[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-Coder-World04--Complete-Machine-Learning-":3,"tool-Coder-World04--Complete-Machine-Learning-":65},[4,23,32,40,49,57],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":22},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。",85092,2,"2026-04-10T11:13:16",[13,14,15,16,17,18,19,20,21],"图像","数据工具","视频","插件","Agent","其他","语言模型","开发框架","音频","ready",{"id":24,"name":25,"github_repo":26,"description_zh":27,"stars":28,"difficulty_score":29,"last_commit_at":30,"category_tags":31,"status":22},5784,"funNLP","fighting41love\u002FfunNLP","funNLP 是一个专为中文自然语言处理（NLP）打造的超级资源库，被誉为\"NLP 民工的乐园”。它并非单一的软件工具，而是一个汇集了海量开源项目、数据集、预训练模型和实用代码的综合性平台。\n\n面对中文 NLP 领域资源分散、入门门槛高以及特定场景数据匮乏的痛点，funNLP 提供了“一站式”解决方案。这里不仅涵盖了分词、命名实体识别、情感分析、文本摘要等基础任务的标准工具，还独特地收录了丰富的垂直领域资源，如法律、医疗、金融行业的专用词库与数据集，甚至包含古诗词生成、歌词创作等趣味应用。其核心亮点在于极高的全面性与实用性，从基础的字典词典到前沿的 BERT、GPT-2 模型代码，再到高质量的标注数据和竞赛方案，应有尽有。\n\n无论是刚刚踏入 NLP 领域的学生、需要快速验证想法的算法工程师，还是从事人工智能研究的学者，都能在这里找到急需的“武器弹药”。对于开发者而言，它能大幅减少寻找数据和复现模型的时间；对于研究者，它提供了丰富的基准测试资源和前沿技术参考。funNLP 以开放共享的精神，极大地降低了中文自然语言处理的开发与研究成本，是中文 AI 社区不可或缺的宝藏仓库。",79857,1,"2026-04-08T20:11:31",[19,14,18],{"id":33,"name":34,"github_repo":35,"description_zh":36,"stars":37,"difficulty_score":29,"last_commit_at":38,"category_tags":39,"status":22},5773,"cs-video-courses","Developer-Y\u002Fcs-video-courses","cs-video-courses 是一个精心整理的计算机科学视频课程清单，旨在为自学者提供系统化的学习路径。它汇集了全球知名高校（如加州大学伯克利分校、新南威尔士大学等）的完整课程录像，涵盖从编程基础、数据结构与算法，到操作系统、分布式系统、数据库等核心领域，并深入延伸至人工智能、机器学习、量子计算及区块链等前沿方向。\n\n面对网络上零散且质量参差不齐的教学资源，cs-video-courses 解决了学习者难以找到成体系、高难度大学级别课程的痛点。该项目严格筛选内容，仅收录真正的大学层级课程，排除了碎片化的简短教程或商业广告，确保用户能接触到严谨的学术内容。\n\n这份清单特别适合希望夯实计算机基础的开发者、需要补充特定领域知识的研究人员，以及渴望像在校生一样系统学习计算机科学的自学者。其独特的技术亮点在于分类极其详尽，不仅包含传统的软件工程与网络安全，还细分了生成式 AI、大语言模型、计算生物学等新兴学科，并直接链接至官方视频播放列表，让用户能一站式获取高质量的教育资源，免费享受世界顶尖大学的课堂体验。",79792,"2026-04-08T22:03:59",[18,13,14,20],{"id":41,"name":42,"github_repo":43,"description_zh":44,"stars":45,"difficulty_score":46,"last_commit_at":47,"category_tags":48,"status":22},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。",77062,3,"2026-04-04T04:44:48",[17,13,20,19,18],{"id":50,"name":51,"github_repo":52,"description_zh":53,"stars":54,"difficulty_score":46,"last_commit_at":55,"category_tags":56,"status":22},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。",75666,"2026-04-15T23:15:07",[19,13,20,18],{"id":58,"name":59,"github_repo":60,"description_zh":61,"stars":62,"difficulty_score":29,"last_commit_at":63,"category_tags":64,"status":22},3215,"awesome-machine-learning","josephmisiti\u002Fawesome-machine-learning","awesome-machine-learning 是一份精心整理的机器学习资源清单，汇集了全球优秀的机器学习框架、库和软件工具。面对机器学习领域技术迭代快、资源分散且难以甄选的痛点，这份清单按编程语言（如 Python、C++、Go 等）和应用场景（如计算机视觉、自然语言处理、深度学习等）进行了系统化分类，帮助使用者快速定位高质量项目。\n\n它特别适合开发者、数据科学家及研究人员使用。无论是初学者寻找入门库，还是资深工程师对比不同语言的技术选型，都能从中获得极具价值的参考。此外，清单还延伸提供了免费书籍、在线课程、行业会议、技术博客及线下聚会等丰富资源，构建了从学习到实践的全链路支持体系。\n\n其独特亮点在于严格的维护标准：明确标记已停止维护或长期未更新的项目，确保推荐内容的时效性与可靠性。作为机器学习领域的“导航图”，awesome-machine-learning 以开源协作的方式持续更新，旨在降低技术探索门槛，让每一位从业者都能高效地站在巨人的肩膀上创新。",72149,"2026-04-03T21:50:24",[20,18],{"id":66,"github_repo":67,"name":68,"description_en":69,"description_zh":70,"ai_summary_zh":70,"readme_en":71,"readme_zh":72,"quickstart_zh":73,"use_case_zh":74,"hero_image_url":75,"owner_login":76,"owner_name":77,"owner_avatar_url":78,"owner_bio":79,"owner_company":80,"owner_location":81,"owner_email":80,"owner_twitter":80,"owner_website":82,"owner_url":83,"languages":80,"stars":84,"forks":85,"last_commit_at":86,"license":87,"difficulty_score":29,"env_os":88,"env_gpu":89,"env_ram":89,"env_deps":90,"category_tags":98,"github_topics":80,"view_count":10,"oss_zip_url":80,"oss_zip_packed_at":80,"status":22,"created_at":99,"updated_at":100,"faqs":101,"releases":102},7951,"Coder-World04\u002FComplete-Machine-Learning-","Complete-Machine-Learning-","This repository contains everything you need to become proficient in Machine Learning","Complete-Machine-Learning- 是一个专为数据科学与机器学习初学者打造的系统化学习资源库。它通过\"60 天学习计划”的形式，将庞大的知识体系拆解为每日可执行的任务，帮助用户从零开始逐步掌握核心技能。\n\n该项目主要解决了学习者面对海量资料时无从下手、缺乏系统路径以及理论与实践脱节的痛点。内容安排循序渐进：前六天专注于 Python 编程，从基础语法、数据结构深入至面向对象、装饰器及生成器等高级特性；随后引入统计学基础，并计划延伸至完整的机器学习项目实战。除了代码实现，资源库还整合了技术面试指南、系统设计案例研究以及配套的视频教程，形成了“学习 - 实践 - 求职”的完整闭环。\n\n这套资源非常适合希望转行进入数据领域的开发者、计算机专业学生以及需要夯实基础的初级算法工程师。其独特亮点在于高度结构化的日程安排与“代码 + 文章 + 视频”的多维教学模式，让抽象的算法概念变得具体可感。