":71,"readme_zh":72,"quickstart_zh":73,"use_case_zh":74,"hero_image_url":75,"owner_login":76,"owner_name":77,"owner_avatar_url":78,"owner_bio":79,"owner_company":80,"owner_location":80,"owner_email":80,"owner_twitter":80,"owner_website":80,"owner_url":81,"languages":82,"stars":91,"forks":92,"last_commit_at":93,"license":80,"difficulty_score":94,"env_os":95,"env_gpu":95,"env_ram":95,"env_deps":96,"category_tags":105,"github_topics":106,"view_count":23,"oss_zip_url":80,"oss_zip_packed_at":80,"status":16,"created_at":110,"updated_at":111,"faqs":112,"releases":113},2302,"BindiChen\u002Fmachine-learning","machine-learning","Practical Full-Stack Machine Learning","machine-learning 是一个专注于全栈机器学习实战的开源知识库，由作者 Bindi Chen 整理并与其 Medium 技术博客深度联动。它并非一个独立的软件框架，而是一套系统化的学习资源集合，旨在帮助开发者打通从环境配置、数据获取、清洗分析到模型构建的完整工作流。\n\n针对初学者在入门机器学习时常遇到的“环境搭建难”、“数据处理琐碎”以及“理论脱离实战”等痛点，machine-learning 提供了大量基于真实场景的 Jupyter Notebook 代码示例。内容涵盖使用 virtualenv 和 conda 配置开发环境、利用 Pandas 高效读写与清洗各类格式数据（如 CSV、JSON、HTML）、执行探索性数据分析（EDA），以及运用 TensorFlow、PyTorch 和 Scikit-Learn 进行模型开发。其独特亮点在于不仅讲解核心算法，更细致地分享了诸如日期解析加速、嵌套 JSON 扁平化、缺失值处理等提升效率的实用技巧。\n\n这套资源非常适合希望提升工程落地能力的 Python 开发者、数据分析师以及人工智能领域的研究人员。对于想要跳过繁琐文档搜索，","machine-learning 是一个专注于全栈机器学习实战的开源知识库，由作者 Bindi Chen 整理并与其 Medium 技术博客深度联动。它并非一个独立的软件框架，而是一套系统化的学习资源集合，旨在帮助开发者打通从环境配置、数据获取、清洗分析到模型构建的完整工作流。\n\n针对初学者在入门机器学习时常遇到的“环境搭建难”、“数据处理琐碎”以及“理论脱离实战”等痛点，machine-learning 提供了大量基于真实场景的 Jupyter Notebook 代码示例。内容涵盖使用 virtualenv 和 conda 配置开发环境、利用 Pandas 高效读写与清洗各类格式数据（如 CSV、JSON、HTML）、执行探索性数据分析（EDA），以及运用 TensorFlow、PyTorch 和 Scikit-Learn 进行模型开发。其独特亮点在于不仅讲解核心算法，更细致地分享了诸如日期解析加速、嵌套 JSON 扁平化、缺失值处理等提升效率的实用技巧。\n\n这套资源非常适合希望提升工程落地能力的 Python 开发者、数据分析师以及人工智能领域的研究人员。对于想要跳过繁琐文档搜索，直接通过可运行代码掌握数据处理窍门和建模流程的学习者而言，machine-learning 是一份极具价值的实战指南。","[![View on Medium](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMedium-View%20on%20Medium-red?logo=medium)](https:\u002F\u002Fbindichen.medium.com\u002F) [![View on GitHub](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View_on_GitHub-blue?logo=GitHub)](https:\u002F\u002Fgithub.com\u002FBindiChen\u002Fmachine-learning)\n\n# Machine Learning\nPractical Machine Learning topics for articles in my [Medium blog](https:\u002F\u002Fbindichen.medium.com\u002F) \n\n#### Content\n1. [General Setup](#general-setup)\n1. [Data Analysis](#data-analysis)\n   * [Pandas](#pandas)\n   * [Applied Data Analysis and EDA](#applied-data-analysis-and-eda)\n1. [Web scraping](#web-scraping)\n1. [Data Visualization](#data-visualization)\n1. [TensorFlow](#tensorflow)\n1. [PyTorch](#pytorch)\n1. [Scikit-Learn](#scikit-learn-and-general-machine-learning)\n\n## General Setup\n* [Create Virtual Environment using “virtualenv” and add it to Jupyter Notebook](https:\u002F\u002Ftowardsdatascience.com\u002Fcreate-virtual-environment-using-virtualenv-and-add-it-to-jupyter-notebook-6e1bf4e03415)\n* [Create Virtual Environment using “conda” and add it to Jupyter Notebook](https:\u002F\u002Fmedium.com\u002Fanalytics-vidhya\u002Fcreate-virtual-environment-using-conda-and-add-it-to-jupyter-notebook-d319a81dfd1)\n* [7 ways to load external data into Google Colab](https:\u002F\u002Fbindichen.medium.com\u002F7-ways-to-load-external-data-into-google-colab-7ba73e7d5fc7)\n\n\n## Data Analysis\n\n### Pandas\n\n* Reading & Writing data\n    * [4 tricks to parse date columns with Pandas `read_csv()`](https:\u002F\u002Ftowardsdatascience.com\u002F4-tricks-you-should-know-to-parse-date-columns-with-pandas-read-csv-27355bb2ad0e) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F012-parse-date-with-read_csv\u002Fparse-date-column-with-read_csv.ipynb)\n    * [Pandas `read_csv()` tricks you should know](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fall-the-pandas-read-csv-you-should-know-to-speed-up-your-data-analysis-1e16fe1039f3) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F006-pandas-read_csv\u002Fread_csv-tricks.ipynb)\n    * [All Pandas `read_html()` you should know for scraping data from HTML tables](https:\u002F\u002Fbindichen.medium.com\u002Fall-pandas-read-html-you-should-know-for-scraping-data-from-html-tables-a3cbb5ce8274) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F024-pandas-read_html\u002Fpandas-read_html.ipynb)\n    * [How to convert JSON into a Pandas DataFrame?](https:\u002F\u002Fbindichen.medium.com\u002Fhow-to-convert-json-into-a-pandas-dataframe-100b2ae1e0d8) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F027-pandas-convert-json\u002Fpandas-convert-json.ipynb)\n    * [Pandas `json_normalize()` for flattening JSON](https:\u002F\u002Fbindichen.medium.com\u002Fall-pandas-json-normalize-you-should-know-for-flattening-json-13eae1dfb7dd) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F028-pandas-json_normalize\u002Fpandas-json_normalize.ipynb)\n* Data Profiling\n    * [9 Pandas `value_counts()` tricks](https:\u002F\u002Ftowardsdatascience.com\u002F9-pandas-value-counts-tricks-to-improve-your-data-analysis-7980a2b46536) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F046-pandas-value_counts\u002Fpandas-value_counts.ipynb)\n    * [Getting more value from the Pandas `count()`](https:\u002F\u002Fbindichen.medium.com\u002Fgetting-more-value-from-the-pandas-count-3e45a62c7077) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F043-pandas-count\u002Fpandas-count.ipynb)\n* Data Preprocessing\n    * [What is One-Hot Encoding and how to use `get_dummies()`](https:\u002F\u002Ftowardsdatascience.com\u002Fwhat-is-one-hot-encoding-and-how-to-use-pandas-get-dummies-function-922eb9bd4970) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F002-one-hot-encoding\u002Fone-hot-encoding.ipynb)\n    * [Working with missing values in Pandas](https:\u002F\u002Ftowardsdatascience.com\u002Fworking-with-missing-values-in-pandas-5da45d16e74) | TBA soon\n    * [Working with datetime in Pandas