[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-microsoft--ML-For-Beginners":3,"tool-microsoft--ML-For-Beginners":60},[4,17,27,35,43,52],{"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":10,"last_commit_at":49,"category_tags":50,"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",[15,14,13,26,51],"其他",{"id":53,"name":54,"github_repo":55,"description_zh":56,"stars":57,"difficulty_score":10,"last_commit_at":58,"category_tags":59,"status":16},519,"PaddleOCR","PaddlePaddle\u002FPaddleOCR","PaddleOCR 是一款基于百度飞桨框架开发的高性能开源光学字符识别工具包。它的核心能力是将图片、PDF 等文档中的文字提取出来，转换成计算机可读取的结构化数据，让机器真正“看懂”图文内容。\n\n面对海量纸质或电子文档，PaddleOCR 解决了人工录入效率低、数字化成本高的问题。尤其在人工智能领域，它扮演着连接图像与大型语言模型（LLM）的桥梁角色，能将视觉信息直接转化为文本输入，助力智能问答、文档分析等应用场景落地。\n\nPaddleOCR 适合开发者、算法研究人员以及有文档自动化需求的普通用户。其技术优势十分明显：不仅支持全球 100 多种语言的识别，还能在 Windows、Linux、macOS 等多个系统上运行，并灵活适配 CPU、GPU、NPU 等各类硬件。作为一个轻量级且社区活跃的开源项目，PaddleOCR 既能满足快速集成的需求，也能支撑前沿的视觉语言研究，是处理文字识别任务的理想选择。",74913,"2026-04-05T10:44:17",[26,14,13,51],{"id":61,"github_repo":62,"name":63,"description_en":64,"description_zh":65,"ai_summary_zh":65,"readme_en":66,"readme_zh":67,"quickstart_zh":68,"use_case_zh":69,"hero_image_url":70,"owner_login":71,"owner_name":72,"owner_avatar_url":73,"owner_bio":74,"owner_company":75,"owner_location":75,"owner_email":76,"owner_twitter":77,"owner_website":78,"owner_url":79,"languages":80,"stars":105,"forks":106,"last_commit_at":107,"license":108,"difficulty_score":23,"env_os":109,"env_gpu":110,"env_ram":110,"env_deps":111,"category_tags":117,"github_topics":122,"view_count":134,"oss_zip_url":75,"oss_zip_packed_at":75,"status":16,"created_at":135,"updated_at":136,"faqs":137,"releases":167},2268,"microsoft\u002FML-For-Beginners","ML-For-Beginners","12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all","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。","[![GitHub license](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fmicrosoft\u002FML-For-Beginners.svg)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fblob\u002Fmaster\u002FLICENSE)\n[![GitHub contributors](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcontributors\u002Fmicrosoft\u002FML-For-Beginners.svg)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fgraphs\u002Fcontributors\u002F)\n[![GitHub issues](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fissues\u002Fmicrosoft\u002FML-For-Beginners.svg)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fissues\u002F)\n[![GitHub pull-requests](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fissues-pr\u002Fmicrosoft\u002FML-For-Beginners.svg)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fpulls\u002F)\n[![PRs Welcome](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg?style=flat-square)](http:\u002F\u002Fmakeapullrequest.com)\n\n[![GitHub watchers](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fwatchers\u002Fmicrosoft\u002FML-For-Beginners.svg?style=social&label=Watch)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fwatchers\u002F)\n[![GitHub forks](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fforks\u002Fmicrosoft\u002FML-For-Beginners.svg?style=social&label=Fork)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fnetwork\u002F)\n[![GitHub stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fmicrosoft\u002FML-For-Beginners.svg?style=social&label=Star)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fstargazers\u002F)\n\n### 🌐 Multi-Language Support\n\n#### Supported via GitHub Action (Automated & Always Up-to-Date)\n\n\u003C!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->\n[Arabic](.\u002Ftranslations\u002Far\u002FREADME.md) | [Bengali](.\u002Ftranslations\u002Fbn\u002FREADME.md) | [Bulgarian](.\u002Ftranslations\u002Fbg\u002FREADME.md) | [Burmese (Myanmar)](.\u002Ftranslations\u002Fmy\u002FREADME.md) | [Chinese (Simplified)](.\u002Ftranslations\u002Fzh-CN\u002FREADME.md) | [Chinese (Traditional, Hong Kong)](.\u002Ftranslations\u002Fzh-HK\u002FREADME.md) | [Chinese (Traditional, Macau)](.\u002Ftranslations\u002Fzh-MO\u002FREADME.md) | [Chinese (Traditional, Taiwan)](.\u002Ftranslations\u002Fzh-TW\u002FREADME.md) | [Croatian](.\u002Ftranslations\u002Fhr\u002FREADME.md) | [Czech](.\u002Ftranslations\u002Fcs\u002FREADME.md) | [Danish](.\u002Ftranslations\u002Fda\u002FREADME.md) | [Dutch](.\u002Ftranslations\u002Fnl\u002FREADME.md) | [Estonian](.\u002Ftranslations\u002Fet\u002FREADME.md) | [Finnish](.\u002Ftranslations\u002Ffi\u002FREADME.md) | [French](.\u002Ftranslations\u002Ffr\u002FREADME.md) | [German](.\u002Ftranslations\u002Fde\u002FREADME.md) | [Greek](.\u002Ftranslations\u002Fel\u002FREADME.md) | [Hebrew](.\u002Ftranslations\u002Fhe\u002FREADME.md) | [Hindi](.\u002Ftranslations\u002Fhi\u002FREADME.md) | [Hungarian](.\u002Ftranslations\u002Fhu\u002FREADME.md) | [Indonesian](.\u002Ftranslations\u002Fid\u002FREADME.md) | [Italian](.\u002Ftranslations\u002Fit\u002FREADME.md) | [Japanese](.\u002Ftranslations\u002Fja\u002FREADME.md) | [Kannada](.\u002Ftranslations\u002Fkn\u002FREADME.md) | [Korean](.\u002Ftranslations\u002Fko\u002FREADME.md) | [Lithuanian](.\u002Ftranslations\u002Flt\u002FREADME.md) | [Malay](.\u002Ftranslations\u002Fms\u002FREADME.md) | [Malayalam](.\u002Ftranslations\u002Fml\u002FREADME.md) | [Marathi](.\u002Ftranslations\u002Fmr\u002FREADME.md) | [Nepali](.\u002Ftranslations\u002Fne\u002FREADME.md) | [Nigerian Pidgin](.\u002Ftranslations\u002Fpcm\u002FREADME.md) | [Norwegian](.\u002Ftranslations\u002Fno\u002FREADME.md) | [Persian (Farsi)](.\u002Ftranslations\u002Ffa\u002FREADME.md) | [Polish](.\u002Ftranslations\u002Fpl\u002FREADME.md) | [Portuguese (Brazil)](.\u002Ftranslations\u002Fpt-BR\u002FREADME.md) | [Portuguese (Portugal)](.\u002Ftranslations\u002Fpt-PT\u002FREADME.md) | [Punjabi (Gurmukhi)](.\u002Ftranslations\u002Fpa\u002FREADME.md) | [Romanian](.\u002Ftranslations\u002Fro\u002FREADME.md) | [Russian](.\u002Ftranslations\u002Fru\u002FREADME.md) | [Serbian (Cyrillic)](.\u002Ftranslations\u002Fsr\u002FREADME.md) | [Slovak](.\u002Ftranslations\u002Fsk\u002FREADME.md) | [Slovenian](.\u002Ftranslations\u002Fsl\u002FREADME.md) | [Spanish](.\u002Ftranslations\u002Fes\u002FREADME.md) | [Swahili](.\u002Ftranslations\u002Fsw\u002FREADME.md) | [Swedish](.\u002Ftranslations\u002Fsv\u002FREADME.md) | [Tagalog (Filipino)](.\u002Ftranslations\u002Ftl\u002FREADME.md) | [Tamil](.\u002Ftranslations\u002Fta\u002FREADME.md) | [Telugu](.\u002Ftranslations\u002Fte\u002FREADME.md) | [Thai](.\u002Ftranslations\u002Fth\u002FREADME.md) | [Turkish](.\u002Ftranslations\u002Ftr\u002FREADME.md) | [Ukrainian](.\u002Ftranslations\u002Fuk\u002FREADME.md) | [Urdu](.\u002Ftranslations\u002Fur\u002FREADME.md) | [Vietnamese](.\u002Ftranslations\u002Fvi\u002FREADME.md)\n\n> **Prefer to Clone Locally?**\n>\n> This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout:\n>\n> **Bash \u002F macOS \u002F Linux:**\n> ```bash\n> git clone --filter=blob:none --sparse https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners.git\n> cd ML-For-Beginners\n> git sparse-checkout set --no-cone '\u002F*' '!translations' '!translated_images'\n> ```\n>\n> **CMD (Windows):**\n> ```cmd\n> git clone --filter=blob:none --sparse https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners.git\n> cd ML-For-Beginners\n> git sparse-checkout set --no-cone \"\u002F*\" \"!translations\" \"!translated_images\"\n> ```\n>\n> This gives you everything you need to complete the course with a much faster download.\n\u003C!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->\n\n#### Join Our Community\n\n[![Microsoft Foundry Discord](https:\u002F\u002Fdcbadge.limes.pink\u002Fapi\u002Fserver\u002FnTYy5BXMWG)](https:\u002F\u002Fdiscord.gg\u002FnTYy5BXMWG)\n\nWe have a Discord learn with AI series ongoing, learn more and join us at [Learn with AI Series](https:\u002F\u002Faka.ms\u002Flearnwithai\u002Fdiscord) from 18 - 30 September, 2025. You will get tips and tricks of using GitHub Copilot for Data Science.