无论你是想系统构建知识框架，还是为技术面试做准备，Complete-Machine-Learning- 都能提供一条清晰高效的成长路径。","# 60 days of Data Science and ML with project Series \n\n![image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCoder-World04_Complete-Machine-Learning-_readme_77f25f0cf808.png)\n\nYoutube for all the implemented projects and tech interview resources - [Ignito Youtube Channel](https:\u002F\u002Fwww.youtube.com\u002F@ignito5917\u002Fabout)\n\n[Complete Cheat Sheet for Tech Interviews - How to prepare efficiently](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fnaina0405\u002Fp\u002Fcheat-sheet-for-tech-interviews-how-339?r=14q3sp&utm_campaign=post&utm_medium=web)\n\n[I took theses Projects Based Courses to Build Industry aligned Data Science and ML skills](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fnaina0405\u002Fp\u002Fi-took-theses-projects-based-courses-af3?r=14q3sp&utm_campaign=post&utm_medium=web)\n\n[Part 1 - How to solve Any ML System Design Problem](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fnaina0405\u002Fp\u002Fpart-1-how-to-solve-any-ml-system?r=14q3sp&utm_campaign=post&utm_medium=web)\n\nStart here - [ML System Design Case Studies Series](https:\u002F\u002Fbit.ly\u002F3i5EDiH)\n\n------\n\n**Day 1 : Python Basics with Code Implementation — Part 1**\n\nIn this post we covered end to end Python Basics ( Part 1) that you should know. Topics like data types,strings, operators, and Chaining Comparison Operators with Logical Operators are covered.\n\nWhere to find Day 1 post: [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002F60-days-of-data-science-and-machine-learning-series-day-1-a62ce3c7aac1)\n\n---------------------\n\n**Day 2 : Python Basics with Code Implementation — Part 2**\n\nIn this post we covered end to end Python Basics ( Part 2) that you should know. Topics like Python Lists and Dictionaries, Sets, Tuples etc are covered in detail.\n\nWhere to find Day 2 post: [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-2-60-days-of-data-science-and-machine-learning-series-5ef21f098454)\n\n-------------------\n\n**Day 3 : Python Basics with Code Implementation — Part 3**\n\nIn this post we covered end to end Python Basics ( Part 3) that you should know. Topics like Tuples, Sets, Loops, Break and Continue Statements, Object-Oriented Programming and Class and attributes in Python are covered in detail.\n\nWhere to find Day 3 post: [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-3-60-days-of-data-science-and-machine-learning-series-abcd9c6c5c18)\n\n------------------\n\n**Day 4 : Intermediate Python with Code Implementation — Part 1**\n\nIn this post we covered end to end Intermediate Python ( Part 1) that you should know. Topics like First Class functions,Private Variables, Global and Non Local Variables, __import__ function, Magic Functions, Tuple Unpacking, Static Variables and Methods in Python are covered in detail.\nWhere to find Day 4 post:\n\nWhere to find Day 4 post: [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-4-60-days-of-data-science-and-machine-learning-series-dd590f54d928)\n\n--------------\n\n**Day 5: Intermediate Python with Code Implementation — Part 2**\n\nIn this post we covered end to end Intermediate Python( Part 2) that you should know. Topics like Lambda Functions, Magic methods, Inheritance and Polymorphism, Errors and Exception Handling, User-defined functions, Python garbage collection, and debugger are covered in detail.\n\nWhere to find Day 5 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-5-60-days-of-data-science-and-machine-learning-series-f31259481904)\n\n-------------\n\n**Day 6:Advanced Python with Code Implementation**\n\nIn this post we covered end to end Advanced Python that you should know. Topics like Decorators, Memoization using Decorators, Generators, Ordered and Defaultdict, Coroutine with Code implementation are covered in detail .\n\nWhere to find Day 6 post: [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-6-60-days-of-data-science-and-machine-learning-series-3cfd04c1011c)\n\n------------\n\n**Day 7 : Statistics for Data Science and Machine Learning with Code Implementation**\n\nIn this post we covered Statistics for Data Science you should know.\n\nWhere to find Day 7 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-7-60-days-of-data-science-and-machine-learning-6bc9cc2ceb0b)\n\n------------\n\n**Day 8 : Maths for Data Science and Machine learning**\n\nIn this post we covered Maths for ML. Topics like Linear Algebra, Calculus, Matrix and Vectors, Bayes Theorem and Cheatsheets etc are covered in detail.\n\nWhere to find Day 8 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-8-60-days-of-data-science-and-machine-learning-5155cfc78a68)\n\n------------\n\n**Day 9 : Pandas Part 1 with Code Implementation**\n\nIn this post we covered Pandas part 1 in depth with Code Implementation . Pandas is an open source Python package written for the Python programming language for data manipulation, analysis and ML tasks .\n\nWhere to find Day 9 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-9-60-days-of-data-science-and-machine-learning-2d0f75a498b9)\n\n----------\n\n**Day 10: Pandas Part 2 with Code Implementation**\n\nIn this post we covered Pandas part 2 in depth with Code Implementation.Topics like indexing,filtering, transformation, Merging, Hierarchical Indexing etc are covered.