DataFrame](https:\u002F\u002Ftowardsdatascience.com\u002Fworking-with-datetime-in-pandas-dataframe-663f7af6c587) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F008-pandas-datetime\u002Fpandas-datetime.ipynb)\n    * [11 Tricks to Master `sort_values()` in Pandas](https:\u002F\u002Fbindichen.medium.com\u002F11-tricks-to-master-values-sorting-in-pandas-7f2cfbf19730) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F040-pandas-sort_values\u002Fpandas-sort_values.ipynb)\n    * [How to do a Custom Sort on Pandas DataFrame](https:\u002F\u002Fbindichen.medium.com\u002Fhow-to-do-a-custom-sort-on-pandas-dataframe-ac18e7ea5320) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F017-pandas-custom-sort\u002Fpandas-custom-sort.ipynb)\n    * [Pandas `cut()` to transform numerical data into categorical data](https:\u002F\u002Fbindichen.medium.com\u002Fall-pandas-cut-you-should-know-for-transforming-numerical-data-into-categorical-data-1370cf7f4c4f) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F026-pandas-cut\u002Fpandas-cut.ipynb)\n    * [Pandas `qcut()` for binning numerical data based on sample quantiles](https:\u002F\u002Fbindichen.medium.com\u002Fall-pandas-qcut-you-should-know-for-binning-numerical-data-based-on-sample-quantiles-c8b13a8ed844) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F041-pandas-qcut\u002Fpandas-qcut.ipynb)\n    * [Finding and removing duplicate rows in Pandas DataFrame](https:\u002F\u002Fbindichen.medium.com\u002Ffinding-and-removing-duplicate-rows-in-pandas-dataframe-c6117668631f) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F034-pandas-find-and-remove-duplicates\u002Fpandas-duplicates.ipynb)\n    * [Renaming columns in a Pandas DataFrame](https:\u002F\u002Fbindichen.medium.com\u002Frenaming-columns-in-a-pandas-dataframe-1d909360ddc6) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F033-pandas-rename-columns\u002Fpandas-rename-columns.ipynb)\n    * [10 tricks for Converting data to a numeric type](https:\u002F\u002Fbindichen.medium.com\u002Fconverting-data-to-a-numeric-type-in-pandas-db9415caab0b) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F036-pandas-change-data-to-numeric-type\u002Fchange-data-to-a-numeric-type.ipynb)\n    * [10 tricks for Converting numbers and strings to datetime](https:\u002F\u002Fbindichen.medium.com\u002F10-tricks-for-converting-numbers-and-strings-to-datetime-in-pandas-82a4645fc23d) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F037-pandas-change-data-to-datetime\u002Fchange-data-to-datetime.ipynb)\n    * [Pandas `resample()` tricks for manipulating time-series data](https:\u002F\u002Fbindichen.medium.com\u002Fpandas-resample-tricks-you-should-know-for-manipulating-time-series-data-7e9643a7e7f3) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F020-pandas-resample\u002Fpandas-resample.ipynb)\n    * [When to use Pandas `transform()` function](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fwhen-to-use-pandas-transform-function-df8861aa0dcf) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F013-pandas-transform\u002Fpandas-transform.ipynb)\n    * [Difference between `apply()` and `transform()` in Pandas](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fdifference-between-apply-and-transform-in-pandas-242e5cf32705) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F014-pandas-apply-vs-transform\u002Fpandas-apply-vs-transform.ipynb)\n    * [Introduction to Pandas `apply()`, `applymap()`, and `map()`](https:\u002F\u002Ftowardsdatascience.com\u002Fintroduction-to-pandas-apply-applymap-and-map-5d3e044e93ff) | TBA soon\n    * [All the Pandas `shift()` you should know](https:\u002F\u002Fbindichen.medium.com\u002Fall-the-pandas-shift-you-should-know-for-data-analysis-791c1692b5e) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F021-pandas-shift\u002Fpandas-shift.ipynb)\n    * [Delete rows\u002Fcolumns from a DataFrame using `drop()`](https:\u002F\u002Fbindichen.medium.com\u002Fdelete-rows-and-columns-from-a-dataframe-using-pandas-drop-d2533cf7b4bd) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F063-pandas-drop\u002Fpandas-drop.ipynb)\n    * [Flatten MultiIndex columns and rows](https:\u002F\u002Fbindichen.medium.com\u002Fhow-to-flatten-multiindex-columns-and-rows-in-pandas-f5406c50e569) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F069-pandas-flatten-multiIndex\u002Fflatten-multiindex.ipynb)\n* Combining data\n    * [All the Pandas `merge()` for combining datasets](https:\u002F\u002Fbindichen.medium.com\u002Fall-the-pandas-merge-you-should-know-for-combining-datasets-526b9ecaf184) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F018-pandas-merge\u002Fpandas-merge.ipynb)\n    * [Pandas `concat()` tricks to speed up your data analysis](https:\u002F\u002Ftowardsdatascience.com\u002Fpandas-concat-tricks-you-should-know-to-speed-up-your-data-analysis-cd3d4fdfe6dd) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F016-pandas-concat\u002Fpandas-concat.ipynb)\n    * [5 Tricks to master Pandas `append()`](https:\u002F\u002Fbindichen.medium.com\u002F5-tricks-to-master-pandas-append-ede4318cc700) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F055-pandas-append\u002Fpandas-append.ipynb)\n* Selecting and Querying\n    * [Pandas `loc` and `iloc` for selecting data](https:\u002F\u002Fbindichen.medium.com\u002Fhow-to-use-loc-and-iloc-for-selecting-data-in-pandas-bd09cb4c3d79) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F030-pandas-loc-and-iloc\u002Fpandas-loc-and-iloc.ipynb)\n    * [Pandas Equivalents of various SQL queries](https:\u002F\u002Ftowardsdatascience.com\u002Fintroduction-to-pandas-equivalents-of-various-sql-queries-448fb57dd9b9) | TBA soon\n    * [Creating conditional columns with Numpy `select()` and `where()` methods](https:\u002F\u002Fbindichen.medium.com\u002Fcreating-conditional-columns-on-pandas-with-numpy-select-and-where-methods-8ee6e2dbd5d5) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F015-pandas-numpy-select-where\u002Fpandas-and-numpy-select-where.ipynb)\n    * [Accessing data in a MultiIndex DataFrame](https:\u002F\u002Fbindichen.medium.com\u002Faccessing-data-in-a-multiindex-dataframe-in-pandas-569e8767201d) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F031-pandas-multiIndex\u002Fmultiindex-selection.ipynb)\n* Reshaping\n    * [Reshaping a DataFrame from wide to long format using `melt()`](https:\u002F\u002Fbindichen.medium.com\u002Freshaping-a-dataframe-using-pandas-melt-83a151ce1907) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F048-pandas-melt\u002Fpandas-melt.ipynb)\n    * [Reshaping a DataFrame from long to wide format using `pivot()`](https:\u002F\u002Fbindichen.medium.com\u002Freshaping-a-dataframe-from-long-to-wide-format-using-pivot-b099930b30ae) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F049-pandas-pivot\u002Fpivot.ipynb)\n    * [Reshaping a DataFrame\u002FSeries with `stack()` and `unstack()`](https:\u002F\u002Fbindichen.medium.com\u002Freshaping-a-dataframe-with-pandas-stack-and-unstack-925dc9ce1289) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F067-pandas-stack\u002Fpandas-stack-unstack.ipynb)\n    * [Exploding a list-like column with Pandas `explode()` method](https:\u002F\u002Fbindichen.medium.com\u002Fexploding-a-list-like-column-with-pandas-explode-method-3ffd41f9f7e2) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F074-pandas-explodes\u002Fexplode-list-like-columns.ipynb)\n* Grouping and Summarizing\n    * [Pandas `groupby()` for grouping data and performing operations](https:\u002F\u002Fbindichen.medium.com\u002Fall-pandas-groupby-you-should-know-for-grouping-data-and-performing-operations-2a8ec1327b5) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F032-pandas-groupby\u002Fpandas-groupby.ipynb)\n    * [A Practical Introduction to Pandas `pivot_table()`](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fa-practical-introduction-to-pandas-pivot-table-function-3e1002dcd4eb) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F003-pandas-pivot-table\u002F003-pandas-pivot-table.ipynb)\n    * [Summarizing data with Pandas `crosstab()`](https:\u002F\u002Fbindichen.medium.com\u002Fsummarizing-data-with-pandas-crosstab-efc8b9abecf) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F045-pandas-crosstab\u002Fpandas-crosstab.ipynb)\n* Best Practice & Code Readability\n    * [Using Pandas `pipe()` to improve code readability](https:\u002F\u002Ftowardsdatascience.com\u002Fusing-pandas-pipe-function-to-improve-code-readability-96d66abfaf8) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F001-pandad-pipe-function\u002Fpandas-pipe-to-improve-code-readability.ipynb)\n    * [Using Pandas method chaining to improve code readability](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fusing-pandas-method-chaining-to-improve-code-readability-d8517c5626ac) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F007-method-chaining\u002Fmethod-chaining.ipynb)\n    * [7 setups you should include at the beginning of a data science project](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002F7-setups-you-should-include-at-the-beginning-of-a-data-science-project-8232ab10a1ec) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F004-7-setups-for-a-data-science-project\u002F7-setups.ipynb)\n    * [6 Pandas Tricks you should know to speed up your data analysis](https:\u002F\u002Ftowardsdatascience.com\u002F6-pandas-tricks-you-should-know-to-speed-up-your-data-analysis-d3dec7c29e5) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F005-6-pandas-tricks\u002F6-pandas-tricks.ipynb)\n    * [8 Commonly used Pandas display options you should know](https:\u002F\u002Fbindichen.medium.com\u002F8-commonly-used-pandas-display-options-you-should-know-a832365efa95) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F035-pandas-display-opts\u002Fpandas-display-options.ipynb)\n* Introduction & Others\n    * [A Practical Introduction to Pandas Series](https:\u002F\u002Fbindichen.medium.com\u002Fa-practical-introduction-to-pandas-series-9915521cdc69) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F029-pandas-series\u002Fintro-to-pands-series.ipynb)\n\n### Applied Data Analysis and EDA\n\n* [COVID-19 data processing with Pandas DataFrame](https:\u002F\u002Ftowardsdatascience.com\u002Fcovid-19-data-processing-58aaa3663f6) | TBA soon\n\n## Web scraping\n* [Scraping tables from a JavaScript webpage using Selenium, BeautifulSoup, and Pandas](https:\u002F\u002Fmedium.com\u002Fanalytics-vidhya\u002Fscraping-tables-from-a-javascript-webpage-using-selenium-beautifulsoup-and-pandas-cbd305ca75fe) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](web-scraping\u002F001-selenium-beautifulSoup-and-pandas\u002Fmain.py)\n\n\n## Data Visualization\n\n* [Dual-axis combo chart in Python - Matplotlib, Seaborn, and Pandas `plot()`](https:\u002F\u002Fbindichen.medium.com\u002Fcreating-a-dual-axis-combo-chart-in-python-52624b187834) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-visualization\u002F0006-dual-axis-combo-chart\u002Fdual-axis-combo-chart.ipynb)\n* [Adding 3rd Y-axis to combo chart in Python - Matplotlib, Seaborn, and Pandas `plot()`](https:\u002F\u002Fbindichen.medium.com\u002Fadding-a-third-y-axis-to-python-combo-chart-39f60fb66708) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-visualization\u002F0010-multiple-y-axis\u002Fmultiple-y-axis-combo-chart.ipynb)\n\nAltair\n* [Python Interactive Data Visualization with Altair](https:\u002F\u002Ftowardsdatascience.com\u002Fpython-interactive-data-visualization-with-altair-b4c4664308f8) | [Gist](https:\u002F\u002Fgist.github.com\u002FBindiChen\u002F0dea2e7fa189f8ff1397180f3b764cc7#file-altair-interactive-selection-chart-py)\n* [Interactive