\n\n![Learn with AI series](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmicrosoft_ML-For-Beginners_readme_2de21431c87b.png)\n\n# Machine Learning for Beginners - A Curriculum\n\n> 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍\n\nCloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called **classic machine learning**, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our [AI for Beginners' curriculum](https:\u002F\u002Faka.ms\u002Fai4beginners). Pair these lessons with our ['Data Science for Beginners' curriculum](https:\u002F\u002Faka.ms\u002Fds4beginners), as well!\n\nTravel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.\n\n**✍️ Hearty thanks to our authors** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd\n\n**🎨 Thanks as well to our illustrators** Tomomi Imura, Dasani Madipalli, and Jen Looper\n\n**🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers, and content contributors**, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal\n\n**🤩 Extra gratitude to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!**\n\n# Getting Started\n\nFollow these steps:\n1. **Fork the Repository**: Click on the \"Fork\" button at the top-right corner of this page.\n2. **Clone the Repository**:   `git clone https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners.git`\n\n> [find all additional resources for this course in our Microsoft Learn collection](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fcollections\u002Fqrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)\n\n> 🔧 **Need help?** Check our [Troubleshooting Guide](TROUBLESHOOTING.md) for solutions to common issues with installation, setup, and running lessons.\n\n\n**[Students](https:\u002F\u002Faka.ms\u002Fstudent-page)**, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:\n\n- Start with a pre-lecture quiz.\n- Read the lecture and complete the activities, pausing and reflecting at each knowledge check.\n- Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the `\u002Fsolution` folders in each project-oriented lesson.\n- Take the post-lecture quiz.\n- Complete the challenge.\n- Complete the assignment.\n- After completing a lesson group, visit the [Discussion Board](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fdiscussions) and \"learn out loud\" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.\n\n> For further study, we recommend following these [Microsoft Learn](https:\u002F\u002Fdocs.microsoft.com\u002Fen-us\u002Fusers\u002Fjenlooper-2911\u002Fcollections\u002Fk7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modules and learning paths.\n\n**Teachers**, we have [included some suggestions](for-teachers.md) on how to use this curriculum.\n\n---\n\n## Video walkthroughs\n\nSome of the lessons are available as short form video. You can find all these in-line in the lessons, or on the [ML for Beginners playlist on the Microsoft Developer YouTube channel](https:\u002F\u002Faka.ms\u002Fml-beginners-videos) by clicking the image below.\n\n[![ML for beginners banner](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmicrosoft_ML-For-Beginners_readme_2ee5851fdd14.png)](https:\u002F\u002Faka.ms\u002Fml-beginners-videos)\n\n---\n\n## Meet the Team\n\n[![Promo video](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmicrosoft_ML-For-Beginners_readme_f3dead8f5c74.gif)](https:\u002F\u002Fyoutu.be\u002FTj1XWrDSYJU)\n\n**Gif by** [Mohit Jaisal](https:\u002F\u002Flinkedin.com\u002Fin\u002Fmohitjaisal)\n\n> 🎥 Click the image above for a video about the project and the folks who created it!\n\n---\n\n## Pedagogy\n\nWe have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on **project-based** and that it includes **frequent quizzes**. In addition, this curriculum has a common **theme** to give it cohesion.\n\nBy ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12-week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.\n\n> Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translations](translations), and [Troubleshooting](TROUBLESHOOTING.md) guidelines. We welcome your constructive feedback!\n\n## Each lesson includes\n\n- optional sketchnote\n- optional supplemental video\n- video walkthrough (some lessons only)\n- [pre-lecture warmup quiz](https:\u002F\u002Fff-quizzes.netlify.app\u002Fen\u002Fml\u002F)\n- written lesson\n- for project-based lessons, step-by-step guides on how to build the project\n- knowledge checks\n- a challenge\n- supplemental reading\n- assignment\n- [post-lecture quiz](https:\u002F\u002Fff-quizzes.netlify.app\u002Fen\u002Fml\u002F)\n\n> **A note about languages**: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the `\u002Fsolution` folder and look for R lessons. They include an .rmd extension that represents an **R Markdown** file which can be simply defined as an embedding of `code chunks` (of R or other languages) and a `YAML header` (that guides how to format outputs such as PDF) in a `Markdown document`. As such, it serves as an exemplary authoring framework for data science since it allows you to combine your code, its output, and your thoughts by allowing you to write them down in Markdown. Moreover, R Markdown documents can be rendered to output formats such as PDF, HTML, or Word.\n\n> **A note about quizzes**: All quizzes are contained in [Quiz App folder](.\u002Fquiz-app\u002F), for 52 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder to locally host or deploy to Azure.\n\n| Lesson Number |                             Topic                              |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                                                              Linked Lesson                                                               |                        Author                        |\n| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |\n|      01       |                Introduction to machine learning                |      [Introduction](1-Introduction\u002FREADME.md)       | Learn the basic concepts behind machine learning                                                                                |                                             [Lesson](1-Introduction\u002F1-intro-to-ML\u002FREADME.md)                                             |                       Muhammad                       |\n|      02       |                The History of machine learning                 |      [Introduction](1-Introduction\u002FREADME.md)       | Learn the history underlying this field                                                                                         |                                            [Lesson](1-Introduction\u002F2-history-of-ML\u002FREADME.md)                                            |                     Jen and Amy                      |\n|      03       |                 Fairness and machine learning                  |      [Introduction](1-Introduction\u002FREADME.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |                                              [Lesson](1-Introduction\u002F3-fairness\u002FREADME.md)                                               |                        Tomomi                        |\n|      04       |                Techniques for machine learning                 |      [Introduction](1-Introduction\u002FREADME.md)       | What techniques do ML researchers use to build ML models?                                                                       |                                          [Lesson](1-Introduction\u002F4-techniques-of-ML\u002FREADME.md)                                           |                    Chris and Jen                     |\n|      05       |                   Introduction to regression                   |        [Regression](2-Regression\u002FREADME.md)         | Get started with Python and Scikit-learn for regression models                                                                  |         [Python](2-Regression\u002F1-Tools\u002FREADME.md) • [R](2-Regression\u002F1-Tools\u002Fsolution\u002FR\u002Flesson_1.html)         |      Jen • Eric Wanjau       |\n|      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression\u002FREADME.md)         | Visualize and clean data in preparation for ML                                                                                  |          [Python](2-Regression\u002F2-Data\u002FREADME.md) • [R](2-Regression\u002F2-Data\u002Fsolution\u002FR\u002Flesson_2.html)          |      Jen • Eric Wanjau       |\n|      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression\u002FREADME.md)         | Build linear and polynomial regression models                                                                                   |        [Python](2-Regression\u002F3-Linear\u002FREADME.md) • [R](2-Regression\u002F3-Linear\u002Fsolution\u002FR\u002Flesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |\n|      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression\u002FREADME.md)         | Build a logistic regression model                                                                                               |     [Python](2-Regression\u002F4-Logistic\u002FREADME.md) • [R](2-Regression\u002F4-Logistic\u002Fsolution\u002FR\u002Flesson_4.html)      |      Jen • Eric Wanjau       |\n|      09       |                          A Web App 🔌                          |           [Web App](3-Web-App\u002FREADME.md)            | Build a web app to use your trained model                                                                                       |                                                 [Python](3-Web-App\u002F1-Web-App\u002FREADME.md)                                                  |                         Jen                          |\n|      10       |                 Introduction to classification                 |    [Classification](4-Classification\u002FREADME.md)     | Clean, prep, and visualize your data; introduction to classification                                                            | [Python](4-Classification\u002F1-Introduction\u002FREADME.md) • [R](4-Classification\u002F1-Introduction\u002Fsolution\u002FR\u002Flesson_10.html)  | Jen and Cassie • Eric Wanjau |\n|      11       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification\u002FREADME.md)     | Introduction to classifiers                                                                                                     | [Python](4-Classification\u002F2-Classifiers-1\u002FREADME.md) • [R](4-Classification\u002F2-Classifiers-1\u002Fsolution\u002FR\u002Flesson_11.html) | Jen and Cassie • Eric Wanjau |\n|      12       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification\u002FREADME.md)     | More classifiers                                                                                                                | [Python](4-Classification\u002F3-Classifiers-2\u002FREADME.md) • [R](4-Classification\u002F3-Classifiers-2\u002Fsolution\u002FR\u002Flesson_12.html) | Jen and Cassie • Eric Wanjau |\n|      13       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification\u002FREADME.md)     | Build a recommender web app using your model                                                                                    |                                              [Python](4-Classification\u002F4-Applied\u002FREADME.md)                                              |                         Jen                          |\n|      14       |                   Introduction to clustering                   |        [Clustering](5-Clustering\u002FREADME.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |         [Python](5-Clustering\u002F1-Visualize\u002FREADME.md) • [R](5-Clustering\u002F1-Visualize\u002Fsolution\u002FR\u002Flesson_14.html)         |      Jen • Eric Wanjau       |\n|      15       |              Exploring Nigerian Musical Tastes 🎧              |        [Clustering](5-Clustering\u002FREADME.md)         | Explore the K-Means clustering method                                                                                           |           [Python](5-Clustering\u002F2-K-Means\u002FREADME.md) • [R](5-Clustering\u002F2-K-Means\u002Fsolution\u002FR\u002Flesson_15.html)           |      Jen • Eric Wanjau       |\n|      16       |        Introduction to natural language processing ☕️         |   [Natural language processing](6-NLP\u002FREADME.md)    | Learn the basics about NLP by building a simple bot                                                                             |                                             [Python](6-NLP\u002F1-Introduction-to-NLP\u002FREADME.md)                                              |                       Stephen                        |\n|      17       |                      Common NLP Tasks ☕️                      |   [Natural language processing](6-NLP\u002FREADME.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |                                                    [Python](6-NLP\u002F2-Tasks\u002FREADME.md)                                                     |                       Stephen                        |\n|      18       |             Translation and sentiment analysis ♥️              |   [Natural language processing](6-NLP\u002FREADME.md)    | Translation and sentiment analysis with Jane Austen                                                                             |                                            [Python](6-NLP\u002F3-Translation-Sentiment\u002FREADME.md)                                             |                       Stephen                        |\n|      19       |                  Romantic hotels of Europe ♥️                  |   [Natural language processing](6-NLP\u002FREADME.md)    | Sentiment analysis with hotel reviews 1                                                                                         |                                               [Python](6-NLP\u002F4-Hotel-Reviews-1\u002FREADME.md)                                                |                       Stephen                        |\n|      20       |                  Romantic hotels of Europe ♥️                  |   [Natural language processing](6-NLP\u002FREADME.md)    | Sentiment analysis with hotel reviews 2                                                                                         |                                               [Python](6-NLP\u002F5-Hotel-Reviews-2\u002FREADME.md)                                                |                       Stephen                        |\n|      21       |            Introduction to time series forecasting             |        [Time series](7-TimeSeries\u002FREADME.md)        | Introduction to time series forecasting                                                                                         |                                             [Python](7-TimeSeries\u002F1-Introduction\u002FREADME.md)                                              |                      Francesca                       |\n|      22       | ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA |        [Time series](7-TimeSeries\u002FREADME.md)        | Time series forecasting with ARIMA                                                                                              |                                                 [Python](7-TimeSeries\u002F2-ARIMA\u002FREADME.md)                                                 |                      Francesca                       |\n|      23       |  ⚡️ World Power Usage ⚡️ - time series forecasting with SVR  |        [Time series](7-TimeSeries\u002FREADME.md)        | Time series forecasting with Support Vector Regressor                                                                           |                                                  [Python](7-TimeSeries\u002F3-SVR\u002FREADME.md)                                                  |                       Anirban                        |\n|      24       |             Introduction to reinforcement learning             | [Reinforcement learning](8-Reinforcement\u002FREADME.md) | Introduction to reinforcement learning with Q-Learning                                                                          |                                             [Python](8-Reinforcement\u002F1-QLearning\u002FREADME.md)                                              |                        Dmitry                        |\n|      25       |                 Help Peter avoid the wolf! 