\n\nWhere to find Day 10 post: [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-10-60-days-of-data-science-and-machine-learning-d5d789fbda79)\n\n-----------\n\n**Day 11 : Numpy with Code Implementation**\n\nIn this post we covered Numpy part 1 with focus on Flattening the arrays, Concatenation and Broadcasting etc in detail. Numpy is a python library for scientific computing — to work with multidimensional array objects and used to handle large amount of data. An array which is a grid of values and is indexed by a tuple of nonnegative integers is main data structure of the Numpy library.\n\nWhere to find Day 11 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-11-60-days-of-data-science-and-machine-learning-643f1b3e3659)\n\n----------\n\n**Day 12:Data Pre-processing Part 1 with Code Implementation**\n\nIn this post we learned\u002Fimplemented Hands on Data Pre-processing in depth — Part 1. Data preprocessing , one of the first and crucial step — the process in which we prepare the raw data and make it suitable for a ML model to increase its accuracy and efficiency.\n\nWhere to find Day 12 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-12-60-days-of-data-science-and-machine-learning-1264f9c31b77)\n\n-----------\n\n**Day 13 : Data Pre-processing Part 1 with Code Implementation**\n\nIn this post we learned\u002Fimplemented Hands on Data Pre-processing in depth — Part 2 . Topics like Data Cleaning, Data Augmentation, Transformation, Channel Shift etc are covered in detail.\n\nWhere to find Day 13 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-13-60-days-of-data-science-and-machine-learning-52df0d8d88f)\n\n-----------\n\n**Day 14 : Regression Part 1 with Code Implementation**\n\nIn this post where we learned\u002Fimplemented Hands on Regression in depth — Part 1. Topics like Simple Linear Regression,Multi Linear Regression,Polynomial Regression are covered in detail.\n\nWhere to find Day 14 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-14-60-days-of-data-science-and-machine-learning-7486395061b)\n\n----------\n\n**Day 15: Regression Part 2 with Code Implementation**\n\nIn this post where we learned\u002Fimplemented Hands on Regression in depth — Part 2. Topics like Support Vector Regression, Decision Tree Regression and Random Forest Regression are covered in detail.\n\nWhere to find Day 15 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-15-60-days-of-data-science-and-machine-learning-65d582dc293a)\n\n----------\n\n**Day 16:Reflect and Connect the dots**\n\nIn this we covered various Data Science and ML projects.\n\nWhere to find Day 16 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-16-60-days-of-data-science-and-machine-learning-1e8340650e4)\n\n-----------\n\n**Day 17: Project — Kaggle’s annual Machine Learning and Data Science Survey ( Part 1 )**\n\nIn this post we implemented a project and covered some of the most important concepts —data cleaning, preprocessing, EDA etc through a project. This data ( Kaggle’s annual Machine Learning and Data Science Survey) has 42+ questions and 25,973 responses and for this post we will cover how to approach a problem and a very elementary view covering how to analyze your data.\n\nWhere to find Day 17 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-17-60-days-of-data-science-and-machine-learning-866400cab068)\n\n-----------\n\n**Day 18: Project —DecisionTreeRegressor and RandomForestRegressor**\n\nIn this post we developed an intuition and implemented DecisionTreeRegressor and RandomForestRegressor through a project.\n\nWhere to find Day 18 post : [Link](https:\u002F\u002Fnaina0412.medium.com\u002Fday-18-60-days-of-data-science-and-machine-learning-38c7a46f5496)\n\n-----------\n\n**Day 19: Project — Kaggle’s annual Machine Learning and Data Science Survey ( Part 2 )**\n\nIn this post we covered second part of the Kaggle’s annual Machine Learning and Data Science Survey project.\n\nWhere to find Day 19 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-19-60-days-of-data-science-and-machine-learning-bba89212d7ff)\n\n-----------\n\n**Day 20 : Project — Detailed Crypto Analysis (Part 1)**\n\nIn this post we covered detailed Crypto Analysis to build a basic intuition and part 2 covers how we can build a model to predict the prices .\n\nWhere to find Day 20 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-20-60-days-of-data-science-and-machine-learning-5ab13660b4ec)\n\n-----------\n\n**Day 21 : Exploratory Data Analysis Project**\n\nDemonstrated how to do effective Exploratory Data Visualization.\n\nWhere to find Day 21 post: [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-21-60-days-of-data-science-and-machine-learning-series-b0feb6ba71f4?sk=c2a68682a01ea2de48b837f429032db1)\n\n----------\n\n**Day 22 : All the Important ML algorithms with Projet 1**\n  \nWhere to find Day 22 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-22-60-days-of-data-science-and-machine-learning-series-752dca7af6e?sk=aee759311db45f3fcb6f8b0e084b8a7f)\n\n----------\n\n**Day 23 : ML Classification and a Project.