Data Visualization for exploring Coronavirus Spreads](https:\u002F\u002Ftowardsdatascience.com\u002Finteractive-data-visualization-for-exploring-coronavirus-spreads-f33cabc64043) | [Gist](https:\u002F\u002Fgist.github.com\u002FBindiChen\u002Fde39182e050962c0b627d5146e3bce09#file-altair-data-visualization-py)\n\nMatplotlib\n* [Matplotlib animation in Jupyter Notebook](https:\u002F\u002Fbindichen.medium.com\u002Fmatplotlib-animations-in-jupyter-notebook-4422e4f0e389) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-visualization\u002F0001-matplotlib-animation\u002Fmatplotlib-animation-notebook.ipynb)\n* [Matplotlib Linear Regression animation in Jupyter Notebook](https:\u002F\u002Fbindichen.medium.com\u002Fmatplotlib-linear-regression-animation-in-jupyter-notebook-2435b711bea2) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-visualization\u002F0002-matplotlib-animation-with-regression\u002Fmatplotlib-linear-regression-animation.ipynb)\n\n## TensorFlow\n\n* [The Google's 7 steps of Machine Learing in Practice](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-googles-7-steps-of-machine-learning-in-practice-a-tensorflow-example-for-structured-data-96ccbb707d77) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](\u002Ftensorflow2\u002F001-googles-7-steps-of-machine-learning-in-practice\u002F001-googles-7-steps-of-machine-learning-in-practice.ipynb)\n* [3 ways to create a Machine Learning model with Keras and TensorFlow 2.0](https:\u002F\u002Ftowardsdatascience.com\u002F3-ways-to-create-a-machine-learning-model-with-keras-and-tensorflow-2-0-de09323af4d3) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F002-3-ways-to-build-machine-learning-model-with-keras\u002F3-ways-to-build-a-machine-learning-model-with-keras.ipynb)\n* [Model Regularization in practice](https:\u002F\u002Ftowardsdatascience.com\u002Fmachine-learning-model-regularization-in-practice-an-example-with-keras-and-tensorflow-2-0-52a96746123e) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F003-model-regularization\u002Fmodel-regularization.ipynb)\n* [Batch Normalization in practice](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fbatch-normalization-in-practice-an-example-with-keras-and-tensorflow-2-0-b1ec28bde96f) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F004-batch-norm\u002Fbatch-normalization.ipynb)\n* [Early Stopping in practice](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fa-practical-introduction-to-early-stopping-in-machine-learning-550ac88bc8fd) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F005-early-stopping\u002Fearly-stopping.ipynb)\n* [Learning Rate schedules in Practice](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Flearning-rate-schedule-in-practice-an-example-with-keras-and-tensorflow-2-0-2f48b2888a0c) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F006-learning-rate-schedules\u002Flearning-rate-schedules.ipynb)\n* [Keras Callbacks in Practice](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fa-practical-introduction-to-keras-callbacks-in-tensorflow-2-705d0c584966) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F007-keras-callback\u002Fkeras-callbacks.ipynb)\n* [Keras Custom Callbacks](https:\u002F\u002Fbindichen.medium.com\u002Fbuilding-custom-callbacks-with-keras-and-tensorflow-2-85e1b79915a3) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F008-keras-custom-callback\u002Fkeras-custom-callback.ipynb)\n* [7 popular activation functions in Deep Learning](https:\u002F\u002Fbindichen.medium.com\u002F7-popular-activation-functions-you-should-know-in-deep-learning-and-how-to-use-them-with-keras-and-27b4d838dfe6) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F010-popular-activation-functions\u002Fpopular-activation-functions.ipynb)\n* [Why ReLU in Deep Learning and the best practice](https:\u002F\u002Ftowardsdatascience.com\u002Fwhy-rectified-linear-unit-relu-in-deep-learning-and-the-best-practice-to-use-it-with-tensorflow-e9880933b7ef) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F011-relu\u002Frelu-and-best-practice.ipynb)\n\n### PyTorch\n\nTBA\n\n## Scikit-Learn and General Machine Learning\n\n* [A Practical Introduction to Grid Search, Random Search, and Bayes Search](https:\u002F\u002Fbindichen.medium.com\u002Fa-practical-introduction-to-grid-search-random-search-and-bayes-search-d5580b1d941d) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](traditional-machine-learning\u002F005-grid-search-vs-random-search-vs-bayes-search\u002Fgridsearch-vs-randomsearch-vs-bayessearch.ipynb)\n* [A Practical Introduction to 9 Regression Algorithms](https:\u002F\u002Fbindichen.medium.com\u002Fa-practical-introduction-to-9-regression-algorithms-389057f86eb9) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](traditional-machine-learning\u002F001-regression-algorithms\u002Fregression-algorithms.ipynb)\n* Train-Test split and Cross-Validation you should know in Machine Learning (TBA) | [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](traditional-machine-learning\u002F006-train-test-split-and-cross-validation\u002Ftrain-test-and-cross-validation.ipynb)\n\n","[![在Medium上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMedium-View%20on%20Medium-red?logo=medium)](https:\u002F\u002Fbindichen.medium.com\u002F) [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View_on_GitHub-blue?logo=GitHub)](https:\u002F\u002Fgithub.com\u002FBindiChen\u002Fmachine-learning)\n\n# 机器学习\n我[Medium博客](https:\u002F\u002Fbindichen.medium.com\u002F)中关于实用机器学习主题的文章\n\n#### 内容\n1. [通用设置](#general-setup)\n1. [数据分析](#data-analysis)\n   * [Pandas](#pandas)\n   * [应用数据分析与EDA](#applied-data-analysis-and-eda)\n1. [网页爬虫](#web-scraping)\n1. [数据可视化](#data-visualization)\n1. [TensorFlow](#tensorflow)\n1. [PyTorch](#pytorch)\n1. [Scikit-Learn](#scikit-learn-and-general-machine-learning)\n\n## 