🐺                  | [Reinforcement learning](8-Reinforcement\u002FREADME.md) | Reinforcement learning Gym                                                                                                      |                                                [Python](8-Reinforcement\u002F2-Gym\u002FREADME.md)                                                 |                        Dmitry                        |\n|  Postscript   |            Real-World ML scenarios and applications            |      [ML in the Wild](9-Real-World\u002FREADME.md)       | Interesting and revealing real-world applications of classical ML                                                               |                                             [Lesson](9-Real-World\u002F1-Applications\u002FREADME.md)                                              |                         Team                         |\n|  Postscript   |            Model Debugging in ML using RAI dashboard          |      [ML in the Wild](9-Real-World\u002FREADME.md)       | Model Debugging in Machine Learning using Responsible AI dashboard components                                                              |                                             [Lesson](9-Real-World\u002F2-Debugging-ML-Models\u002FREADME.md)                                              |                         Ruth Yakubu                       |\n\n> [find all additional resources for this course in our Microsoft Learn collection](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fcollections\u002Fqrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)\n\n## Offline access\n\nYou can run this documentation offline by using [Docsify](https:\u002F\u002Fdocsify.js.org\u002F#\u002F). Fork this repo, [install Docsify](https:\u002F\u002Fdocsify.js.org\u002F#\u002Fquickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.\n\n## PDFs\n\nFind a pdf of the curriculum with links [here](https:\u002F\u002Fmicrosoft.github.io\u002FML-For-Beginners\u002Fpdf\u002Freadme.pdf).\n\n\n## 🎒 Other Courses \n\nOur team produces other courses! Check out:\n\n\u003C!-- CO-OP TRANSLATOR OTHER COURSES START -->\n### LangChain\n[![LangChain4j for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https:\u002F\u002Faka.ms\u002Flangchain4j-for-beginners)\n[![LangChain.js for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https:\u002F\u002Faka.ms\u002Flangchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)\n[![LangChain for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLangChain%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Flangchain-for-beginners?WT.mc_id=m365-94501-dwahlin)\n---\n\n### Azure \u002F Edge \u002F MCP \u002F Agents\n[![AZD for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAZD-for-beginners?WT.mc_id=academic-105485-koreyst)\n[![Edge AI for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEdge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fedgeai-for-beginners?WT.mc_id=academic-105485-koreyst)\n[![MCP for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmcp-for-beginners?WT.mc_id=academic-105485-koreyst)\n[![AI Agents for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)\n\n---\n \n### Generative AI Series\n[![Generative AI for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGenerative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgenerative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)\n[![Generative AI (.NET)](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGenerative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FGenerative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)\n[![Generative AI (Java)](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGenerative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgenerative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)\n[![Generative AI (JavaScript)](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGenerative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgenerative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)\n\n---\n \n### Core Learning\n[![ML for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https:\u002F\u002Faka.ms\u002Fml-beginners?WT.mc_id=academic-105485-koreyst)\n[![Data Science for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FData%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https:\u002F\u002Faka.ms\u002Fdatascience-beginners?WT.mc_id=academic-105485-koreyst)\n[![AI for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https:\u002F\u002Faka.ms\u002Fai-beginners?WT.mc_id=academic-105485-koreyst)\n[![Cybersecurity for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FSecurity-101?WT.mc_id=academic-96948-sayoung)\n[![Web Dev for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWeb%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https:\u002F\u002Faka.ms\u002Fwebdev-beginners?WT.mc_id=academic-105485-koreyst)\n[![IoT for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https:\u002F\u002Faka.ms\u002Fiot-beginners?WT.mc_id=academic-105485-koreyst)\n[![XR Development for Beginners](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FXR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fxr-development-for-beginners?WT.mc_id=academic-105485-koreyst)\n\n---\n \n### Copilot Series\n[![Copilot for AI Paired Programming](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCopilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https:\u002F\u002Faka.ms\u002FGitHubCopilotAI?WT.mc_id=academic-105485-koreyst)\n[![Copilot for C#\u002F.NET](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCopilot%20for%20C%23\u002F.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)\n[![Copilot Adventure](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCopilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FCopilotAdventures?WT.mc_id=academic-105485-koreyst)\n\u003C!-- CO-OP TRANSLATOR OTHER COURSES END -->\n\n## Getting Help\n\nIf you get stuck or have any questions about building AI apps. Join fellow learners and experienced developers in discussions about MCP. It's a supportive community where questions are welcome and knowledge is shared freely.\n\n[![Microsoft Foundry Discord](https:\u002F\u002Fdcbadge.limes.pink\u002Fapi\u002Fserver\u002FnTYy5BXMWG)](https:\u002F\u002Fdiscord.gg\u002FnTYy5BXMWG)\n\nIf you have product feedback or errors while building visit:\n\n[![Microsoft Foundry Developer Forum](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https:\u002F\u002Faka.ms\u002Ffoundry\u002Fforum)\n## Additional Learning Tips\n\n- Review notebooks after each lesson for better understanding.\n- Practice implementing algorithms on your own.\n- Explore real-world datasets using learned concepts.