**\n\nWhere to find Day 23 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-23-60-days-of-data-science-and-machine-learning-series-242818479186?sk=499e1e658844c3ceb9c0e60ad7beb91c)\n\n---------\n\n**Day 24 : ML Classification Project 2 ( Part 1)**\n\nClassification algorithms are used for predictive modeling problem where input training data is used to predict the probability that future data will fall into one of the predetermined\u002Flabelled categories.\n\nWhere to find Day 24 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-24-60-days-of-data-science-and-machine-learning-series-a151d4c86f51?sk=3d36f199bc940b844706108b02cb581d)\n\n---------\n**Day 25 : ML Classification Project 2 ( Part 2)**\n\nClassification algorithms are used for predictive modeling problem where input training data is used to predict the probability that future data will fall into one of the predetermined\u002Flabelled categories.\n\nWhere to find Day 25 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-25-60-days-of-data-science-and-machine-learning-series-5aca25ae9756?sk=93f029610eb2d595f07c3912f0c4c807)\n\n----------\n\n**Day 26 : Machine Learning Clustering in detail with a project 1**\n\nIn this post we covered Machine Learning Clustering in detail with a project( Part 1).\n\nWhere to find Day 26 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-26-60-days-of-data-science-and-machine-learning-series-5d317cea4cad)\n\n\n----------\n\n**Day 27 : Machine Learning Clustering in detail with a project 1**\n\nIn this post we covered Machine Learning Clustering in detail with a project( Part 2) .\n\nWhere to find Day 27 post : [Link](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-27-60-days-of-data-science-and-machine-learning-series-4c4b7fe6af7)\n\n----------\n\n**Day 28 : Machine Learning Clustering in detail with a project 2 ( part 1)**\n\nIn this post we covered Machine Learning Clustering in detail with another project( Part 1).\nWhere to find Day 28 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-28-60-days-of-data-science-and-machine-learning-series-ee7e4f3b6b46)\n\n----------\n\n**Day 29 : Machine Learning Clustering in detail with a project 2( part 2)**\n\nIn this post we covered Machine Learning Clustering in detail with another project( Part 2).\n\nWhere to find Day 29 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-29-60-days-of-data-science-and-machine-learning-series-a31184450ce5)\n\n----------\n**Day 30: Machine Learning Clustering in detail with a project 2 ( part 3)**\n\nIn this post we covered Machine Learning Clustering in detail with another project( Part 3).\n\nWhere to find Day 30 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-30-60-days-of-data-science-and-machine-learning-series-823fa9447928)\n\n----------\n**Day 31: Machine Learning Regression in detail with a project**\n\nIn this post we covered univariate linear regression with a project.\n\nWhere to find Day 31 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-31-60-days-of-data-science-and-machine-learning-series-7c211301bab0)\n\n----------\n**Day 32: Multiple linear regression with a project**\n\nIn this post we covered multiple linear regression with a project. Along the lines we evaluated model fit and accuracy using numerical measures such as R² and RMSE.\n\nWhere to find Day 32 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-32-60-days-of-data-science-and-machine-learning-series-c4a1205d37ff)\n\n----------\n**Day 33 : Logistic regression with a project**\n\nIn this post we covered logistic regression with a project.\n\nWhere to find Day 33 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-33-60-days-of-data-science-and-machine-learning-series-79830d11b365)\n\n----------\n**Day 34 : Logistic regression with another project**\n\nIn this post we covered logistic regression with another project.\n\nWhere to find Day 34 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-34-60-days-of-data-science-and-machine-learning-series-420df19d1ec0)\n\n----------\n**Day 35 : Principal Component Analysis with a project**\n\nIn this post we covered Principal Component Analysis with a project.\n\nWhere to find Day 35 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-35-60-days-of-data-science-and-machine-learning-series-63819382660)\n\n----------\n**Day 36 : Advanced Regression Techniques with project ( Part 1)**\n\nIn this post we covered Advanced Regression Techniques with a project\n\nWhere to find Day 36 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-36-60-days-of-data-science-and-machine-learning-series-7219a2bf77fc)\n\n----------\n**Day 37 : Advanced Regression Techniques with project ( Part 2)**\n\nIn this post we covered Advanced Regression Techniques with a project\n\nWhere to find Day 37 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-37-60-days-of-data-science-and-machine-learning-series-2e78afca9680)\n\n----------\n**Day 38 : Support Vector Machine with a project**\n\nIn this post we covered Support Vector Machine with a project\n\nWhere to find Day 38 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-38-60-days-of-data-science-and-machine-learning-series-6f9175b0d12)\n\n----------\n**Day 39 : Scikit learn with a project**\n\nIn this post we covered the basics of Scikit learn with a project.