[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-greyhatguy007--Machine-Learning-Specialization-Coursera":3,"tool-greyhatguy007--Machine-Learning-Specialization-Coursera":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",140436,2,"2026-04-05T23:32:43",[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":79,"owner_location":80,"owner_email":79,"owner_twitter":81,"owner_website":82,"owner_url":83,"languages":84,"stars":93,"forks":94,"last_commit_at":95,"license":96,"difficulty_score":97,"env_os":98,"env_gpu":98,"env_ram":98,"env_deps":99,"category_tags":105,"github_topics":106,"view_count":23,"oss_zip_url":79,"oss_zip_packed_at":79,"status":16,"created_at":125,"updated_at":126,"faqs":127,"releases":168},3756,"greyhatguy007\u002FMachine-Learning-Specialization-Coursera","Machine-Learning-Specialization-Coursera"," Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG","Machine-Learning-Specialization-Coursera 是一个专为吴恩达教授在 Coursera 平台上开设的“机器学习专项课程”（2022 版）打造的学习辅助资源库。它系统地整理了该课程全套的习题解答、核心笔记以及可选实验的代码实现，涵盖监督学习中的回归与分类、多变量线性回归等关键章节。\n\n对于许多自学者而言，机器学习课程中的数学推导和编程作业往往是最大的拦路虎。这个资源库恰好解决了这一痛点，它不仅提供了课后练习测验的详细答案，还通过 Jupyter Notebook 形式展示了模型表示、成本函数计算及梯度下降算法等核心实验的完整代码逻辑。学习者可以借此对照自己的解题思路，快速定位知识盲区，从而更透彻地理解算法背后的数学原理与工程实现。\n\n这份资料非常适合正在修读该课程的初学者、希望夯实基础的开发者，以及需要复习经典算法的研究人员使用。其独特的技术亮点在于将抽象的理论知识转化为可运行的 Python 代码，特别是利用 Numpy 进行向量化操作的实验演示，能帮助使用者直观感受如何高效地训练模型。如果你渴望深入掌握机器学习的底层逻辑，而不仅仅是调用现成库，Ma","Machine-Learning-Specialization-Coursera 是一个专为吴恩达教授在 Coursera 平台上开设的“机器学习专项课程”（2022 版）打造的学习辅助资源库。它系统地整理了该课程全套的习题解答、核心笔记以及可选实验的代码实现，涵盖监督学习中的回归与分类、多变量线性回归等关键章节。\n\n对于许多自学者而言，机器学习课程中的数学推导和编程作业往往是最大的拦路虎。这个资源库恰好解决了这一痛点，它不仅提供了课后练习测验的详细答案，还通过 Jupyter Notebook 形式展示了模型表示、成本函数计算及梯度下降算法等核心实验的完整代码逻辑。学习者可以借此对照自己的解题思路，快速定位知识盲区，从而更透彻地理解算法背后的数学原理与工程实现。\n\n这份资料非常适合正在修读该课程的初学者、希望夯实基础的开发者，以及需要复习经典算法的研究人员使用。其独特的技术亮点在于将抽象的理论知识转化为可运行的 Python 代码，特别是利用 Numpy 进行向量化操作的实验演示，能帮助使用者直观感受如何高效地训练模型。如果你渴望深入掌握机器学习的底层逻辑，而不仅仅是调用现成库，Machine-Learning-Specialization-Coursera 将是你不可或缺的学习伴侣。","# Machine Learning Specialization Coursera\n\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fgreyhatguy007_Machine-Learning-Specialization-Coursera_readme_158fc7784f88.png)\n\nContains Solutions and Notes for the [Machine Learning Specialization](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmachine-learning-introduction\u002F?utm_medium=coursera&utm_source=home-page&utm_campaign=mlslaunch2022IN) by Andrew NG on Coursera \n\n**Note : If you would like to have a deeper understanding of the concepts by understanding all the math required, have a look at [Mathematics for Machine Learning and Data Science](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera)**\n\n\u003Chr\u002F>\n\n## Course 1 : [Supervised Machine Learning: Regression and Classification ](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning?specialization=machine-learning-introduction)\n\n- [Week 1](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1)\n\n    - [Practice quiz: Regression](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FPractice%20quiz%20-%20Regression)\n    - [Practice quiz: Supervised vs unsupervised learning](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FPractice%20quiz%20-%20Supervised%20vs%20unsupervised%20learning)\n    - [Practice quiz: Train the model with gradient descent](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FPractice%20quiz%20-%20Train%20the%20model%20with%20gradient%20descent)\n  - [Optional Labs](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FOptional%20Labs)\n    - [Model Representation](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FOptional%20Labs\u002FC1_W1_Lab03_Model_Representation_Soln.ipynb)\n    - [Cost Function](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FOptional%20Labs\u002FC1_W1_Lab04_Cost_function_Soln.ipynb)\n    - [Gradient Descent](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FOptional%20Labs\u002FC1_W1_Lab05_Gradient_Descent_Soln.ipynb)\n\n\u003Cbr\u002F>\n\n- [Week 2](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2) \n\n    - [Practice quiz: Gradient descent in practice](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FPractice%20quiz%20-%20Gradient%20descent%20in%20practice)\n    - [Practice quiz: Multiple linear regression](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FPractice%20quiz%20-%20Multiple%20linear%20regression)\n    - [Optional Labs](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs)\n      - [Numpy Vectorization](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb)\n      - [Multi Variate Regression](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab02_Multiple_Variable_Soln.ipynb)\n      - [Feature