通用设置\n* [使用“virtualenv”创建虚拟环境并将其添加到Jupyter Notebook](https:\u002F\u002Ftowardsdatascience.com\u002Fcreate-virtual-environment-using-virtualenv-and-add-it-to-jupyter-notebook-6e1bf4e03415)\n* [使用“conda”创建虚拟环境并将其添加到Jupyter Notebook](https:\u002F\u002Fmedium.com\u002Fanalytics-vidhya\u002Fcreate-virtual-environment-using-conda-and-add-it-to-jupyter-notebook-d319a81dfd1)\n* [7种将外部数据加载到Google Colab的方法](https:\u002F\u002Fbindichen.medium.com\u002F7-ways-to-load-external-data-into-google-colab-7ba73e7d5fc7)\n\n\n## 数据分析\n\n### Pandas\n\n* 读取与写入数据\n    * [使用 Pandas `read_csv()` 解析日期列的 4 个技巧](https:\u002F\u002Ftowardsdatascience.com\u002F4-tricks-you-should-know-to-parse-date-columns-with-pandas-read-csv-27355bb2ad0e) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F012-parse-date-with-read_csv\u002Fparse-date-column-with-read_csv.ipynb)\n    * [你应该掌握的 Pandas `read_csv()` 技巧](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fall-the-pandas-read-csv-you-should-know-to-speed-up-your-data-analysis-1e16fe1039f3) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F006-pandas-read_csv\u002Fread_csv-tricks.ipynb)\n    * [从 HTML 表格中抓取数据时应掌握的 Pandas `read_html()` 全部知识](https:\u002F\u002Fbindichen.medium.com\u002Fall-pandas-read-html-you-should-know-for-scraping-data-from-html-tables-a3cbb5ce8274) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F024-pandas-read_html\u002Fpandas-read_html.ipynb)\n    * [如何将 JSON 转换为 Pandas DataFrame？](https:\u002F\u002Fbindichen.medium.com\u002Fhow-to-convert-json-into-a-pandas-dataframe-100b2ae1e0d8) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F027-pandas-convert-json\u002Fpandas-convert-json.ipynb)\n    * [使用 Pandas `json_normalize()` 展平 JSON 数据](https:\u002F\u002Fbindichen.medium.com\u002Fall-pandas-json-normalize-you-should-know-for-flattening-json-13eae1dfb7dd) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F028-pandas-json_normalize\u002Fpandas-json_normalize.ipynb)\n* 数据剖析\n    * [9 个 Pandas `value_counts()` 技巧](https:\u002F\u002Ftowardsdatascience.com\u002F9-pandas-value-counts-tricks-to-improve-your-data-analysis-7980a2b46536) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F046-pandas-value_counts\u002Fpandas-value_counts.ipynb)\n    * [从 Pandas `count()` 中获得更多价值](https:\u002F\u002Fbindichen.medium.com\u002Fgetting-more-value-from-the-pandas-count-3e45a62c7077) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F043-pandas-count\u002Fpandas-count.ipynb)\n* 数据预处理\n    * [什么是独热编码以及如何使用 `get_dummies()`](https:\u002F\u002Ftowardsdatascience.com\u002Fwhat-is-one-hot-encoding-and-how-to-use-pandas-get-dummies-function-922eb9bd4970) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F002-one-hot-encoding\u002Fone-hot-encoding.ipynb)\n    * [在 Pandas 中处理缺失值](https:\u002F\u002Ftowardsdatascience.com\u002Fworking-with-missing-values-in-pandas-5da45d16e74) | 即将发布\n    * [在 Pandas DataFrame 中处理日期时间数据](https:\u002F\u002Ftowardsdatascience.com\u002Fworking-with-datetime-in-pandas-dataframe-663f7af6c587) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F008-pandas-datetime\u002Fpandas-datetime.ipynb)\n    * [掌握 Pandas 中 `sort_values()` 的 11 个技巧](https:\u002F\u002Fbindichen.medium.com\u002F11-tricks-to-master-values-sorting-in-pandas-7f2cfbf19730) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F040-pandas-sort_values\u002Fpandas-sort_values.ipynb)\n    * [如何对 Pandas DataFrame 进行自定义排序](https:\u002F\u002Fbindichen.medium.com\u002Fhow-to-do-a-custom-sort-on-pandas-dataframe-ac18e7ea5320) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F017-pandas-custom-sort\u002Fpandas-custom-sort.ipynb)\n    * [使用 Pandas `cut()` 将数值数据转换为分类数据](https:\u002F\u002Fbindichen.medium.com\u002Fall-pandas-cut-you-should-know-for-transforming-numerical-data-into-categorical-data-1370cf7f4c4f) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F026-pandas-cut\u002Fpandas-cut.ipynb)\n    * [使用 Pandas `qcut()` 基于样本分位数对数值数据进行分箱](https:\u002F\u002Fbindichen.medium.com\u002Fall-pandas-qcut-you-should-know-for-binning-numerical-data-based-on-sample-quantiles-c8b13a8ed844) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F041-pandas-qcut\u002Fpandas-qcut.ipynb)\n    * [查找并删除 Pandas DataFrame 中的重复行](https:\u002F\u002Fbindichen.medium.com\u002Ffinding-and-removing-duplicate-rows-in-pandas-dataframe-c6117668631f) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F034-pandas-find-and-remove-duplicates\u002Fpandas-duplicates.ipynb)\n    * [重命名 Pandas DataFrame 中的列](https:\u002F\u002Fbindichen.medium.com\u002Frenaming-columns-in-a-pandas-dataframe-1d909360ddc6) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F033-pandas-rename-columns\u002Fpandas-rename-columns.ipynb)\n    * [将数据转换为数值类型的 10 个技巧](https:\u002F\u002Fbindichen.medium.com\u002Fconverting-data-to-a-numeric-type-in-pandas-db9415caab0b) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F036-pandas-change-data-to-numeric-type\u002Fchange-data-to-a-numeric-type.ipynb)\n    * [将数字和字符串转换为日期时间的 10 个技巧](https:\u002F\u002Fbindichen.medium.com\u002F10-tricks-for-converting-numbers-and-strings-to-datetime-in-pandas-82a4645fc23d) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F037-pandas-change-data-to-datetime\u002Fchange-data-to-datetime.ipynb)\n    * [用于操作时间序列数据的 Pandas `resample()` 技巧](https:\u002F\u002Fbindichen.medium.com\u002Fpandas-resample-tricks-you-should-know-for-manipulating-time-series-data-7e9643a7e7f3) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F020-pandas-resample\u002Fpandas-resample.ipynb)\n    * [何时使用 Pandas `transform()` 函数](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fwhen-to-use-pandas-transform-function-df8861aa0dcf) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F013-pandas-transform\u002Fpandas-transform.ipynb)\n    * [Pandas 中 `apply()` 和 `transform()` 