\n\nWhere to find Day 39 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-39-60-days-of-data-science-and-machine-learning-series-95af4ac9ac68)\n\n----------\n**Day 40 : Tensorflow with a project**\n\nIn this post we covered the basics of Tensorflow with a project .\n\nWhere to find Day 40 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-40-60-days-of-data-science-and-machine-learning-series-2f1214969836)\n\n----------\n**Day 41 : Neural Network with a project**\n\nIn this post we covered the basics of Neural Network with Tensorflow with a project.\n\nWhere to find Day 41 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-41-60-days-of-data-science-and-machine-learning-series-d0b6649587c9)\n\n----------\n**Day 42 : RNN and Tensorflow with a project**\n\nIn this post we covered the basics of RNN and Tensorflow with a project.\n\nWhere to find Day 42 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-42-60-days-of-data-science-and-machine-learning-series-d82a53d13cf7)\n\n----------\n**Day 43: Regression using Tensorflow with a project**\n\nIn this post we covered Regression using Tensorflow with a project\n\nWhere to find Day 43 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-43-60-days-of-data-science-and-machine-learning-series-299818452cea)\n\n----------\n\n**Day 44: Long Short Term Memory networks (LSTM) with Keras**\n\nIn this post we covered the basics of Long Short Term Memory networks (LSTM) with Keras through a project\n\nWhere to find Day 44 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-44-60-days-of-data-science-and-machine-learning-series-eee5568c4e97)\n\n----------\n**Day 45 : Recurrent Neural Network with a project**\n\nIn this post we covered the basics of Recurrent Neural Network with a project\n\nWhere to find Day 45 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-45-60-days-of-data-science-and-machine-learning-series-241136b9412e)\n\n----------\n**Day 46 : Language Classification with a project**\n\nIn this post we covered the basics of Multinomial Naive Bayes through a project.\n\nWhere to find Day 46 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-46-60-days-of-data-science-and-machine-learning-series-c7bbbb6750b2)\n\n----------\n**Day 47 : RNN and LSTM with a project**\n\nIn this post we covered the basics of RNN and LSTM with a project\n\nWhere to find Day 47 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-47-60-days-of-data-science-and-machine-learning-series-919df5d831db)\n\n----------\n**Day 48 : Multilayer Perceptron with project**\n\nIn this project we implemented a multilayer Perceptron model with Keras.\n\nWhere to find Day 48 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-48-60-days-of-data-science-and-machine-learning-series-b22b0c9bf384)\n\n----------\n**Day 49 : Yellowbrick for NLP**\n\nIn this post, we analyzed the text data using Yellowbrick and assess document similarity, topic modeling etc that are predicated on the notion of “similarity” between documents.\n\nWhere to find Day 49 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-49-60-days-of-data-science-and-machine-learning-series-311ab1d62bc2)\n\n----------\n**Day 50 : Bidirectional Encoder Representations from Transformers ( BERT) with a project**\n\nIn this post we learned how to fine tune BERT for text classification.\n\nWhere to find Day 50 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-50-60-days-of-data-science-and-machine-learning-series-33a30369d91a)\n\n----------\n**Day 51 : Yellowbrick with a project**\n\nIn this project we implemented visualization using yellowbrick\n\nWhere to find Day 51 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-51-60-days-of-data-science-and-machine-learning-series-b82a72fd1bd4)\n\n----------\n**Day 52 : Yellowbrick with 2nd project**\n\nIn this project we implemented visualization using yellowbrick through a project\n\nWhere to find Day 52 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-52-60-days-of-data-science-and-machine-learning-series-4e7788c3245e)\n\n----------\n**Day 53 : Yellowbrick with 3rd project**\n\nIn this project we implemented visualization using yellowbrick through a project\n\nWhere to find Day 53 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-53-60-days-of-data-science-and-machine-learning-series-d42724810a11)\n\n----------\n**Day 54 : Pytorch and ResNet with a project**\n\nIn this post we learned about the basics of PyTorch ( one of my favorite library) and ResNet.