Scaling](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb)\n      - [Feature Engineering](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb)\n      - [Sklearn Gradient Descent](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab05_Sklearn_GD_Soln.ipynb)\n      - [Sklearn Normal Method](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab05_Sklearn_GD_Soln.ipynb)\n    - [Programming Assignment](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FC1W2A1)\n      - [Linear Regression](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FC1W2A1\u002FC1_W2_Linear_Regression.ipynb)\n\n\u003Cbr\u002F>\n\n- [Week 3](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3)\n\n    - [Practice quiz: Cost function for logistic regression](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FPractice%20quiz%20-%20Cost%20function%20for%20logistic%20regression)\n    - [Practice quiz: Gradient descent for logistic regression](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FPractice%20quiz%20-%20Gradient%20descent%20for%20logistic%20regression)\n    - [Optional Labs](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs)\n        - [Classification](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab01_Classification_Soln.ipynb)\n        - [Sigmoid Function](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab02_Sigmoid_function_Soln.ipynb)\n        - [Decision Boundary](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab03_Decision_Boundary_Soln.ipynb)\n        - [Logistic Loss](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab04_LogisticLoss_Soln.ipynb)\n        - [Cost Function](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab05_Cost_Function_Soln.ipynb)\n        - [Gradient Descent](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab06_Gradient_Descent_Soln.ipynb)\n        - [Scikit Learn - Logistic Regression](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab07_Scikit_Learn_Soln.ipynb)\n        - [Overfitting](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab08_Overfitting_Soln.ipynb)\n        - [Regularization](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab09_Regularization_Soln.ipynb)\n    - [Programming Assignment](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FC1W3A1)\n      - [Logistic Regression](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FC1W3A1\u002FC1_W3_Logistic_Regression.ipynb)\n\n#### [Certificate Of Completion](https:\u002F\u002Fcoursera.org\u002Fshare\u002F195768f3c1a83e42298d3f61dae99d01)\n\n\u003Cbr\u002F>\n\n## Course 2 : [Advanced Learning Algorithms](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fadvanced-learning-algorithms?specialization=machine-learning-introduction)\n\n- [Week 1](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1)\n    - [Practice quiz: Neural networks intuition](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FPractice%20quiz%20-%20Neural%20networks%20intuition)\n    - [Practice quiz: Neural network model](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FPractice%20quiz%20-%20Neural%20network%20model)\n    - [Practice quiz: TensorFlow implementation](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FPractice%20quiz%20-%20TensorFlow%20implementation)\n    - [Practice quiz : Neural Networks Implementation in Numpy](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FPractice-Quiz-Neural-Networks-Implementation-in-python)\n    - [Optional Labs](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002Foptional-labs)\n      - [Neurons and Layers](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002Foptional-labs\u002FC2_W1_Lab01_Neurons_and_Layers.ipynb)\n      - [Coffee Roasting](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002Foptional-labs\u002FC2_W1_Lab02_CoffeeRoasting_TF.ipynb)\n      - [Coffee Roasting Using Numpy](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002Foptional-labs\u002FC2_W1_Lab02_CoffeeRoasting_TF.ipynb)\n    - [Programming Assignment](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FC2W1A1)\n      - [Neural Networks for Binary Classification](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FC2W1A1\u002FC2_W1_Assignment.ipynb)\n  \n\n  \u003Cbr\u002F>\n\n- [Week 2](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2)\n    - [Practice quiz : Neural Networks Training](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FPractice-Quiz-Neural-Network-Training)\n    - [Practice quiz : Activation Functions](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FPractice-Quiz-Activation-Functions)\n    - [Practice quiz : Multiclass