\n","[![GitHub 许可证](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fmicrosoft\u002FML-For-Beginners.svg)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fblob\u002Fmaster\u002FLICENSE)\n[![GitHub 贡献者](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcontributors\u002Fmicrosoft\u002FML-For-Beginners.svg)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fgraphs\u002Fcontributors\u002F)\n[![GitHub 问题](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fissues\u002Fmicrosoft\u002FML-For-Beginners.svg)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fissues\u002F)\n[![GitHub 拉取请求](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fissues-pr\u002Fmicrosoft\u002FML-For-Beginners.svg)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fpulls\u002F)\n[![欢迎提交 PR](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg?style=flat-square)](http:\u002F\u002Fmakeapullrequest.com)\n\n[![GitHub 监视者](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fwatchers\u002Fmicrosoft\u002FML-For-Beginners.svg?style=social&label=Watch)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fwatchers\u002F)\n[![GitHub 复刻](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fforks\u002Fmicrosoft\u002FML-For-Beginners.svg?style=social&label=Fork)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fnetwork\u002F)\n[![GitHub 星标](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fmicrosoft\u002FML-For-Beginners.svg?style=social&label=Star)](https:\u002F\u002FGitHub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fstargazers\u002F)\n\n### 🌐 多语言支持\n\n#### 通过 GitHub Action 实现（自动化且始终保持最新）\n\n\u003C!-- 合作翻译语言表格开始 -->\n[阿拉伯语](.\u002Ftranslations\u002Far\u002FREADME.md) | [孟加拉语](.\u002Ftranslations\u002Fbn\u002FREADME.md) | [保加利亚语](.\u002Ftranslations\u002Fbg\u002FREADME.md) | [缅甸语](.\u002Ftranslations\u002Fmy\u002FREADME.md) | [简体中文](.\u002Ftranslations\u002Fzh-CN\u002FREADME.md) | [繁体中文（香港）](.\u002Ftranslations\u002Fzh-HK\u002FREADME.md) | [繁体中文（澳门）](.\u002Ftranslations\u002Fzh-MO\u002FREADME.md) | [繁体中文（台湾）](.\u002Ftranslations\u002Fzh-TW\u002FREADME.md) | [克罗地亚语](.\u002Ftranslations\u002Fhr\u002FREADME.md) | [捷克语](.\u002Ftranslations\u002Fcs\u002FREADME.md) | [丹麦语](.\u002Ftranslations\u002Fda\u002FREADME.md) | [荷兰语](.\u002Ftranslations\u002Fnl\u002FREADME.md) | [爱沙尼亚语](.\u002Ftranslations\u002Fet\u002FREADME.md) | [芬兰语](.\u002Ftranslations\u002Ffi\u002FREADME.md) | [法语](.\u002Ftranslations\u002Ffr\u002FREADME.md) | [德语](.\u002Ftranslations\u002Fde\u002FREADME.md) | [希腊语](.\u002Ftranslations\u002Fel\u002FREADME.md) | [希伯来语](.\u002Ftranslations\u002Fhe\u002FREADME.md) | [印地语](.\u002Ftranslations\u002Fhi\u002FREADME.md) | [匈牙利语](.\u002Ftranslations\u002Fhu\u002FREADME.md) | [印尼语](.\u002Ftranslations\u002Fid\u002FREADME.md) | [意大利语](.\u002Ftranslations\u002Fit\u002FREADME.md) | [日语](.\u002Ftranslations\u002Fja\u002FREADME.md) | [坎纳达语](.\u002Ftranslations\u002Fkn\u002FREADME.md) | [韩语](.\u002Ftranslations\u002Fko\u002FREADME.md) | [立陶宛语](.\u002Ftranslations\u002Flt\u002FREADME.md) | [马来语](.\u002Ftranslations\u002Fms\u002FREADME.md) | [马拉雅拉姆语](.\u002Ftranslations\u002Fml\u002FREADME.md) | [马拉地语](.\u002Ftranslations\u002Fmr\u002FREADME.md) | [尼泊尔语](.\u002Ftranslations\u002Fne\u002FREADME.md) | [尼日利亚皮钦语](.\u002Ftranslations\u002Fpcm\u002FREADME.md) | [挪威语](.\u002Ftranslations\u002Fno\u002FREADME.md) | [波斯语（法尔西语）](.\u002Ftranslations\u002Ffa\u002FREADME.md) | [波兰语](.\u002Ftranslations\u002Fpl\u002FREADME.md) | [巴西葡萄牙语](.\u002Ftranslations\u002Fpt-BR\u002FREADME.md) | [葡萄牙语（葡萄牙）](.\u002Ftranslations\u002Fpt-PT\u002FREADME.md) | [旁遮普语（古木基文）](.\u002Ftranslations\u002Fpa\u002FREADME.md) | [罗马尼亚语](.\u002Ftranslations\u002Fro\u002FREADME.md) | [俄语](.\u002Ftranslations\u002Fru\u002FREADME.md) | [塞尔维亚语（西里尔字母）](.\u002Ftranslations\u002Fsr\u002FREADME.md) | [斯洛伐克语](.\u002Ftranslations\u002Fsk\u002FREADME.md) | [斯洛文尼亚语](.\u002Ftranslations\u002Fsl\u002FREADME.md) | [西班牙语](.\u002Ftranslations\u002Fes\u002FREADME.md) | [斯瓦希里语](.\u002Ftranslations\u002Fsw\u002FREADME.md) | [瑞典语](.\u002Ftranslations\u002Fsv\u002FREADME.md) | [塔加路语（菲律宾语）](.\u002Ftranslations\u002Ftl\u002FREADME.md) | [泰米尔语](.\u002Ftranslations\u002Fta\u002FREADME.md) | [泰卢固语](.\u002Ftranslations\u002Fte\u002FREADME.md) | [泰语](.\u002Ftranslations\u002Fth\u002FREADME.md) | [土耳其语](.\u002Ftranslations\u002Ftr\u002FREADME.md) | [乌克兰语](.\u002Ftranslations\u002Fuk\u002FREADME.md) | [乌尔都语](.\u002Ftranslations\u002Fur\u002FREADME.md) | [越南语](.\u002Ftranslations\u002Fvi\u002FREADME.md)\n\n> **更倾向于本地克隆吗？**\n>\n> 此仓库包含50多种语言的翻译，这会显著增加下载大小。若要不包含翻译而克隆，请使用稀疏检出：\n>\n> **Bash \u002F macOS \u002F Linux：**\n> ```bash\n> git clone --filter=blob:none --sparse https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners.git\n> cd ML-For-Beginners\n> git sparse-checkout set --no-cone '\u002F*' '!translations' '!translated_images'\n> ```\n>\n> **CMD（Windows）：**\n> ```cmd\n> git clone --filter=blob:none --sparse https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners.git\n> cd ML-For-Beginners\n> git sparse-checkout set --no-cone \"\u002F*\" \"!translations\" \"!translated_images\"\n> ```\n>\n> 这样可以让你以更快的速度下载所需内容，从而顺利完成课程。\n\u003C!-- 合作翻译语言表格结束 -->\n\n#### 加入我们的社区\n\n[![Microsoft Foundry Discord](https:\u002F\u002Fdcbadge.limes.pink\u002Fapi\u002Fserver\u002FnTYy5BXMWG)](https:\u002F\u002Fdiscord.gg\u002FnTYy5BXMWG)\n\n我们正在举办“与 AI 共学”系列线上活动，更多信息及参与方式请访问 [Learn with AI Series](https:\u002F\u002Faka.ms\u002Flearnwithai\u002Fdiscord)，活动时间为2025年9月18日至30日。你将学习如何利用 GitHub Copilot 进行数据科学的相关技巧与窍门。\n\n![与 AI 共学系列](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmicrosoft_ML-For-Beginners_readme_2de21431c87b.png)\n\n# 面向初学者的机器学习课程\n\n> 🌍 穿越全球，用世界文化探索机器学习 🌍\n\n微软云倡导团队很高兴推出为期12周、共26课时的**机器学习**课程。在本课程中，你将学习所谓的**经典机器学习**，主要使用 Scikit-learn 库，而不涉及深度学习；深度学习的内容则涵盖在我们的[面向初学者的人工智能课程](https:\u002F\u002Faka.ms\u002Fai4beginners)中。同时，也建议将这些课程与我们的[面向初学者的数据科学课程](https:\u002F\u002Faka.ms\u002Fds4beginners)结合学习！\n\n让我们一起环游世界，将这些经典技术应用于来自世界各地的数据。每节课都包含课前和课后测验、完成课程的书面说明、解决方案、作业等丰富内容。我们采用项目式教学法，在实践中学习并构建技能，这是一种已被证明能够有效巩固新知识的方法。\n\n**✍️ 衷心感谢我们的作者**：Jen Looper、Stephen Howell、Francesca Lazzeri、Tomomi Imura、Cassie Breviu、Dmitry Soshnikov、Chris Noring、Anirban Mukherjee、Ornella Altunyan、Ruth Yakubu 和 Amy Boyd\n\n**🎨 同时感谢我们的插画师**：Tomomi Imura、Dasani Madipalli 和 Jen Looper\n\n**🙏 特别感谢** **微软学生大使们的作者、审稿人以及内容贡献者**，尤其是 Rishit Dagli、Muhammad Sakib Khan Inan、Rohan Raj、Alexandru Petrescu、Abhishek Jaiswal、Nawrin Tabassum、Ioan Samuila 和 Snigdha Agarwal\n\n**🤩 更加感激微软学生大使 Eric Wanjau、Jasleen Sondhi 和 Vidushi Gupta 为我们的 R 课程所做出的贡献！**\n\n# 入门\n\n请按照以下步骤操作：\n1. **复刻仓库**：点击此页面右上角的“Fork”按钮。\n2. **克隆仓库**：`git clone https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners.git`\n\n> [在我们的 Microsoft Learn 课程集中查找本课程的所有附加资源](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fcollections\u002Fqrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)\n\n> 🔧 **需要帮助吗？** 请查看我们的[故障排除指南](TROUBLESHOOTING.md)，以获取有关安装、设置和运行课程时常见问题的解决方案。\n\n\n**[学生](https:\u002F\u002Faka.ms\u002Fstudent-page)**，要使用本课程，请将整个仓库复刻到您自己的 GitHub 账户，并单独或与小组一起完成练习：\n- 首先进行课前测验。\n- 阅读课程内容并完成各项活动，在每次知识检测处暂停并反思。\n- 尽量通过理解课程内容来创建项目，而不是直接运行提供的解决方案代码；不过，每个项目式课程的 `\u002Fsolution` 文件夹中都提供了这些代码。\n- 完成课后测验。\n- 完成挑战任务。\n- 完成作业。\n- 每完成一个课程组后，请访问[讨论区](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fdiscussions)，并通过填写相应的 PAT 评分标准“大声学习”。PAT 是一种进度评估工具，即您填写的评分标准，用于进一步促进学习。您也可以对其他 PAT 进行回应，以便我们共同学习。\n\n> 如需进一步学习，我们建议您跟随这些 [Microsoft Learn](https:\u002F\u002Fdocs.microsoft.com\u002Fen-us\u002Fusers\u002Fjenlooper-2911\u002Fcollections\u002Fk7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) 模块和学习路径。\n\n**教师们**，我们已在[教师指南](for-teachers.md)中提供了一些关于如何使用本课程的建议。\n\n---\n\n## 视频教程\n\n部分课程有短视频版本。您可以在课程正文中找到这些视频，也可以通过点击下方图片访问 [微软开发者 YouTube 频道上的“机器学习入门”播放列表](https:\u002F\u002Faka.ms\u002Fml-beginners-videos)。\n\n[![机器学习入门横幅](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmicrosoft_ML-For-Beginners_readme_2ee5851fdd14.png)](https:\u002F\u002Faka.ms\u002Fml-beginners-videos)\n\n---\n\n## 团队介绍\n\n[![宣传视频](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmicrosoft_ML-For-Beginners_readme_f3dead8f5c74.gif)](https:\u002F\u002Fyoutu.be\u002FTj1XWrDSYJU)\n\n**动图由** [Mohit Jaisal](https:\u002F\u002Flinkedin.com\u002Fin\u002Fmohitjaisal) 制作\n\n> 🎥 点击上方图片，观看关于该项目及其创建者的视频！\n\n---\n\n## 教学法\n\n我们在构建本课程时选择了两项教学原则：确保课程是**基于项目的**实践型课程，并且包含**频繁的测验**。此外，本课程还具有一个贯穿始终的**主题**，以增强整体连贯性。\n\n通过使内容与项目紧密结合，可以使学习过程对学生更具吸引力，并有助于加深对概念的理解和记忆。此外，在课堂开始前进行一次低风险的测验，可以帮助学生明确学习目标；而课后再次进行测验，则能进一步巩固所学内容。本课程设计灵活有趣，您可以选择完整学习或分阶段学习。项目难度由浅入深，到为期 12 周的学习周期结束时会逐渐复杂化。本课程还附有机器学习在现实世界中的应用简介，可作为额外学分或讨论的基础。\n\n> 请参阅我们的[行为准则](CODE_OF_CONDUCT.md)、[贡献指南](CONTRIBUTING.md)、[翻译文件](translations)以及[故障排除指南](TROUBLESHOOTING.md)。我们欢迎您的建设性反馈！