\n\nWhere to find Day 54 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-54-60-days-of-data-science-and-machine-learning-series-86491f964a0e)\n\n----------\n**Day 55 : Natural Language Processing using Naive Bayes through a project**\n\nIn this post we learned and implemented the basics of NLP using Naive Bayes through a project.\n\nWhere to find Day 55 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-55-60-days-of-data-science-and-machine-learning-series-7393ff714992)\n\n----------\n**Day 56 : ANN, Linear Regression, Decision Tree Regression and Random Forest with a project**\n\nIn this post we covered ANN, Linear Regression, Decision Tree Regression and Random Forest with a project\n\nWhere to find Day 56 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-56-60-days-of-data-science-and-machine-learning-series-71774a7fe5a1)\n\n----------\n**Day 57 : Deep learning and BERT**\n\nIn this post we learned how to perform sentiment analysis using BERT.\n\nWhere to find Day 57 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-57-60-days-of-data-science-and-machine-learning-series-43f3a687603c)\n\n----------\n**Day 58 : RNN and LSTM through a project**\n\nIn this post we covered the basics of RNN and LSTM through a project.\n\nWhere to find Day 58 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-58-60-days-of-data-science-and-machine-learning-series-2df3f4e03a55)\n\n----------\n\n**Day 59 : Natural Language Processing and Convolutions**\n\nIn this post we learned and implemented 1D Convolutions as Feature Extractors for Text in NLP.\n\nWhere to find Day 59 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-59-60-days-of-data-science-and-machine-learning-series-3786d513fcbd)\n\n----------\n\n**Day 60 : Transfer learning and Text Classification**\n\nIn this project we learned and implemented how to use transfer learning to fine-tune models, use pre-trained NLP text embedding models from TensorFlow Hub.\n\nWhere to find Day 60 post : [Link](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-60-60-days-of-data-science-and-machine-learning-series-29f72bd88c8c)\n\n----------\n\n# Some of the other best Series-\n\n[Complete 60 Days of Data Science and Machine Learning Series ](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-1-day-60-quick-recap-of-60-days-of-data-science-and-ml-6fc021643d1?sk=4e75e043b7630a9f963562ebac94e129)\n\n[30 days of Machine Learning Ops](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-1-of-30-days-of-machine-learning-ops-7c299e4b09be?sk=4ab48350a5c359fc157109e48b1d738f)\n\n[30 Days of Natural Language Processing ( NLP) Series](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fquick-recap-30-days-of-natural-language-processing-nlp-with-projects-series-ceb674e3c09b?sk=ca09b27b3d5867f23ab4dc367b6c0c32)\n\n[Data Science and Machine Learning Research ( papers) Simplified **](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-1-data-science-and-ml-research-papers-simplified-a68b00a3b1c4?sk=56136229ff738bd734f19d2b6953f78c)\n\n[30 days of Data Engineering with projects Series](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-1-of-30-days-of-data-engineering-894822fcb128?sk=76ba558bfe2d9f85cbe741e505295531)\n\n[60 days of Data Science and ML Series with projects](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-1-day-60-quick-recap-of-60-days-of-data-science-and-ml-6fc021643d1?sk=4e75e043b7630a9f963562ebac94e129)\n\n[100 days : Your Data Science and Machine Learning Degree Series with projects](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002F100-days-your-data-science-and-ml-degree-part-3-c621ecfdf711?sk=1a8c7b0c204d73432d56b7d1a3a26474)\n\n[23 Data Science Techniques You Should Know](https:\u002F\u002Fai.plainenglish.io\u002F23-data-science-techniques-you-should-know-61bc2c9d1b3a?sk=1680c36193eb22198974c9008d62a33c)\n\n[Tech Interview Series — Curated List of coding questions](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fmega-post-tech-interview-the-only-list-of-questions-you-need-to-practice-ee349ea197bb?sk=fac3614684daff4b50a70c0a71e4d528)\n\n[Complete System Design with most popular Questions Series](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fsystem-design-made-easy-quick-recap-of-complete-system-design-34af7e3aedfb?sk=bdd6a19edc1f3ce4a5064923f5b68721)\n\n[Complete Data Visualization and Pre-processing Series with projects](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fcomplete-data-preprocessing-and-data-visualization-with-projects-mega-compilation-part-2-41584ef0920e?sk=842390da51689b8d43148c3980570db0)\n\n[Complete Python Series with Projects](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fcomplete-python-and-projects-mega-compilation-7ec8f7adfe71?sk=ee0ecf43f23c6dd44dd35d984b3e5df4)\n\n[Complete Advanced Python Series with Projects](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fcomplete-advanced-python-with-projects-mega-compilation-part-6-729c1826032b?sk=7faffe20f8039fa57099f7a372b6d665)\n\n[Kaggle Best Notebooks that will teach you the