Classification](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FPractice-quiz-Multiclass-Classification)\n    - [Practice quiz : Additional Neural Networks Concepts](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FPractice-Quiz-Additional-Neural-Network-Concepts)\n    - [Optional Labs](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002Foptional-labs)\n        - [RElu](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002Foptional-labs\u002FC2_W2_Relu.ipynb)\n        - [Softmax](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002Foptional-labs\u002FC2_W2_SoftMax.ipynb)\n        - [Multiclass Classification](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002Foptional-labs\u002FC2_W2_Multiclass_TF.ipynb)\n    - [Programming Assignment](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FC2W2A1)\n      - [Neural Networks For Handwritten Digit Recognition - Multiclass](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FC2W2A1\u002FC2_W2_Assignment.ipynb)\n    \n\n\u003Cbr\u002F>\n\n- [Week 3](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3)\n    - [Practice quiz : Advice for Applying Machine Learning](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3\u002FPractice-Quiz-Advice-for-applying-machine-learning)    \n    - [Practice quiz : Bias and Variance](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3\u002Fpractice-quiz-bias-and-variance)\n    - [Practice quiz : Machine Learning Development Process](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3\u002Fpractice-quiz-machine-learning-development-process)\n    - [Programming Assignment](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3\u002FC2W3A1)\n        - [Advice for Applied Machine Learning](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3\u002FC2W3A1\u002FC2_W3_Assignment.ipynb)\n\n\u003Cbr\u002F>\n\n\n- [Week 4](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4)\n    - [Practice quiz : Decision Trees](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4\u002Fpractice-quiz-decision-trees)\n    - [Practice quiz : Decision Trees Learning](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4\u002Fpractice-quiz-decision-tree-learning)\n    - [Practice quiz : Decision Trees Ensembles](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4\u002Fpractice-quiz-tree-ensembles)\n    - [Programming Assignment](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4\u002FC2W4A1)\n        - [Decision Trees](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4\u002FC2W4A1\u002FC2_W4_Decision_Tree_with_Markdown.ipynb)\n\n#### [Certificate of Completion](https:\u002F\u002Fcoursera.org\u002Fshare\u002Fc9a7766b0c6eab27db2e955376d29bf7)        \n\n\u003Cbr\u002F>\n\n## Course 3 : [Unsupervised Learning, Recommenders, Reinforcement Learning](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Funsupervised-learning-recommenders-reinforcement-learning?specialization=machine-learning-introduction)\n\n\u003Cbr\u002F>\n\n- [Week 1](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1)\n    - [Practice quiz : Clustering](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1\u002FPractice%20Quiz%20-%20Clustering)\n    - [Practice quiz : Anomaly Detection](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1\u002FPractice%20Quiz%20-%20Anomaly%20Detection)\n    - [Programming Assignments](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1\u002FC3W1A)\n        - [K means](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1\u002FC3W1A\u002FC3W1A1\u002FC3_W1_KMeans_Assignment.ipynb)\n        - [Anomaly Detection](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1\u002FC3W1A\u002FC3W1A2\u002FC3_W1_Anomaly_Detection.ipynb)\n\n\u003Cbr\u002F>\n\n- [Week 2](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2)\n    - [Practice quiz : Collaborative Filtering](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FPractice%20Quiz%20-%20Collaborative%20Filtering)\n    - [Practice quiz : Recommender systems implementation](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FPractice%20Quiz%20-%20Recommender%20systems%20implementation)\n    - [Practice quiz : Content-based filtering](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FPractice%20Quiz%20-%20Content-based%20filtering)\n    - [Programming Assignments](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FC3W2)\n        - [Collaborative Filtering RecSys](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FC3W2\u002FC3W2A1\u002FC3_W2_Collaborative_RecSys_Assignment.ipynb)\n        - [RecSys using Neural Networks](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FC3W2\u002FC3W2A2\u002FC3_W2_RecSysNN_Assignment.ipynb)\n\n\u003Cbr\u002F>\n\n- [Week 