的区别](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fdifference-between-apply-and-transform-in-pandas-242e5cf32705) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F014-pandas-apply-vs-transform\u002Fpandas-apply-vs-transform.ipynb)\n    * [Pandas `apply()`、`applymap()` 和 `map()` 简介](https:\u002F\u002Ftowardsdatascience.com\u002Fintroduction-to-pandas-apply-applymap-and-map-5d3e044e93ff) | 即将发布\n    * [你应该掌握的 Pandas `shift()` 全部知识](https:\u002F\u002Fbindichen.medium.com\u002Fall-the-pandas-shift-you-should-know-for-data-analysis-791c1692b5e) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F021-pandas-shift\u002Fpandas-shift.ipynb)\n    * [使用 `drop()` 删除 DataFrame 中的行或列](https:\u002F\u002Fbindichen.medium.com\u002Fdelete-rows-and-columns-from-a-dataframe-using-pandas-drop-d2533cf7b4bd) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F063-pandas-drop\u002Fpandas-drop.ipynb)\n    * [展平 MultiIndex 列和行](https:\u002F\u002Fbindichen.medium.com\u002Fhow-to-flatten-multiindex-columns-and-rows-in-pandas-f5406c50e569) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F069-pandas-flatten-multiIndex\u002Fflatten-multiindex.ipynb)\n* 数据合并\n    * [用于合并数据集的 Pandas `merge()` 全部知识](https:\u002F\u002Fbindichen.medium.com\u002Fall-the-pandas-merge-you-should-know-for-combining-datasets-526b9ecaf184) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F018-pandas-merge\u002Fpandas-merge.ipynb)\n    * [加速数据分析的 Pandas `concat()` 技巧](https:\u002F\u002Ftowardsdatascience.com\u002Fpandas-concat-tricks-you-should-know-to-speed-up-your-data-analysis-cd3d4fdfe6dd) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F016-pandas-concat\u002Fpandas-concat.ipynb)\n    * [掌握 Pandas `append()` 的 5 个技巧](https:\u002F\u002Fbindichen.medium.com\u002F5-tricks-to-master-pandas-append-ede4318cc700) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F055-pandas-append\u002Fpandas-append.ipynb)\n* 数据选择与查询\n    * [使用 Pandas `loc` 和 `iloc` 选择数据](https:\u002F\u002Fbindichen.medium.com\u002Fhow-to-use-loc-and-iloc-for-selecting-data-in-pandas-bd09cb4c3d79) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F030-pandas-loc-and-iloc\u002Fpandas-loc-and-iloc.ipynb)\n    * [各种 SQL 查询的 Pandas 等价物](https:\u002F\u002Ftowardsdatascience.com\u002Fintroduction-to-pandas-equivalents-of-various-sql-queries-448fb57dd9b9) | 即将发布\n    * [使用 Numpy 的 `select()` 和 `where()` 方法创建条件列](https:\u002F\u002Fbindichen.medium.com\u002Fcreating-conditional-columns-on-pandas-with-numpy-select-and-where-methods-8ee6e2dbd5d5) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F015-pandas-numpy-select-where\u002Fpandas-and-numpy-select-where.ipynb)\n    * [访问 MultiIndex DataFrame 中的数据](https:\u002F\u002Fbindichen.medium.com\u002Faccessing-data-in-a-multiindex-dataframe-in-pandas-569e8767201d) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F031-pandas-multiIndex\u002Fmultiindex-selection.ipynb)\n* 数据重塑\n    * [使用 `melt()` 将 DataFrame 从宽格式重塑为长格式](https:\u002F\u002Fbindichen.medium.com\u002Freshaping-a-dataframe-using-pandas-melt-83a151ce1907) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F048-pandas-melt\u002Fpandas-melt.ipynb)\n    * [使用 `pivot()` 将 DataFrame 从长格式重塑为宽格式](https:\u002F\u002Fbindichen.medium.com\u002Freshaping-a-dataframe-from-long-to-wide-format-using-pivot-b099930b30ae) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F049-pandas-pivot\u002Fpivot.ipynb)\n    * [使用 `stack()` 和 `unstack()` 重塑 DataFrame\u002FSeries](https:\u002F\u002Fbindichen.medium.com\u002Freshaping-a-dataframe-with-pandas-stack-and-unstack-925dc9ce1289) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F067-pandas-stack\u002Fpandas-stack-unstack.ipynb)\n    * [使用 Pandas `explode()` 方法展开列表型列](https:\u002F\u002Fbindichen.medium.com\u002Fexploding-a-list-like-column-with-pandas-explode-method-3ffd41f9f7e2) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F074-pandas-explodes\u002Fexplode-list-like-columns.ipynb)\n* 分组与汇总\n    * [使用 Pandas `groupby()` 对数据进行分组和操作](https:\u002F\u002Fbindichen.medium.com\u002Fall-pandas-groupby-you-should-know-for-grouping-data-and-performing-operations-2a8ec1327b5) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F032-pandas-groupby\u002Fpandas-groupby.ipynb)\n    * [Pandas `pivot_table()` 的实用入门](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fa-practical-introduction-to-pandas-pivot-table-function-3e1002dcd4eb) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F003-pandas-pivot-table\u002F003-pandas-pivot-table.ipynb)\n    * [使用 Pandas `crosstab()` 汇总数据](https:\u002F\u002Fbindichen.medium.com\u002Fsummarizing-data-with-pandas-crosstab-efc8b9abecf) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F045-pandas-crosstab\u002Fpandas-crosstab.ipynb)\n* 最佳实践与代码可读性\n    * [使用 Pandas `pipe()` 提高代码可读性](https:\u002F\u002Ftowardsdatascience.com\u002Fusing-pandas-pipe-function-to-improve-code-readability-96d66abfaf8) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F001-pandad-pipe-function\u002Fpandas-pipe-to-improve-code-readability.ipynb)\n    * [使用 Pandas 方法链式调用提高代码可读性](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fusing-pandas-method-chaining-to-improve-code-readability-d8517c5626ac) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F007-method-chaining\u002Fmethod-chaining.ipynb)\n    * [数据科学项目开始时应包含的 7 种设置](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002F7-setups-you-should-include-at-the-beginning-of-a-data-science-project-8232ab10a1ec) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F004-7-setups-for-a-data-science-project\u002F7-setups.ipynb)\n    * [加速数据分析的 6 个 Pandas 技巧](https:\u002F\u002Ftowardsdatascience.com\u002F6-pandas-tricks-you-should-know-to-speed-up-your-data-analysis-d3dec7c29e5) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F005-6-pandas-tricks\u002F6-pandas-tricks.ipynb)\n    * [你应该掌握的 8 种常用的 Pandas 