most](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fmy-list-of-kaggle-best-notebooks-topic-wise-data-science-and-machine-learning-part-2-84772863e9ae?sk=5ed02e419854a6c11add3ddc1e52947f)\n\n[Complete Developers Guide to Git](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fthe-complete-developers-guide-to-git-6a23125996e1?sk=e30479bbe713930ea93018e1a46d9185)\n\n[Exceptional Github Repos — Part 1](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002F6-exceptional-github-repos-for-all-developers-part-1-21e8fa04e150?sk=9140b249af6fe73d45717185fad48962)\n\n[Exceptional Github Repos — Part 2](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002F6-exceptional-github-repos-for-all-developers-part-2-3eec9a68c31c?sk=8e31d0eb7eb1d2d0bbbcecaa66bd4e7e)\n\n[All the Data Science and Machine Learning Resources](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fbest-resources-for-data-science-and-machine-learning-full-list-5ceb9a2791bf?sk=cf85b2cef95560c58509877a794577ff)\n\n[210 Machine Learning Projects](https:\u002F\u002Fmedium.datadriveninvestor.com\u002F210-machine-learning-projects-with-source-code-that-you-can-build-today-721b035649e0?sk=da5f593572a0261a6314afad99a0356c)\n\n-------\n\n\n# 6 Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) - \n\n1. Complete Data Scientist : https:\u002F\u002Fbit.ly\u002F3wiIo8u\n\nLearn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud.\n\n----\n\n2. Complete Data Engineering : https:\u002F\u002Fbit.ly\u002F3A9oVs5\n\nLearn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets\n\n-----\n\n3. Complete Machine Learning Engineer : https:\u002F\u002Fbit.ly\u002F3Tir8ub\n\nLearn advanced machine learning techniques and algorithms - including how to package and deploy your models to a production environment.\n\n-----\n\n4. Complete Data Product Manager : https:\u002F\u002Fbit.ly\u002F3QGUtwi\n\nLeverage data to build products that deliver the right experiences, to the right users, at the right time. Lead the development of data-driven products that position businesses to win in their market.\n\n------\n\n5. Complete Natural Language Processing : https:\u002F\u002Fbit.ly\u002F3T7J8qY\n\nBuild models on real data, and get hands-on experience with sentiment analysis, machine translation, and more.\n\n------\n\n6. Complete Deep Learning: https:\u002F\u002Fbit.ly\u002F3T5ppIo\n\nLearn to implement Neural Networks using the deep learning framework PyTorch\n\n------\n\n\n\n","# 60天数据科学与机器学习项目系列\n\n![image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCoder-World04_Complete-Machine-Learning-_readme_77f25f0cf808.png)\n\n所有已实现项目的YouTube频道及技术面试资源——[Ignito YouTube频道](https:\u002F\u002Fwww.youtube.com\u002F@ignito5917\u002Fabout)\n\n[技术面试完整速查表——如何高效备考](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fnaina0405\u002Fp\u002Fcheat-sheet-for-tech-interviews-how-339?r=14q3sp&utm_campaign=post&utm_medium=web)\n\n[我通过这些项目式课程培养了符合行业需求的数据科学和机器学习技能](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fnaina0405\u002Fp\u002Fi-took-theses-projects-based-courses-af3?r=14q3sp&utm_campaign=post&utm_medium=web)\n\n[第1部分——如何解决任何机器学习系统设计问题](https:\u002F\u002Fopen.substack.com\u002Fpub\u002Fnaina0405\u002Fp\u002Fpart-1-how-to-solve-any-ml-system?r=14q3sp&utm_campaign=post&utm_medium=web)\n\n从这里开始——[机器学习系统设计案例研究系列](https:\u002F\u002Fbit.ly\u002F3i5EDiH)\n\n------\n\n**第1天：Python基础与代码实现——第1部分**\n\n在这篇文章中，我们全面介绍了你需要掌握的Python基础知识（第1部分）。内容包括数据类型、字符串、运算符，以及逻辑运算符与链式比较运算符的结合使用等。\n\n第1天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002F60-days-of-data-science-and-machine-learning-series-day-1-a62ce3c7aac1)\n\n---------------------\n\n**第2天：Python基础与代码实现——第2部分**\n\n在这篇文章中，我们全面介绍了你需要掌握的Python基础知识（第2部分）。详细讲解了Python列表、字典、集合、元组等内容。\n\n第2天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-2-60-days-of-data-science-and-machine-learning-series-5ef21f098454)\n\n-------------------\n\n**第3天：Python基础与代码实现——第3部分**\n\n在这篇文章中，我们全面介绍了你需要掌握的Python基础知识（第3部分）。详细讲解了元组、集合、循环、break和continue语句、面向对象编程以及Python中的类和属性等内容。\n\n第3天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-3-60-days-of-data-science-and-machine-learning-series-abcd9c6c5c18)\n\n------------------\n\n**第4天：Python进阶与代码实现——第1部分**\n\n在这篇文章中，我们全面介绍了你需要掌握的Python进阶知识（第1部分）。详细讲解了高阶函数、私有变量、全局变量与非局部变量、__import__函数、魔术方法、元组解包、静态变量和方法等内容。\n\n第4天的文章链接：\n\n第4天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-4-60-days-of-data-science-and-machine-learning-series-dd590f54d928)\n\n--------------\n\n**第5天：Python进阶与代码实现——第2部分**\n\n在这篇文章中，我们全面介绍了你需要掌握的Python进阶知识（第2部分）。详细讲解了Lambda函数、魔术方法、继承与多态、错误与异常处理、用户自定义函数、Python垃圾回收机制以及调试器等内容。\n\n第5天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-5-60-days-of-data-science-and-machine-learning-series-f31259481904)\n\n-------------\n\n**第6天：Python高级特性与代码实现**\n\n在这篇文章中，我们全面介绍了你需要掌握的Python高级特性。详细讲解了装饰器、使用装饰器进行记忆化、生成器、有序字典与默认字典，以及带有代码实现的协程等内容。\n\n第6天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-6-60-days-of-data-science-and-machine-learning-series-3cfd04c1011c)\n\n------------\n\n**第7天：数据科学与机器学习中的统计学与代码实现**\n\n在这篇文章中，我们介绍了数据科学领域必须掌握的统计学知识。