3](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3)\n    - [Practice quiz : Reinforcement learning introduction](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3\u002FPractice%20Quiz%20-%20Reinforcement%20learning%20introduction)\n    - [Practice Quiz : State-action value function](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3\u002FPractice%20Quiz%20-%20State-action%20value%20function)\n    - [Practice Quiz : Continuous state spaces](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3\u002FPractice%20Quiz%20-%20Continuous%20state%20spaces)\n    - [Programming Assignment](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3\u002FC3W3A1)\n        - [Deep Q-Learning - Lunar Lander](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3\u002FC3W3A1\u002FC3_W3_A1_Assignment.ipynb)\n#### [Certificate of Completion](https:\u002F\u002Fcoursera.org\u002Fshare\u002F5bf5ee456b0c806df9b8622067b47ca6)\n\n\n### [Specialization Certificate](https:\u002F\u002Fcoursera.org\u002Fshare\u002Fa15ac6426f90924491a542850700a759)\n\n\u003Cbr\u002F>\n\n\u003Cbr\u002F>\n\n\u003Chr\u002F>\n\n\u003Cdiv align=\"center\">\n\n                        \n### Stargazers over time\n[![Stargazers over time](https:\u002F\u002Fstarchart.cc\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera.svg?variant=adaptive)](https:\u002F\u002Fstarchart.cc\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera)                 \n\n[![Hits](https:\u002F\u002Fhits.seeyoufarm.com\u002Fapi\u002Fcount\u002Fincr\u002Fbadge.svg?url=https%3A%2F%2Fgithub.com%2Fgreyhatguy007%2FMachine-Learning-Specialization-Coursera&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https:\u002F\u002Fhits.seeyoufarm.com)\n\n\u003C\u002Fdiv>\n\n### Course Review :\n\nThis Course is a best place towards becoming a Machine Learning Engineer. Even if you're an expert, many algorithms are covered in depth such as decision trees which may help in further improvement of skills.\n\n**Special thanks to [Professor Andrew Ng](https:\u002F\u002Fwww.andrewng.org\u002F) for structuring and tailoring this Course.**\n\n\u003Cbr\u002F>\n\n\u003Chr\u002F>\n\n#### An insight of what you might be able to accomplish at the end of this specialization :\n\n* \u003Ci>Write an unsupervised learning algorithm to **Land the Lunar Lander** Using Deep Q-Learning\u003C\u002Fi>\n\n    - The Rover was trained to land correctly on the surface, correctly between the flags as indicators after many unsuccessful attempts in learning how to do it.\n    - The final landing after training the agent using appropriate parameters : \n\nhttps:\u002F\u002Fuser-images.githubusercontent.com\u002F77543865\u002F182395635-703ae199-ba79-4940-86eb-23dd90093ab3.mp4\n\n* \u003Ci>Write an algorithm for a **Movie Recommender System**\u003C\u002Fi>\n    \n    - A movie database is collected based on its genre.\n    - A content based filtering and collaborative filtering algorithm is trained and the movie recommender system is implemented.\n    - It gives movie recommendentations based on the movie genre.\n\n![movie_recommendation](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fgreyhatguy007_Machine-Learning-Specialization-Coursera_readme_c2b91c053c66.png)\n\n* \u003Ci> And Much More !! \u003C\u002Fi>\n\n\nConcluding, this is a course which I would recommend everyone to take. Not just because you learn many new stuffs, but also the assignments are real life examples which are *exciting to complete*. \n\n\u003Cbr\u002F>\n\n**Happy Learning :))**\n\n\n \n \n","# 机器学习专项课程 Coursera\n\n\n![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fgreyhatguy007_Machine-Learning-Specialization-Coursera_readme_158fc7784f88.png)\n\n包含 Andrew NG 在 Coursera 上开设的 [机器学习专项课程](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmachine-learning-introduction\u002F?utm_medium=coursera&utm_source=home-page&utm_campaign=mlslaunch2022IN) 的解答与笔记。\n\n**注：如果你想通过理解所有必要的数学知识来更深入地掌握这些概念，请参阅 [机器学习与数据科学中的数学](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera)**\n\n\u003Chr\u002F>\n\n## 课程 1：[监督式机器学习：回归与分类](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning?specialization=machine-learning-introduction)\n\n- [第 1 周](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1)\n\n    - [练习测验：回归](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FPractice%20quiz%20-%20Regression)\n    - [练习测验：监督学习 vs 无监督学习](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FPractice%20quiz%20-%20Supervised%20vs%20unsupervised%20learning)\n    - [练习测验：用梯度下降训练模型](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FPractice%20quiz%20-%20Train%20the%20model%20with%20gradient%20descent)\n  - [可选实验](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FOptional%20Labs)\n    - [模型表示](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FOptional%20Labs\u002FC1_W1_Lab03_Model_Representation_Soln.ipynb)\n    - [损失函数](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FOptional%20Labs\u002FC1_W1_Lab04_Cost_function_Soln.ipynb)\n    - [梯度下降](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek1\u002FOptional%20Labs\u002FC1_W1_Lab05_Gradient_Descent_Soln.ipynb)\n\n\u003Cbr\u002F>\n\n- [第 2 周](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2) \n\n    - [练习测验：梯度下降的实际应用](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FPractice%20quiz%20-%20Gradient%20descent%20in%20practice)\n    - [练习测验：多元线性回归](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FPractice%20quiz%20-%20Multiple%20linear%20regression)\n    - [可选实验](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs)\n      - [Numpy 向量化](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb)\n      - [多元回归](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab02_Multiple_Variable_Soln.ipynb)\n      - [特征缩放](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb)\n      - [特征工程](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb)\n      - [Sklearn 梯度下降](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab05_Sklearn_GD_Soln.ipynb)\n      - [Sklearn 正规方程法](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FOptional%20Labs\u002FC1_W2_Lab05_Sklearn_GD_Soln.ipynb)\n    - [编程作业](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FC1W2A1)\n      - [线性回归](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek2\u002FC1W2A1\u002FC1_W2_Linear_Regression.ipynb)\n\n\u003Cbr\u002F>\n\n- [第 3 周](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3)\n\n- [练习测验：逻辑回归的成本函数](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FPractice%20quiz%20-%20Cost%20function%20for%20logistic%20regression)\n    - [练习测验：逻辑回归的梯度下降](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FPractice%20quiz%20-%20Gradient%20descent%20for%20logistic%20regression)\n    - [可选实验](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs)\n        - [分类](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab01_Classification_Soln.ipynb)\n        - [Sigmoid函数](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab02_Sigmoid_function_Soln.ipynb)\n        - [决策边界](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab03_Decision_Boundary_Soln.ipynb)\n        - [逻辑损失](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab04_LogisticLoss_Soln.ipynb)\n        - [成本函数](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab05_Cost_Function_Soln.ipynb)\n        - [梯度下降](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab06_Gradient_Descent_Soln.ipynb)\n        - [Scikit Learn - 逻辑回归](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab07_Scikit_Learn_Soln.ipynb)\n        - [过拟合](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab08_Overfitting_Soln.ipynb)\n        - [正则化](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FOptional%20Labs\u002FC1_W3_Lab09_Regularization_Soln.ipynb)\n    - [编程作业](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FC1W3A1)\n      - [逻辑回归](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification\u002Fweek3\u002FC1W3A1\u002FC1_W3_Logistic_Regression.ipynb)\n\n#### [结业证书](https:\u002F\u002Fcoursera.org\u002Fshare\u002F195768f3c1a83e42298d3f61dae99d01)\n\n\u003Cbr\u002F>\n\n\n\n## 课程2：[高级学习算法](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fadvanced-learning-algorithms?specialization=machine-learning-introduction)\n\n- [第1周](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1)\n    - [练习测验：神经网络直觉](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FPractice%20quiz%20-%20Neural%20networks%20intuition)\n    - [练习测验：神经网络模型](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FPractice%20quiz%20-%20Neural%20network%20model)\n    - [练习测验：TensorFlow实现](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FPractice%20quiz%20-%20TensorFlow%20implementation)\n    - [练习测验：用Numpy实现神经网络](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FPractice-Quiz-Neural-Networks-Implementation-in-python)\n    - [可选实验](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002Foptional-labs)\n      - [神经元和层](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002Foptional-labs\u002FC2_W1_Lab01_Neurons_and_Layers.ipynb)\n      - [咖啡烘焙](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002Foptional-labs\u002FC2_W1_Lab02_CoffeeRoasting_TF.ipynb)\n      - [使用Numpy进行咖啡烘焙](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002Foptional-labs\u002FC2_W1_Lab02_CoffeeRoasting_TF.ipynb)\n    - [编程作业](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FC2W1A1)\n      - [用于二分类的神经网络](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek1\u002FC2W1A1\u002FC2_W1_Assignment.ipynb)\n  \n\n  \u003Cbr\u002F>\n\n- [第2周](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2)\n    - [练习测验：神经网络训练](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FPractice-Quiz-Neural-Network-Training)\n    - [练习测验：激活函数](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FPractice-Quiz-Activation-Functions)\n    - [练习测验：多分类问题](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FPractice-quiz-Multiclass-Classification)\n    - [练习测验：神经网络的其他概念](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FPractice-Quiz-Additional-Neural-Network-Concepts)\n    - [可选实验](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002Foptional-labs)\n        - [ReLU](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002Foptional-labs\u002FC2_W2_Relu.ipynb)\n        - [Softmax](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002Foptional-labs\u002FC2_W2_SoftMax.ipynb)\n        - [多分类问题](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002Foptional-labs\u002FC2_W2_Multiclass_TF.ipynb)\n    - [编程作业](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FC2W2A1)\n      - [用于手写数字识别的多分类神经网络](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek2\u002FC2W2A1\u002FC2_W2_Assignment.ipynb)\n\n\u003Cbr\u002F>\n\n- [第3周](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3)\n    - [练习测验：机器学习应用建议](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3\u002FPractice-Quiz-Advice-for-applying-machine-learning)    \n    - [练习测验：偏差与方差](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3\u002Fpractice-quiz-bias-and-variance)\n    - [练习测验：机器学习开发流程](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3\u002Fpractice-quiz-machine-learning-development-process)\n    - [编程作业](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3\u002FC2W3A1)\n        - [机器学习应用建议](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek3\u002FC2W3A1\u002FC2_W3_Assignment.ipynb)\n\n\u003Cbr\u002F>\n\n\n- [第4周](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4)\n    - [练习测验：决策树](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4\u002Fpractice-quiz-decision-trees)\n    - [练习测验：决策树学习](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4\u002Fpractice-quiz-decision-tree-learning)\n    - [练习测验：决策树集成](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4\u002Fpractice-quiz-tree-ensembles)\n    - [编程作业](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4\u002FC2W4A1)\n        - [决策树](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC2%20-%20Advanced%20Learning%20Algorithms\u002Fweek4\u002FC2W4A1\u002FC2_W4_Decision_Tree_with_Markdown.ipynb)\n\n#### [结业证书](https:\u002F\u002Fcoursera.org\u002Fshare\u002Fc9a7766b0c6eab27db2e955376d29bf7)        \n\n\u003Cbr\u002F>\n\n## 课程3：无监督学习、推荐系统、强化学习](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Funsupervised-learning-recommenders-reinforcement-learning?specialization=machine-learning-introduction)\n\n\u003Cbr\u002F>\n\n- [第1周](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1)\n    - [练习测验：聚类](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1\u002FPractice%20Quiz%20-%20Clustering)\n    - [练习测验：异常检测](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1\u002FPractice%20Quiz%20-%20Anomaly%20Detection)\n    - [编程作业](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1\u002FC3W1A)\n        - [K均值算法](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1\u002FC3W1A\u002FC3W1A1\u002FC3_W1_KMeans_Assignment.ipynb)\n        - [异常检测](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek1\u002FC3W1A\u002FC3W1A2\u002FC3_W1_Anomaly_Detection.ipynb)\n\n\u003Cbr\u002F>\n\n- [第2周](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2)\n    - [练习测验：协同过滤](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FPractice%20Quiz%20-%20Collaborative%20Filtering)\n    - [练习测验：推荐系统实现](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FPractice%20Quiz%20-%20Recommender%20systems%20implementation)\n    - [练习测验：基于内容的过滤](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FPractice%20Quiz%20-%20Content-based%20filtering)\n    - [编程作业](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FC3W2)\n        - [协同过滤推荐系统](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FC3W2\u002FC3W2A1\u002FC3_W2_Collaborative_RecSys_Assignment.ipynb)\n        - [基于神经网络的推荐系统](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek2\u002FC3W2\u002FC3W2A2\u002FC3_W2_RecSysNN_Assignment.ipynb)\n\n\u003Cbr\u002F>\n\n- [第3周](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3)\n    - [练习测验：强化学习导论](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3\u002FPractice%20Quiz%20-%20Reinforcement%20learning%20introduction)\n    - [练习测验：状态-动作值函数](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3\u002FPractice%20Quiz%20-%20State-action%20value%20function)\n    - [练习测验：连续状态空间](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3\u002FPractice%20Quiz%20-%20Continuous%20state%20spaces)\n    - [编程作业](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Ftree\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3\u002FC3W3A1)\n        - [深度Q学习——月球着陆器](https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fblob\u002Fmain\u002FC3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning\u002Fweek3\u002FC3W3A1\u002FC3_W3_A1_Assignment.ipynb)\n#### [结业证书](https:\u002F\u002Fcoursera.org\u002Fshare\u002F5bf5ee456b0c806df9b8622067b47ca6)\n\n\n### [专项课程证书](https:\u002F\u002Fcoursera.org\u002Fshare\u002Fa15ac6426f90924491a542850700a759)\n\n\u003Cbr\u002F>\n\n\u003Cbr\u002F>\n\n\u003Chr\u002F>\n\n\u003Cdiv align=\"center\">\n\n                        \n### 随时间变化的星标数\n[![随时间变化的星标数](https:\u002F\u002Fstarchart.cc\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera.svg?variant=adaptive)](https:\u002F\u002Fstarchart.cc\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera)                 \n\n[![访问量](https:\u002F\u002Fhits.seeyoufarm.com\u002Fapi\u002Fcount\u002Fincr\u002Fbadge.svg?url=https%3A%2F%2Fgithub.com%2Fgreyhatguy007%2FMachine-Learning-Specialization-Coursera&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https:\u002F\u002Fhits.seeyoufarm.com)\n\n\u003C\u002Fdiv>\n\n### 课程评价：\n\n本课程是迈向机器学习工程师之路的最佳起点。即便你已是该领域的专家，课程仍会深入讲解多种算法，例如决策树，这有助于进一步提升你的技能。\n\n**特别感谢[吴恩达教授](https:\u002F\u002Fwww.andrewng.org\u002F)对本课程的精心设计与量身定制。**\n\n\u003Cbr\u002F>\n\n\u003Chr\u002F>\n\n#### 完成本专项课程后，你可能达成的成果一览：\n\n* \u003Ci>编写无监督学习算法，利用深度Q学习实现**月球着陆器的精准着陆**\u003C\u002Fi>\n\n    - 经过多次失败的尝试，智能体最终学会了如何在标志物之间准确着陆到月球表面。\n    - 使用恰当的参数训练智能体后，最终成功着陆的视频如下：\n\nhttps:\u002F\u002Fuser-images.githubusercontent.com\u002F77543865\u002F182395635-703ae199-ba79-4940-86eb-23dd90093ab3.mp4\n\n* \u003Ci>编写**电影推荐系统**算法\u003C\u002Fi>\n    \n    - 收集了一个按类型分类的电影数据库。\n    - 训练了基于内容的过滤算法和协同过滤算法，并实现了完整的电影推荐系统。\n    - 系统能够根据用户偏好的电影类型提供个性化推荐。\n\n![movie_recommendation](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fgreyhatguy007_Machine-Learning-Specialization-Coursera_readme_c2b91c053c66.png)\n\n* \u003Ci>还有更多精彩内容！！\u003C\u002Fi>\n\n\n总而言之，这是一门我强烈推荐大家学习的课程。不仅因为你能学到许多新知识，更因为课程中的作业都是来自真实世界的案例，完成起来令人倍感兴奋。\n\n\u003Cbr\u002F>\n\n**祝学习愉快：))**","# Machine Learning Specialization Coursera 快速上手指南\n\n本仓库提供了吴恩达（Andrew Ng）在 Coursera 上发布的 [机器学习专项课程](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmachine-learning-introduction) 的练习测验解答、可选实验（Optional Labs）及编程作业（Programming Assignments）的代码实现。适合希望深入理解监督学习、神经网络及高级算法的中国开发者参考学习。\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*   **核心依赖库**：\n    *   `numpy` (数值计算)\n    *   `matplotlib` (数据可视化)\n    *   `scikit-learn` (机器学习工具包)\n    *   `tensorflow` (深度学习框架，部分实验需要)\n    *   `jupyter notebook` 或 `jupyter lab` (运行 `.ipynb` 文件)\n\n> **国内加速建议**：建议使用国内镜像源安装依赖，以提升下载速度并避免连接超时。\n\n## 安装步骤\n\n### 1. 克隆项目代码\n将仓库克隆到本地：\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera.git\ncd Machine-Learning-Specialization-Coursera\n```\n\n### 2. 创建虚拟环境（推荐）\n为了避免依赖冲突，建议创建独立的虚拟环境：\n```bash\npython -m venv ml_env\n# Windows\nml_env\\Scripts\\activate\n# macOS\u002FLinux\nsource ml_env\u002Fbin\u002Factivate\n```\n\n### 3. 安装依赖\n使用国内镜像源（如清华源）安装所需库：\n```bash\npip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple numpy matplotlib scikit-learn tensorflow jupyter\n```\n\n## 基本使用\n\n本项目主要由 Jupyter Notebook (`.ipynb`) 文件组成，涵盖了从线性回归到神经网络的完整代码实现。\n\n### 启动 Jupyter Lab\n在项目根目录下运行以下命令启动交互式界面：\n```bash\njupyter lab\n```\n浏览器会自动打开，您可以导航至对应的课程文件夹（例如 `C1 - Supervised Machine Learning...`）。\n\n### 运行示例：线性回归实验\n以第一门课第二周的“多变量回归”实验为例：\n\n1.  在 Jupyter 界面中进入路径：\n    `C1 - Supervised Machine Learning - Regression and Classification\u002Fweek2\u002FOptional Labs\u002F`\n2.  打开文件 `C1_W2_Lab02_Multiple_Variable_Soln.ipynb`。\n3.  点击单元格（Cell），按 `Shift + Enter` 依次执行代码。\n\n**代码片段示例（来自实验室）：**\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom lab_utils_multi import load_house_data\n\n# 加载数据\nX_train, y_train = load_house_data()\n\n# 查看数据前几行\nprint(X_train[:5])\nprint(y_train[:5])\n\n# 后续可按照 Notebook 指引进行特征缩放、梯度下降等操作\n```\n\n### 课程结构导航\n*   **Course 1**: 监督学习（回归与分类），包含线性回归、逻辑回归及正则化内容。\n*   **Course 2**: 高级学习算法，涵盖神经网络、TensorFlow 实现及决策树等。\n*   **Week 文件夹**: 每个周次文件夹内包含 `Practice quiz`（测验思路）、`Optional Labs`（原理演示代码）和 `Programming Assignment`（核心作业代码）。