\n\n## 每个课程包括\n- 可选的速写笔记\n- 可选的补充视频\n- 视频讲解（仅部分课程）\n- [课前热身测验](https:\u002F\u002Fff-quizzes.netlify.app\u002Fen\u002Fml\u002F)\n- 文字版课程内容\n- 对于项目式课程，提供逐步指导以完成项目\n- 知识检测\n- 挑战任务\n- 补充阅读材料\n- 作业\n- [课后测验](https:\u002F\u002Fff-quizzes.netlify.app\u002Fen\u002Fml\u002F)\n\n> **关于语言的说明**：这些课程主要以 Python 编写，但也有很多课程提供 R 语言版本。要完成 R 语言课程，请前往 `\u002Fsolution` 文件夹，查找 R 语言课程。这些课程带有 `.rmd` 扩展名，代表一种 **R Markdown** 文件，可以简单定义为在 `Markdown 文档` 中嵌入 `代码块`（使用 R 或其他语言）以及 `YAML 头部`（用于指导如何格式化 PDF 等输出），从而实现代码、输出结果和文本说明的有机结合。因此，它是一种出色的数据科学创作框架，允许您将代码、其输出和思考以 Markdown 格式记录下来。此外，R Markdown 文档还可以渲染为 PDF、HTML 或 Word 等格式的输出。\n\n> **关于测验的说明**：所有测验都位于 [Quiz App 文件夹](.\u002Fquiz-app\u002F)中，共 52 个测验，每个测验包含三道题目。这些测验链接在课程中，但 Quiz App 也可以在本地运行；请按照 `quiz-app` 文件夹中的说明，在本地托管或部署到 Azure 上。\n\n| 课时编号 |                             主题                              |                   课程分组                   | 学习目标                                                                                                             |                                                              关联课程                                                               |                        作者                        |\n| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |\n|      01       |                机器学习导论                |      [导论](1-Introduction\u002FREADME.md)       | 学习机器学习背后的基本概念                                                                                |                                             [课程](1-Introduction\u002F1-intro-to-ML\u002FREADME.md)                                             |                       Muhammad                       |\n|      02       |                机器学习的历史                 |      [导论](1-Introduction\u002FREADME.md)       | 了解该领域的历史渊源                                                                                         |                                            [课程](1-Introduction\u002F2-history-of-ML\u002FREADME.md)                                            |                     Jen 和 Amy                      |\n|      03       |                 公平性与机器学习                  |      [导论](1-Introduction\u002FREADME.md)       | 在构建和应用机器学习模型时，学生应考虑哪些重要的公平性哲学问题？                                         |                                              [课程](1-Introduction\u002F3-fairness\u002FREADME.md)                                               |                        Tomomi                        |\n|      04       |                机器学习的技术                 |      [导论](1-Introduction\u002FREADME.md)       | 机器学习研究人员使用哪些技术来构建机器学习模型？                                                           |                                          [课程](1-Introduction\u002F4-techniques-of-ML\u002FREADME.md)                                           |                    Chris 和 Jen                     |\n|      05       |                   回归分析导论                   |        [回归分析](2-Regression\u002FREADME.md)         | 开始使用 Python 和 Scikit-learn 进行回归模型的实践                                                            |         [Python](2-Regression\u002F1-Tools\u002FREADME.md) • [R](2-Regression\u002F1-Tools\u002Fsolution\u002FR\u002Flesson_1.html)         |      Jen • Eric Wanjau       |\n|      06       |                北美南瓜价格 🎃                |        [回归分析](2-Regression\u002FREADME.md)         | 为机器学习做准备，对数据进行可视化和清洗                                                                     |          [Python](2-Regression\u002F2-Data\u002FREADME.md) • [R](2-Regression\u002F2-Data\u002Fsolution\u002FR\u002Flesson_2.html)          |      Jen • Eric Wanjau       |\n|      07       |                北美南瓜价格 🎃                |        [回归分析](2-Regression\u002FREADME.md)         | 构建线性回归和多项式回归模型                                                                                   |        [Python](2-Regression\u002F3-Linear\u002FREADME.md) • [R](2-Regression\u002F3-Linear\u002Fsolution\u002FR\u002Flesson_3.html)        |      Jen 和 Dmitry • Eric Wanjau       |\n|      08       |                北美南瓜价格 🎃                |        [回归分析](2-Regression\u002FREADME.md)         | 构建逻辑回归模型                                                                                               |     [Python](2-Regression\u002F4-Logistic\u002FREADME.md) • [R](2-Regression\u002F4-Logistic\u002Fsolution\u002FR\u002Flesson_4.html)      |      Jen • Eric Wanjau       |\n|      09       |                          网页应用 🔌                          |           [网页应用](3-Web-App\u002FREADME.md)            | 构建一个使用你训练好的模型的网页应用                                                                         |                                                 [Python](3-Web-App\u002F1-Web-App\u002FREADME.md)                                                  |                         Jen                          |\n|      10       |                 分类问题导论                 |    [分类](4-Classification\u002FREADME.md)     | 清洗、准备并可视化你的数据；介绍分类问题                                                                      | [Python](4-Classification\u002F1-Introduction\u002FREADME.md) • [R](4-Classification\u002F1-Introduction\u002Fsolution\u002FR\u002Flesson_10.html)  | Jen 和 Cassie • Eric Wanjau |\n|      11       |             美味的亚洲和印度美食 🍜             |    [分类](4-Classification\u002FREADME.md)     | 介绍分类器                                                                                                     | [Python](4-Classification\u002F2-Classifiers-1\u002FREADME.md) • [R](4-Classification\u002F2-Classifiers-1\u002Fsolution\u002FR\u002Flesson_11.html) | Jen 和 Cassie • Eric Wanjau |\n|      12       |             美味的亚洲和印度美食 🍜             |    [分类](4-Classification\u002FREADME.md)     | 更多的分类器                                                                                                                | [Python](4-Classification\u002F3-Classifiers-2\u002FREADME.md) • [R](4-Classification\u002F3-Classifiers-2\u002Fsolution\u002FR\u002Flesson_12.html) | Jen 和 Cassie • Eric Wanjau |\n|      13       |             美味的亚洲和印度美食 🍜             |    [分类](4-Classification\u002FREADME.md)     | 使用你的模型构建一个推荐网页应用                                                                              |                                              [Python](4-Classification\u002F4-Applied\u002FREADME.md)                                              |                         Jen                          |\n|      14       |                   聚类分析导论                   |        [聚类](5-Clustering\u002FREADME.md)         | 清洗、准备并可视化你的数据；介绍聚类分析                                                                     |         [Python](5-Clustering\u002F1-Visualize\u002FREADME.md) • [R](5-Clustering\u002F1-Visualize\u002Fsolution\u002FR\u002Flesson_14.html)         |      Jen • Eric Wanjau       |\n|      15       |              探索尼日利亚音乐品味 🎧              |        [聚类](5-Clustering\u002FREADME.md)         | 探索 K-Means 聚类方法                                                                                           |           [Python](5-Clustering\u002F2-K-Means\u002FREADME.md) • [R](5-Clustering\u002F2-K-Means\u002Fsolution\u002FR\u002Flesson_15.html)           |      Jen • Eric Wanjau       |\n|      16       |        自然语言处理导论 ☕️         |   [自然语言处理](6-NLP\u002FREADME.md)    | 通过构建一个简单的聊天机器人来学习自然语言处理的基础知识                                                     |                                             [Python](6-NLP\u002F1-Introduction-to-NLP\u002FREADME.md)                                              |                       Stephen                        |\n|      17       |                      常见的NLP任务 ☕️                      |   [自然语言处理](6-NLP\u002FREADME.md)    | 通过理解处理语言结构时常见的任务，加深对自然语言处理的理解                                                    |                                                    [Python](6-NLP\u002F2-Tasks\u002FREADME.md)                                                     |                       Stephen                        |\n|      18       |             翻译与情感分析 ♥️              |   [自然语言处理](6-NLP\u002FREADME.md)    | 使用简·奥斯汀的作品进行翻译和情感分析                                                                         |                                            [Python](6-NLP\u002F3-Translation-Sentiment\u002FREADME.md)                                             |                       Stephen                        |\n|      19       |                  欧洲浪漫酒店 ♥️                  |   [自然语言处理](6-NLP\u002FREADME.md)    | 使用酒店评论进行情感分析 1                                                                                     |                                               [Python](6-NLP\u002F4-Hotel-Reviews-1\u002FREADME.md)                                                |                       Stephen                        |\n|      20       |                  欧洲浪漫酒店 ♥️                  |   [自然语言处理](6-NLP\u002FREADME.md)    | 使用酒店评论进行情感分析 2                                                                                     |                                               [Python](6-NLP\u002F5-Hotel-Reviews-2\u002FREADME.md)                                                |                       Stephen                        |\n|      21       |            时间序列预测导论             |        [时间序列](7-TimeSeries\u002FREADME.md)        | 介绍时间序列预测                                                                                         |                                             [Python](7-TimeSeries\u002F1-Introduction\u002FREADME.md)                                              |                      Francesca                       |\n|      22       | ⚡️ 世界电力使用 ⚡️ - 使用 ARIMA 进行时间序列预测 |        [时间序列](7-TimeSeries\u002FREADME.md)        | 使用 ARIMA 进行时间序列预测                                                                                   |                                                 [Python](7-TimeSeries\u002F2-ARIMA\u002FREADME.md)                                                 |                      Francesca                       |\n|      23       |  ⚡️ 世界电力使用 ⚡️ - 使用 SVR 进行时间序列预测  |        [时间序列](7-TimeSeries\u002FREADME.md)        | 使用支持向量回归机进行时间序列预测                                                                           |                                                  [Python](7-TimeSeries\u002F3-SVR\u002FREADME.md)                                                  |                       Anirban                        |\n|      24       |             强化学习导论             | [强化学习](8-Reinforcement\u002FREADME.md) | 使用 Q-Learning 介绍强化学习                                                                                  |                                             [Python](8-Reinforcement\u002F1-QLearning\u002FREADME.md)                                              |                        Dmitry                        |\n|      25       |                 帮助彼得避开狼！ 🐺                  | [强化学习](8-Reinforcement\u002FREADME.md) | 强化学习 Gym                                                                                                      |                                                [Python](8-Reinforcement\u002F2-Gym\u002FREADME.md)                                                 |                        Dmitry                        |\n|  后记   |            真实世界中的机器学习场景与应用            |      [真实世界的机器学习](9-Real-World\u002FREADME.md)       | 经典机器学习在现实世界中一些有趣且富有启发性的应用                                                               |                                             [课程](9-Real-World\u002F1-Applications\u002FREADME.md)                                              |                         团队                         |\n|  后记   |            使用 RAI 控制台调试机器学习模型          |      [真实世界的机器学习](9-Real-World\u002FREADME.md)       | 使用 Responsible AI 控制台组件在机器学习中进行模型调试                                                              |                                             [课程](9-Real-World\u002F2-Debugging-ML-Models\u002FREADME.md)                                              |                         Ruth Yakubu                       |\n\n> [在我们的 Microsoft Learn 课程合集中找到本课程的所有附加资源](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fcollections\u002Fqrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)\n\n\n\n## 离线访问\n\n你可以使用 [Docsify](https:\u002F\u002Fdocsify.js.org\u002F#\u002F) 在离线状态下运行这份文档。先 Fork 这个仓库，在本地机器上[安装 Docsify](https:\u002F\u002Fdocsify.js.org\u002F#\u002Fquickstart)，然后在该仓库的根目录下输入 `docsify serve`。网站将会在你的本地主机的 3000 端口上启动：`localhost:3000`。\n\n## PDF 文件\n\n你可以在[这里](https:\u002F\u002Fmicrosoft.github.io\u002FML-For-Beginners\u002Fpdf\u002Freadme.pdf)找到带有链接的课程 PDF 版本。\n\n\n## 🎒 其他课程\n\n我们的团队还制作了其他课程！请查看：\n\n\u003C!-- CO-OP TRANSLATOR OTHER COURSES START -->\n### LangChain\n[![LangChain4j 入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https:\u002F\u002Faka.ms\u002Flangchain4j-for-beginners)\n[![LangChain.js 入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https:\u002F\u002Faka.ms\u002Flangchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)\n[![LangChain 入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLangChain%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Flangchain-for-beginners?WT.mc_id=m365-94501-dwahlin)\n---\n\n### Azure \u002F Edge \u002F MCP \u002F Agents\n[![AZD 入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAZD-for-beginners?WT.mc_id=academic-105485-koreyst)\n[![Edge AI 入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEdge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fedgeai-for-beginners?WT.mc_id=academic-105485-koreyst)\n[![MCP 入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmcp-for-beginners?WT.mc_id=academic-105485-koreyst)\n[![AI 代理入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)\n\n---\n \n### 生成式 AI 系列\n[![生成式 AI 入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGenerative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgenerative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)\n[![生成式 AI (.NET)](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGenerative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FGenerative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)\n[![生成式 AI (Java)](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGenerative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgenerative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)\n[![生成式 AI (JavaScript)](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGenerative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgenerative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)\n\n---\n \n### 核心学习\n[![机器学习入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https:\u002F\u002Faka.ms\u002Fml-beginners?WT.mc_id=academic-105485-koreyst)\n[![数据科学入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FData%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https:\u002F\u002Faka.ms\u002Fdatascience-beginners?WT.mc_id=academic-105485-koreyst)\n[![人工智能入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https:\u002F\u002Faka.ms\u002Fai-beginners?WT.mc_id=academic-105485-koreyst)\n[![网络安全入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FSecurity-101?WT.mc_id=academic-96948-sayoung)\n[![Web 开发入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWeb%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https:\u002F\u002Faka.ms\u002Fwebdev-beginners?WT.mc_id=academic-105485-koreyst)\n[![物联网入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https:\u002F\u002Faka.ms\u002Fiot-beginners?WT.mc_id=academic-105485-koreyst)\n[![XR 开发入门](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FXR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fxr-development-for-beginners?WT.mc_id=academic-105485-koreyst)\n\n---\n \n### Copilot 系列\n[![Copilot 用于 AI 配对编程](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCopilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https:\u002F\u002Faka.ms\u002FGitHubCopilotAI?WT.mc_id=academic-105485-koreyst)\n[![Copilot 用于 C#\u002F.NET](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCopilot%20for%20C%23\u002F.