显示选项](https:\u002F\u002Fbindichen.medium.com\u002F8-commonly-used-pandas-display-options-you-should-know-a832365efa95) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F035-pandas-display-opts\u002Fpandas-display-options.ipynb)\n* 入门与其他\n    * [Pandas Series 的实用入门](https:\u002F\u002Fbindichen.medium.com\u002Fa-practical-introduction-to-pandas-series-9915521cdc69) | [![在 GitHub 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-analysis\u002F029-pandas-series\u002Fintro-to-pands-series.ipynb)\n\n### 应用数据分析与探索性数据分析\n\n* [使用Pandas DataFrame处理COVID-19数据](https:\u002F\u002Ftowardsdatascience.com\u002Fcovid-19-data-processing-58aaa3663f6) | 即将发布\n\n## 网页爬取\n* [使用Selenium、BeautifulSoup和Pandas从JavaScript网页中抓取表格](https:\u002F\u002Fmedium.com\u002Fanalytics-vidhya\u002Fscraping-tables-from-a-javascript-webpage-using-selenium-beautifulsoup-and-pandas-cbd305ca75fe) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](web-scraping\u002F001-selenium-beautifulSoup-and-pandas\u002Fmain.py)\n\n\n## 数据可视化\n\n* [Python中的双轴组合图 - Matplotlib、Seaborn和Pandas `plot()`](https:\u002F\u002Fbindichen.medium.com\u002Fcreating-a-dual-axis-combo-chart-in-python-52624b187834) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-visualization\u002F0006-dual-axis-combo-chart\u002Fdual-axis-combo-chart.ipynb)\n* [在Python的组合图中添加第3个Y轴 - Matplotlib、Seaborn和Pandas `plot()`](https:\u002F\u002Fbindichen.medium.com\u002Fadding-a-third-y-axis-to-python-combo-chart-39f60fb66708) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-visualization\u002F0010-multiple-y-axis\u002Fmultiple-y-axis-combo-chart.ipynb)\n\nAltair\n* [使用Altair进行Python交互式数据可视化](https:\u002F\u002Ftowardsdatascience.com\u002Fpython-interactive-data-visualization-with-altair-b4c4664308f8) | [Gist](https:\u002F\u002Fgist.github.com\u002FBindiChen\u002F0dea2e7fa189f8ff1397180f3b764cc7#file-altair-interactive-selection-chart-py)\n* [用于探索新冠病毒传播的交互式数据可视化](https:\u002F\u002Ftowardsdatascience.com\u002Finteractive-data-visualization-for-exploring-coronavirus-spreads-f33cabc64043) | [Gist](https:\u002F\u002Fgist.github.com\u002FBindiChen\u002Fde39182e050962c0b627d5146e3bce09#file-altair-data-visualization-py)\n\nMatplotlib\n* [Jupyter Notebook中的Matplotlib动画](https:\u002F\u002Fbindichen.medium.com\u002Fmatplotlib-animations-in-jupyter-notebook-4422e4f0e389) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-visualization\u002F0001-matplotlib-animation\u002Fmatplotlib-animation-notebook.ipynb)\n* [Jupyter Notebook中的Matplotlib线性回归动画](https:\u002F\u002Fbindichen.medium.com\u002Fmatplotlib-linear-regression-animation-in-jupyter-notebook-2435b711bea2) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](data-visualization\u002F0002-matplotlib-animation-with-regression\u002Fmatplotlib-linear-regression-animation.ipynb)\n\n## TensorFlow\n\n* [谷歌机器学习七步实践法](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-googles-7-steps-of-machine-learning-in-practice-a-tensorflow-example-for-structured-data-96ccbb707d77) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](\u002Ftensorflow2\u002F001-googles-7-steps-of-machine-learning-in-practice\u002F001-googles-7-steps-of-machine-learning-in-practice.ipynb)\n* [使用Keras和TensorFlow 2.0创建机器学习模型的三种方法](https:\u002F\u002Ftowardsdatascience.com\u002F3-ways-to-create-a-machine-learning-model-with-keras-and-tensorflow-2-0-de09323af4d3) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F002-3-ways-to-build-machine-learning-model-with-keras\u002F3-ways-to-build-a-machine-learning-model-with-keras.ipynb)\n* [模型正则化实践](https:\u002F\u002Ftowardsdatascience.com\u002Fmachine-learning-model-regularization-in-practice-an-example-with-keras-and-tensorflow-2-0-52a96746123e) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F003-model-regularization\u002Fmodel-regularization.ipynb)\n* [批量归一化实践](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fbatch-normalization-in-practice-an-example-with-keras-and-tensorflow-2-0-b1ec28bde96f) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F004-batch-norm\u002Fbatch-normalization.ipynb)\n* [早停法实践](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fa-practical-introduction-to-early-stopping-in-machine-learning-550ac88bc8fd) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F005-early-stopping\u002Fearly-stopping.ipynb)\n* [学习率调度实践](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Flearning-rate-schedule-in-practice-an-example-with-keras-and-tensorflow-2-0-2f48b2888a0c) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F006-learning-rate-schedules\u002Flearning-rate-schedules.ipynb)\n* [Keras回调实践](https:\u002F\u002Fmedium.com\u002F@bindiatwork\u002Fa-practical-introduction-to-keras-callbacks-in-tensorflow-2-705d0c584966) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F007-keras-callback\u002Fkeras-callbacks.ipynb)\n* [Keras自定义回调](https:\u002F\u002Fbindichen.medium.com\u002Fbuilding-custom-callbacks-with-keras-and-tensorflow-2-85e1b79915a3) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F008-keras-custom-callback\u002Fkeras-custom-callback.ipynb)\n* [深度学习中7种流行的激活函数](https:\u002F\u002Fbindichen.medium.com\u002F7-popular-activation-functions-you-should-know-in-deep-learning-and-how-to-use-them-with-keras-and-27b4d838dfe6) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F010-popular-activation-functions\u002Fpopular-activation-functions.ipynb)\n* [为什么在深度学习中使用ReLU以及最佳实践](https:\u002F\u002Ftowardsdatascience.com\u002Fwhy-rectified-linear-unit-relu-in-deep-learning-and-the-best-practice-to-use-it-with-tensorflow-e9880933b7ef) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](tensorflow2\u002F011-relu\u002Frelu-and-best-practice.ipynb)\n\n### PyTorch\n\n待定\n\n## Scikit-Learn与通用机器学习\n\n* [网格搜索、随机搜索和贝叶斯搜索的实用介绍](https:\u002F\u002Fbindichen.medium.com\u002Fa-practical-introduction-to-grid-search-random-search-and-bayes-search-d5580b1d941d) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](traditional-machine-learning\u002F005-grid-search-vs-random-search-vs-bayes-search\u002Fgridsearch-vs-randomsearch-vs-bayessearch.ipynb)\n* [9种回归算法的实用介绍](https:\u002F\u002Fbindichen.medium.com\u002Fa-practical-introduction-to-9-regression-algorithms-389057f86eb9) | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](traditional-machine-learning\u002F001-regression-algorithms\u002Fregression-algorithms.ipynb)\n* 机器学习中你应该了解的训练-测试集划分与交叉验证（待定） | [![在GitHub上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-Notebook-orange?logo=Github)](traditional-machine-learning\u002F006-train-test-split-and-cross-validation\u002Ftrain-test-and-cross-validation.ipynb)","# Machine Learning 快速上手指南\n\n本仓库是 Bindi Chen 在 Medium 博客上发布的机器学习实战教程合集，涵盖数据清洗、分析、可视化及主流深度学习框架（TensorFlow, PyTorch, Scikit-Learn）的应用技巧。