\n\n您可以直接运行 `Soln.ipynb` 后缀的文件查看完整解答与注释，辅助理解课程数学原理与代码实现。","一名刚转行数据科学的工程师正在自学吴恩达教授的机器学习课程，试图掌握回归与分类算法的核心原理并动手实现代码。\n\n### 没有 Machine-Learning-Specialization-Coursera 时\n- 在推导梯度下降或成本函数的数学公式时，一旦卡壳便无处查证，只能盲目搜索零散博客，难以确认理解是否准确。\n- 完成编程作业时，若代码报错或结果不收敛，缺乏标准参考代码（Solution）进行比对，往往花费数天调试却找不到逻辑漏洞。\n- 对于 Numpy 向量化等关键优化技巧，仅靠理论讲解难以吃透，缺少可运行的实验笔记（Optional Labs）来观察数据维度的实际变化。\n- 学习进度严重受阻，因无法验证练习测验（Practice Quiz）的答案，对“监督学习与无监督学习”等基础概念的信心不足。\n\n### 使用 Machine-Learning-Specialization-Coursera 后\n- 遇到数学难点时，可直接查阅仓库中详尽的笔记，快速厘清从模型表示到梯度更新的每一步推导逻辑。\n- 代码调试效率大幅提升，通过对比官方提供的 Jupyter Notebook 解决方案，能立即定位自己在实现线性回归时的参数更新错误。\n- 利用仓库中的可选实验代码，直观运行并修改 Numpy 向量化示例，深刻理解了如何避免循环以提升计算性能。\n- 每学完一周内容，即可通过配套的练习测验解析自测，确保对多变量回归等知识点的掌握扎实无误，学习路径清晰流畅。\n\nMachine-Learning-Specialization-Coursera 将抽象的理论与可执行的代码完美结合，为自学者提供了一套完整、可信且高效的实战导航系统。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fgreyhatguy007_Machine-Learning-Specialization-Coursera_158fc778.png","greyhatguy007","Ritvik","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fgreyhatguy007_b03f0d90.jpg",null,"[Null Pointer Exception]","rit_08_","https:\u002F\u002Fritvik-blog.vercel.app\u002F","https:\u002F\u002Fgithub.com\u002Fgreyhatguy007",[85,89],{"name":86,"color":87,"percentage":88},"Jupyter Notebook","#DA5B0B",97.5,{"name":90,"color":91,"percentage":92},"Python","#3572A5",2.5,7216,3617,"2026-04-05T06:28:03","MIT",1,"未说明",{"notes":100,"python":98,"dependencies":101},"该项目为 Coursera 吴恩达机器学习专项课程的代码解答与笔记，主要包含 Jupyter Notebook (.ipynb) 文件。根据内容推断，运行环境需支持 Jupyter Lab\u002FNotebook，并安装 NumPy、Scikit-learn 和 TensorFlow（课程中涉及 TF 实现）。由于是基础教学代码，通常对硬件无特殊高要求，普通 CPU 环境即可运行大部分练习，具体依赖版本需参考各 Notebook 文件内的导入语句或课程官方环境配置指南。",[102,103,104],"numpy","scikit-learn","tensorflow",[13],[107,108,109,110,111,112,113,114,115,116,117,118,119,104,120,121,122,123,124],"andrew-ng","andrew-ng-machine-learning","coursera","coursera-assignment","coursera-specialization","deep-learning","linear-regression","logistic-regression","machine-learning","python","solutions","supervised-machine-learning","unsupervised-machine-learning","recommendation-system","unsupervised-learning","decision-trees","neural-network","mooc","2026-03-27T02:49:30.150509","2026-04-06T11:30:52.546778",[128,133,138,143,148,153,158,163],{"id":129,"question_zh":130,"answer_zh":131,"source_url":132},17201,"在 Windows 上克隆仓库时遇到文件名错误或不兼容符号怎么办？","许多文件名包含冒号（:）或过长，这在 Windows 系统中是不允许的，会导致克隆失败。维护者已修复了这些非法文件名并缩短了文件长度。请拉取最新的代码更新即可解决此问题。","https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fissues\u002F10",{"id":134,"question_zh":135,"answer_zh":136,"source_url":137},17202,"运行梯度下降实验时出现 'OverflowError: Python int too large to convert to C long' 错误如何解决？","该问题通常发生在绘制图表时数据溢出。维护者已通过 PR#18 修复了此问题。如果自行尝试修复，有用户建议尝试添加 `dtype=np.uint64` 来处理数据类型，但最推荐的方法是更新仓库以获取官方修复版本。","https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fissues\u002F13",{"id":139,"question_zh":140,"answer_zh":141,"source_url":142},17203,"为什么找不到课程中提到的“反向传播（Back Propagation）”可选实验室文件？","这是因为 Coursera 课程在几个月前进行了更新，增加了关于“导数”和“反向传播”的新内容，而该仓库最初只包含了更新前的内容。维护者表示已将缺失的实验室文件添加到仓库中（见 PR #30），请确保拉取最新代码。","https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fissues\u002F14",{"id":144,"question_zh":145,"answer_zh":146,"source_url":147},17204,"Jupyter Notebook 中的符号表（Notation Tables）显示混乱或格式错误怎么办？","这是 Markdown 表格渲染的问题。维护者已在 PR #31 中修复了 C1W2 可选实验室 02 和 03 中的表格格式问题。此外，有用户指出代码块 `w = w - alpha * dj_dw` 可能需要修改为 `w = w - np.dot(alpha, dj_dw)` 以避免浮点数与列表相乘的错误，建议检查并更新相关代码。","https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fissues\u002F29",{"id":149,"question_zh":150,"answer_zh":151,"source_url":152},17205,"点击某些实验室或测验链接时出现 404 错误怎么办？","部分链接已失效，特别是第一门课程（监督机器学习）中的链接。维护者已修复了这些损坏的链接。如果遇到 404 错误，请刷新页面或重新克隆仓库以获取最新的正确链接。","https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fissues\u002F12",{"id":154,"question_zh":155,"answer_zh":156,"source_url":157},17206,"如何在代码中找到特定的练习标记（如 UNQ_C1）？","如果在代码中找不到注释行 `UNQ_C1` 等标记，这通常是因为本地文件版本过旧或与课程当前版本不匹配。多位用户反映了此问题，建议同步仓库的最新更改，确保笔记本文件与 Coursera 当前课程内容一致。","https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fissues\u002F37",{"id":159,"question_zh":160,"answer_zh":161,"source_url":162},17207,"运行编程作业时遇到语法错误或不熟悉 Jupyter Notebook 该怎么办？","测试用例由 Notebook 本身决定。如果遇到语法错误，建议先复习 Python 基础知识再继续课程。对于不熟悉 Jupyter Notebook 的用户，建议查阅相关入门教程了解基本操作（如运行单元格、查看输出等）。","https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fissues\u002F3",{"id":164,"question_zh":165,"answer_zh":166,"source_url":167},17208,"如何配置运行本课程所需的环境依赖？","用户常询问是否有 `requirements.txt` 或 `environment.yaml` 文件来设置环境。虽然 Issue 中未直接提供文件内容，但此类请求表明项目正在完善环境配置。建议查看仓库根目录是否已更新包含依赖列表的文件，或参考 Coursera 课程页面提供的标准环境配置指南（通常包括 numpy, matplotlib 等基础库）。","https:\u002F\u002Fgithub.com\u002Fgreyhatguy007\u002FMachine-Learning-Specialization-Coursera\u002Fissues\u002F56",[]]