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)\n[![Copilot 冒险](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCopilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FCopilotAdventures?WT.mc_id=academic-105485-koreyst)\n\u003C!-- CO-OP TRANSLATOR OTHER COURSES END -->\n\n## 获取帮助\n\n如果你在构建 AI 应用时遇到困难或有任何问题，欢迎加入 MCP 的讨论区，与志同道合的学习者和经验丰富的开发者一起交流。这是一个充满支持的社区，欢迎大家提问并自由分享知识。\n\n[![Microsoft Foundry Discord](https:\u002F\u002Fdcbadge.limes.pink\u002Fapi\u002Fserver\u002FnTYy5BXMWG)](https:\u002F\u002Fdiscord.gg\u002FnTYy5BXMWG)\n\n如果你在开发过程中遇到产品反馈或错误，请访问：\n\n[![Microsoft Foundry 开发者论坛](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https:\u002F\u002Faka.ms\u002Ffoundry\u002Fforum)\n## 其他学习建议\n\n- 每节课后复习笔记本以加深理解。\n- 自行练习实现算法。\n- 使用所学概念探索真实世界的数据集。","# ML-For-Beginners 快速上手指南\n\n**ML-For-Beginners** 是微软推出的一套为期 12 周、包含 26 节课的机器学习入门课程。本课程专注于“经典机器学习”（非深度学习），主要使用 Python (Scikit-learn) 和 R 语言，通过项目驱动的方式帮助初学者掌握核心概念。\n\n## 环境准备\n\n在开始之前，请确保您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*   **包管理工具**: `pip` (通常随 Python 安装) 或 `conda`\n*   **代码编辑器**: 推荐 [Visual Studio Code](https:\u002F\u002Fcode.visualstudio.com\u002F)\n*   **Git**: 用于克隆仓库\n*   **可选 (R 语言用户)**: 如需完成 R 语言课程，需安装 [R](https:\u002F\u002Fcran.r-project.org\u002F) 和 [RStudio](https:\u002F\u002Fposit.co\u002Fdownload\u002Frstudio-desktop\u002F)，并支持 `.rmd` (R Markdown) 文件渲染。\n\n> **国内加速建议**：\n> *   **Git 克隆加速**：如果直接克隆 GitHub 仓库速度慢，可使用镜像源（如 `https:\u002F\u002Fghproxy.net\u002Fhttps:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners.git`）或在终端配置代理。\n> *   **Python 包下载**：建议使用清华或阿里镜像源安装依赖。\n>     ```bash\n>     pip install -r requirements.txt -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n>     ```\n\n## 安装步骤\n\n### 1. 获取代码\n\n由于该仓库包含 50 多种语言的翻译文件，体积较大。建议普通用户（特别是仅需中文或英文内容的用户）使用 **Sparse Checkout** 模式克隆，仅下载核心课程内容，跳过翻译文件夹以加快下载速度。\n\n**Linux \u002F macOS \u002F Git Bash (Windows):**\n\n```bash\ngit clone --filter=blob:none --sparse https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners.git\ncd ML-For-Beginners\ngit sparse-checkout set --no-cone '\u002F*' '!translations' '!translated_images'\n```\n\n**Windows CMD:**\n\n```cmd\ngit clone --filter=blob:none --sparse https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners.git\ncd ML-For-Beginners\ngit sparse-checkout set --no-cone \"\u002F*\" \"!translations\" \"!translated_images\"\n```\n\n*如果您需要完整的本地多语言翻译文件，可直接运行标准克隆命令：*\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners.git\n```\n\n### 2. 安装依赖\n\n进入课程目录，安装所需的 Python 库。\n\n```bash\ncd ML-For-Beginners\npip install -r requirements.txt\n```\n\n*(注：部分特定章节可能有独立的 `requirements.txt`，请根据具体课程指引安装)*\n\n### 3. 验证安装\n\n确保 `jupyter` 或相关库已正确安装，您可以尝试启动一个示例 Notebook（如果环境中已包含示例数据）或直接进入下一步开始学习。\n\n## 基本使用\n\n本课程采用“课前测验 -> 理论学习 -> 项目实践 -> 课后测验 -> 作业挑战”的流程。\n\n### 学习路径示例\n\n1.  **选择课程**：\n    进入仓库后，浏览根目录下的课程列表（通常在 README 表格中或通过文件夹结构查看）。课程按周次和主题分类（如：回归、分类、聚类等）。\n\n2.  **开始第一课**：\n    以第一周的某节课为例，进入对应的文件夹（例如 `1-Introduction\u002F1-intro-to-ML`）。\n\n3.  **执行学习流程**：\n    *   **课前测验**：访问链接或运行本地 Quiz App 进行热身。\n    *   **阅读教程**：阅读文件夹内的 `.md` 文档或打开 `.ipynb` (Jupyter Notebook) 文件。\n    *   **运行代码**：\n        在 Jupyter Notebook 或 VS Code 中打开课程提供的示例代码单元格，逐步运行并观察结果。\n        ```python\n        # 示例：在 Notebook 中导入常用库\n        import pandas as pd\n        import numpy as np\n        from sklearn.model_selection import train_test_split\n        # 继续按照课程指导加载数据并训练模型...\n        ```\n    *   **动手实践**：尝试不直接运行答案代码，而是根据教程指导自己编写项目逻辑。\n    *   **查看解决方案**：如果遇到瓶颈，可参考 `\u002Fsolution` 文件夹中的完整代码（如果是 R 语言课程，请查找 `.rmd` 文件）。\n\n4.  **完成作业与挑战**：\n    每节课末尾都有 Assignment（作业）和 Challenge（挑战），完成后建议在 GitHub Discussion 区分享您的学习心得 (PAT)。\n\n### 运行本地测验应用 (可选)\n\n所有测验题目存储在 `quiz-app` 文件夹中。您可以将其部署到本地进行测试：\n\n```bash\ncd quiz-app\n# 按照该文件夹内的 README 指示安装依赖并启动服务\nnpm install\nnpm start\n```\n\n现在，您已经准备好开始您的机器学习之旅了！建议配合微软官方的 [Learn 集合资源](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fcollections\u002Fqrqzamz1nn2wx3) 进行深入学习。","某高校计算机系讲师计划为零基础学生开设一门为期三个月的机器学习入门课，急需一套结构严谨且配套资源完善的教学大纲。\n\n### 没有 ML-For-Beginners 时\n- 课程内容碎片化严重，讲师需从 Stack Overflow、各类博客和文档中拼凑知识点，难以保证知识体系的连贯性。\n- 缺乏统一的进度规划与考核标准，布置作业和测验需完全手动出题，耗费大量备课时间且质量参差不齐。\n- 面对班级中英语薄弱的学生，找不到官方认可的多语言教材，导致部分学生因语言障碍无法理解核心概念。\n- 理论讲解与实际代码脱节，学生往往听懂了公式却不知如何在 Python 环境中落地实现，挫败感强。\n\n### 使用 ML-For-Beginners 后\n- 直接采用其\"12 周、26 节课”的标准化课程路径，内容涵盖从数据清洗到模型部署的完整闭环，教学逻辑清晰严密。\n- 复用内置的 52 个测验题和课后作业方案，不仅大幅减轻出题负担，还确保了考核内容与当周知识点精准匹配。\n- 利用其支持的中、西、法等多种语言翻译版本，让非英语母语学生能无障碍阅读教材，显著提升了课堂参与度。\n- 每节课均提供可运行的 Jupyter Notebook 代码实例，学生能边学边练，迅速将数学原理转化为实际的机器学习模型。\n\nML-For-Beginners 将原本需要数月筹备的课程搭建工作缩短至几天，为教育者提供了一套开箱即用、全球验证的高质量机器学习教学解决方案。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmicrosoft_ML-For-Beginners_2de21431.png","microsoft","Microsoft","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fmicrosoft_4900709c.png","Open source projects and samples from Microsoft",null,"opensource@microsoft.com","OpenAtMicrosoft","https:\u002F\u002Fopensource.microsoft.com","https:\u002F\u002Fgithub.com\u002Fmicrosoft",[81,85,89,93,96,99,102],{"name":82,"color":83,"percentage":84},"Jupyter Notebook","#DA5B0B",81.5,{"name":86,"color":87,"percentage":88},"HTML","#e34c26",18.5,{"name":90,"color":91,"percentage":92},"Python","#3572A5",0,{"name":94,"color":95,"percentage":92},"Vue","#41b883",{"name":97,"color":98,"percentage":92},"JavaScript","#f1e05a",{"name":100,"color":101,"percentage":92},"Dockerfile","#384d54",{"name":103,"color":104,"percentage":92},"CSS","#663399",84991,20480,"2026-04-05T10:45:23","MIT","Linux, macOS, Windows","未说明",{"notes":112,"python":113,"dependencies":114},"这是一个面向初学者的机器学习教学课程，主要使用 Scikit-learn 库，不涉及深度学习。代码示例主要提供 Python 版本，部分课程提供 R 语言版本（.rmd 文件）。如需运行本地测验应用或特定语言环境，请参考项目内的 Troubleshooting Guide 和对应文件夹说明。建议通过 Fork 仓库进行学习，若需减少下载体积可使用稀疏克隆（sparse checkout）排除翻译文件。","未说明 (主要使用 Python，部分课程支持 R)",[115,116],"scikit-learn","R (可选)",[14,118,119,120,15,51,26,13,121],"数据工具","视频","插件","音频",[123,124,125,126,127,128,129,115,130,131,132,133],"ml","data-science","machine-learning","machine-learning-algorithms","machinelearning","python","machinelearning-python","scikit-learn-python","r","education","microsoft-for-beginners",83,"2026-03-27T02:49:30.150509","2026-04-06T01:04:04.469781",[138,143,148,153,158,163],{"id":139,"question_zh":140,"answer_zh":141,"source_url":142},10412,"如何参与课程内容的翻译工作？","您可以参考官方提供的翻译指南（TRANSLATIONS.md）开始工作。目前支持的语言包括西班牙语、中文、日语、韩语、法语、葡萄牙语、印尼语和印地语等。如果您想贡献其他语言（如孟加拉语或卡纳达语），可以直接在相关议题下留言提出，并创建草稿 Pull Request（PR）开始翻译。建议在专门的追踪议题中更新进度，以便协调工作。","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fissues\u002F71",{"id":144,"question_zh":145,"answer_zh":146,"source_url":147},10413,"我想贡献某种特定语言（如印地语或葡萄牙语）的翻译，该如何跟踪进度或认领任务？","每种语言通常有独立的追踪议题（例如印地语见 Issue #401，葡萄牙语见 Issue #148）。您可以在这些议题的评论区查看当前的完成状态清单（通常以复选框形式列出各章节），并留言声明您要负责的具体章节（例如“我正在处理分类基础部分”）。维护者会协助将您的名字标记在对应的任务旁，以避免重复工作。","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fissues\u002F551",{"id":149,"question_zh":150,"answer_zh":151,"source_url":152},10414,"为什么我的翻译议题被关闭了？还能重新开启吗？","如果议题长时间没有活动（no-issue-activity），系统机器人会自动将其关闭以保持仓库整洁。这并不意味着项目被取消。您可以随时重新打开该议题，或者在评论区留言表明您打算继续工作，维护者通常会重新激活它。","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fissues\u002F148",{"id":154,"question_zh":155,"answer_zh":156,"source_url":157},10415,"在学习过程中遇到代码执行问题（如 CSS 无法运行），该怎么办？","如果遇到特定工具（如 Atom Script）不支持某种语言（如 CSS）的情况，这通常是编辑器插件的配置问题，而非课程内容错误。建议您尝试在项目的 Discussion 板块提问以获取更多社区帮助，或者前往相关插件的 GitHub 仓库提交功能请求（Issue）或 Pull Request 来添加支持。","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fissues\u002F144",{"id":159,"question_zh":160,"answer_zh":161,"source_url":162},10416,"课程中的手绘笔记（Sketchnotes）风格需要保持一致吗？不同贡献者的风格不同可以吗？","虽然维护者最初希望风格一致，但也接受不同贡献者的独特风格。如果您发现自己的绘图风格与现有笔记不同，可以在评论中向维护者确认。通常情况下，只要内容准确且清晰，风格的多样性是被允许的。您可以直接留言询问：“我的风格与之前的不同，这样可以吗？”以获得明确许可。","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners\u002Fissues\u002F26",{"id":164,"question_zh":165,"answer_zh":166,"source_url":162},10417,"如何知道哪些模块的手绘笔记已经完成，哪些还需要贡献？","您可以查看关于手绘笔记的请求议题（如 Issue #26），其中列出了所有主要模块的状态清单。已完成的模块会被标记为 [x]，未完成的为 [ ]。您也可以在评论区查看维护者的最新回复，他们会同步哪些部分正在编写中，哪些已经准备就绪可供使用。",[]]