以下内容基于仓库结构整理，助你快速开始学习。\n\n## 环境准备\n\n在开始之前，请确保你的开发环境满足以下要求：\n\n*   **操作系统**：Windows, macOS 或 Linux\n*   **Python 版本**：推荐 Python 3.8 或更高版本\n*   **核心依赖**：\n    *   `pandas` (数据分析)\n    *   `numpy` (数值计算)\n    *   `matplotlib` \u002F `seaborn` (数据可视化)\n    *   `scikit-learn` (传统机器学习)\n    *   `tensorflow` 或 `pytorch` (深度学习，按需安装)\n    *   `jupyter notebook` 或 `jupyter lab` (运行示例代码)\n*   **网络环境**：部分教程涉及网页爬虫 (`web scraping`) 和加载外部数据，需确保网络通畅。\n\n## 安装步骤\n\n推荐使用 `conda` 或 `virtualenv` 创建独立的虚拟环境，以避免依赖冲突。\n\n### 方案一：使用 Conda (推荐)\n\n如果你已安装 Anaconda 或 Miniconda，可以使用以下命令创建环境并安装基础库。国内用户建议使用清华源加速下载。\n\n```bash\n# 创建名为 ml-env 的虚拟环境，指定 Python 版本\nconda create -n ml-env python=3.9 -y\n\n# 激活环境\nconda activate ml-env\n\n# 配置清华镜像源 (可选，加速下载)\nconda config --add channels https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002Fanaconda\u002Fpkgs\u002Fmain\u002F\nconda config --set show_channel_urls yes\n\n# 安装核心数据科学库\nconda install pandas numpy matplotlib seaborn scikit-learn jupyter -y\n\n# 按需安装深度学习框架 (二选一或全选)\n# conda install tensorflow\n# conda install pytorch torchvision torchaudio -c pytorch\n```\n\n### 方案二：使用 Pip + Virtualenv\n\n```bash\n# 创建虚拟环境\npython -m venv ml-env\n\n# 激活环境\n# Windows:\nml-env\\Scripts\\activate\n# macOS\u002FLinux:\nsource ml-env\u002Fbin\u002Factivate\n\n# 升级 pip 并使用国内镜像安装依赖\npip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple pandas numpy matplotlib seaborn scikit-learn jupyter\n\n# 按需安装深度学习框架\n# pip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple tensorflow\n# pip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple torch torchvision\n```\n\n### 将环境添加到 Jupyter Notebook\n\n安装完成后，若要在 Jupyter 中使用该特定环境，需执行以下命令：\n\n```bash\n# Conda 环境\nconda install ipykernel -y\npython -m ipykernel install --user --name=ml-env --display-name \"Python (ml-env)\"\n\n# Virtualenv 环境\npip install ipykernel\npython -m ipykernel install --user --name=ml-env --display-name \"Python (ml-env)\"\n```\n\n## 基本使用\n\n本仓库的核心内容是位于 `data-analysis` 等目录下的 Jupyter Notebook (`.ipynb`) 文件。每个文件对应一个具体的技术点（如 Pandas 技巧、数据清洗方法等）。\n\n### 1. 启动 Jupyter Notebook\n\n在终端中激活环境并启动服务：\n\n```bash\nconda activate ml-env  # 或 source ml-env\u002Fbin\u002Factivate\njupyter notebook\n```\n\n### 2. 运行示例代码\n\n在浏览器打开的 Jupyter 界面中，导航至仓库对应的文件夹（例如 `data-analysis\u002F006-pandas-read_csv\u002F`），点击 `.ipynb` 文件即可打开。\n\n以下是一个基于仓库中 **Pandas 数据读取技巧** 的最简使用示例，演示如何高效读取 CSV 并解析日期列：\n\n```python\nimport pandas as pd\n\n# 模拟数据内容 (实际使用时请替换为真实文件路径)\n# 教程重点：使用 parse_dates 参数直接解析日期，无需后续转换\ndata = \"\"\"date,sales,region\n2023-01-01,100,North\n2023-01-02,150,South\n2023-01-03,200,East\"\"\"\n\n# 将字符串保存为临时 CSV 文件用于演示\nwith open('sample_data.csv', 'w') as f:\n    f.write(data)\n\n# 【核心技巧】一步完成读取与日期解析\n# 对应仓库教程：4 tricks to parse date columns with Pandas read_csv()\ndf = pd.read_csv('sample_data.csv', parse_dates=['date'])\n\n# 验证日期列类型\nprint(df.dtypes)\nprint(\"\\n前几行数据：\")\nprint(df.head())\n\n# 清理演示文件\nimport os\nos.remove('sample_data.csv')\n```\n\n### 3. 探索更多主题\n\n你可以根据需求浏览仓库中的不同模块：\n\n*   **数据预处理**：查看 `data-analysis` 目录下的 `one-hot-encoding`, `pandas-datetime`, `pandas-drop` 等笔记。\n*   **数据合并与重塑**：参考 `pandas-merge`, `pandas-melt`, `pandas-pivot` 等示例。\n*   **深度学习**：进入 `tensorflow` 或 `pytorch` 目录查找相关模型构建与训练笔记。\n*   **Google Colab 用户**：参考 `General Setup` 中的教程，学习如何将外部数据加载到 Colab 环境中。","某电商数据分析师需要快速从多个竞品网站抓取价格数据，清洗并构建预测模型以制定动态定价策略。\n\n### 没有 machine-learning 时\n- 面对杂乱的 HTML 表格和嵌套 JSON 数据，手动编写解析代码耗时且容易出错，缺乏统一的 `read_html` 或 `json_normalize` 技巧参考。\n- 处理缺失值和日期格式转换时，需反复搜索零散文档，导致数据预处理阶段占据整个项目 70% 的时间。\n- 搭建深度学习环境时，因不熟悉 Virtualenv 或 Conda 与 Jupyter 的集成配置，常陷入依赖冲突的调试困境。\n- 特征工程阶段，对独热编码（One-Hot Encoding）等基础操作理解不深，只能凭直觉尝试，影响模型初始效果。\n\n### 使用 machine-learning 后\n- 直接复用项目中成熟的 Pandas 实战笔记，利用 `read_html` 和 `json_normalize` 技巧，几分钟内即可将非结构化网页数据转化为标准 DataFrame。\n- 参照数据预处理章节的标准化流程，快速应用 `value_counts` 分析分布、使用 `get_dummies` 完成编码，将清洗效率提升数倍。\n- 遵循 General Setup 中的环境搭建指南，一键配置好隔离的虚拟环境并无缝接入 Jupyter，立即开始模型训练而无须担忧报错。\n- 基于 Scikit-Learn 和 TensorFlow\u002FPyTorch 的实操案例，迅速构建并优化价格预测模型，将业务洞察转化为可落地的算法方案。\n\nmachine-learning 通过提供全栈式的实战代码库，将数据科学家从繁琐的环境配置与基础语法查询中解放出来，使其能专注于核心业务逻辑与模型创新。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FBindiChen_machine-learning_6087dd11.png","BindiChen","Bindi Chen","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FBindiChen_493938bc.png","I write code",null,"https:\u002F\u002Fgithub.com\u002FBindiChen",[83,87],{"name":84,"color":85,"percentage":86},"Jupyter Notebook","#DA5B0B",100,{"name":88,"color":89,"percentage":90},"Python","#3572A5",0,707,473,"2026-03-31T06:37:44",1,"未说明",{"notes":97,"python":95,"dependencies":98},"该项目主要是一个机器学习教程集合，包含大量 Pandas、TensorFlow、PyTorch 和 Scikit-Learn 的示例代码。README 建议使用 virtualenv 或 conda 创建虚拟环境，并提到了 Google Colab 的使用场景。具体的硬件和版本需求取决于用户运行的具体笔记本示例（如深度学习部分可能需要 GPU），但 README 本身未列出统一的硬性指标。",[99,100,101,102,103,104],"pandas","numpy","tensorflow","pytorch","scikit-learn","jupyter notebook",[51,13],[67,107,108,109],"deep-learning","data-analysis","pandas-python","2026-03-27T02:49:30.150509","2026-04-06T05:17:01.420810",[],[]]