\n\n第7天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-7-60-days-of-data-science-and-machine-learning-6bc9cc2ceb0b)\n\n------------\n\n**第8天：数据科学与机器学习中的数学**\n\n在这篇文章中，我们介绍了机器学习所需的数学知识。详细讲解了线性代数、微积分、矩阵与向量、贝叶斯定理以及各类速查表等内容。\n\n第8天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-8-60-days-of-data-science-and-machine-learning-5155cfc78a68)\n\n------------\n\n**第9天：Pandas第1部分与代码实现**\n\n在这篇文章中，我们深入讲解并实现了Pandas第1部分的内容。Pandas是一个开源的Python库，专为Python编程语言设计，用于数据处理、分析和机器学习任务。\n\n第9天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-9-60-days-of-data-science-and-machine-learning-2d0f75a498b9)\n\n----------\n\n**第10天：Pandas第2部分与代码实现**\n\n在这篇文章中，我们深入讲解并实现了Pandas第2部分的内容。主题包括索引、过滤、转换、合并、层次化索引等。\n\n第10天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-10-60-days-of-data-science-and-machine-learning-d5d789fbda79)\n\n-----------\n\n**第11天：Numpy与代码实现**\n\n在这篇文章中，我们重点讲解了Numpy第1部分的内容，详细介绍了数组展平、拼接和广播等操作。Numpy是用于科学计算的Python库，主要用于处理多维数组对象，能够高效地管理大量数据。数组是由一组值组成的网格，通过非负整数元组进行索引，是Numpy库的核心数据结构。\n\n第11天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-11-60-days-of-data-science-and-machine-learning-643f1b3e3659)\n\n----------\n\n**第12天：数据预处理第1部分与代码实现**\n\n在这篇文章中，我们深入学习并实践了数据预处理——第1部分。数据预处理是机器学习流程中的首要且关键步骤之一，旨在对原始数据进行准备，使其更适合机器学习模型，从而提高模型的准确性和效率。\n\n第12天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-12-60-days-of-data-science-and-machine-learning-1264f9c31b77)\n\n-----------\n\n**第13天：数据预处理第2部分与代码实现**\n\n在这篇文章中，我们深入学习并实践了数据预处理——第2部分。详细讲解了数据清洗、数据增强、数据转换、通道偏移等内容。\n\n第13天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-13-60-days-of-data-science-and-machine-learning-52df0d8d88f)\n\n-----------\n\n**第14天：回归分析第1部分与代码实现**\n\n在这篇文章中，我们深入学习并实践了回归分析——第1部分。详细讲解了简单线性回归、多元线性回归和多项式回归等内容。\n\n第14天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-14-60-days-of-data-science-and-machine-learning-7486395061b)\n\n----------\n\n**第15天：回归分析第二部分——代码实现**\n\n在这篇文章中，我们深入学习并实现了回归分析的实践操作——第二部分。详细介绍了支持向量回归、决策树回归和随机森林回归等内容。\n\n第15天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-15-60-days-of-data-science-and-machine-learning-65d582dc293a)\n\n----------\n\n**第16天：反思与串联知识点**\n\n在这一篇中，我们回顾了多个数据科学和机器学习项目。\n\n第16天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-16-60-days-of-data-science-and-machine-learning-1e8340650e4)\n\n-----------\n\n**第17天：项目——Kaggle年度机器学习与数据科学调查（第一部分）**\n\n在这篇文章中，我们通过一个项目来实践，并涵盖了数据清洗、预处理、探索性数据分析等重要概念。该数据集（Kaggle年度机器学习与数据科学调查）包含42个以上的问题和25,973份回复。在本篇中，我们将介绍如何着手解决一个问题，以及如何对数据进行初步分析的基本思路。\n\n第17天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-17-60-days-of-data-science-and-machine-learning-866400cab068)\n\n-----------\n\n**第18天：项目——DecisionTreeRegressor与RandomForestRegressor**\n\n在这篇文章中，我们通过一个项目培养了直觉，并实现了决策树回归和随机森林回归模型。\n\n第18天的文章链接：[链接](https:\u002F\u002Fnaina0412.medium.com\u002Fday-18-60-days-of-data-science-and-machine-learning-38c7a46f5496)\n\n-----------\n\n**第19天：项目——Kaggle年度机器学习与数据科学调查（第二部分）**\n\n在这篇文章中，我们继续完成了Kaggle年度机器学习与数据科学调查项目的第二部分。\n\n第19天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-19-60-days-of-data-science-and-machine-learning-bba89212d7ff)\n\n-----------\n\n**第20天：项目——加密货币详细分析（第一部分）**\n\n在这篇文章中，我们进行了加密货币的详细分析，以建立基本的直观理解；第二部分则将探讨如何构建模型来预测价格。\n\n第20天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-20-60-days-of-data-science-and-machine-learning-5ab13660b4ec)\n\n-----------\n\n**第21天：探索性数据分析项目**\n\n展示了如何进行有效的探索性数据可视化。\n\n第21天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-21-60-days-of-data-science-and-machine-learning-series-b0feb6ba71f4?sk=c2a68682a01ea2de48b837f429032db1)\n\n----------\n\n**第22天：所有重要的机器学习算法与项目1**\n\n第22天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-22-60-days-of-data-science-and-machine-learning-series-752dca7af6e?sk=aee759311db45f3fcb6f8b0e084b8a7f)\n\n----------\n\n**第23天：机器学习分类及其项目。**\n\n第23天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-23-60-days-of-data-science-and-machine-learning-series-242818479186?sk=499e1e658844c3ceb9c0e60ad7beb91c)\n\n---------\n\n**第24天：机器学习分类项目2（第一部分）**\n\n分类算法用于预测建模问题，通过输入的训练数据来预测未来数据属于某一预先定义或标记类别的概率。\n\n第24天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-24-60-days-of-data-science-and-machine-learning-series-a151d4c86f51?sk=3d36f199bc940b844706108b02cb581d)\n\n---------\n**第25天：机器学习分类项目2（第二部分）**\n\n分类算法用于预测建模问题，通过输入的训练数据来预测未来数据属于某一预先定义或标记类别的概率。\n\n第25天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-25-60-days-of-data-science-and-machine-learning-series-5aca25ae9756?sk=93f029610eb2d595f07c3912f0c4c807)\n\n----------\n\n**第26天：机器学习聚类详解与项目1**\n\n在这篇文章中，我们通过一个项目详细讲解了机器学习聚类（第一部分）。\n\n第26天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-26-60-days-of-data-science-and-machine-learning-series-5d317cea4cad)\n\n\n----------\n\n**第27天：机器学习聚类详解与项目1**\n\n在这篇文章中，我们通过一个项目详细讲解了机器学习聚类（第二部分）。\n\n第27天的文章链接：[链接](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fday-27-60-days-of-data-science-and-machine-learning-series-4c4b7fe6af7)\n\n----------\n\n**第28天：机器学习聚类详解与项目2（第一部分）**\n\n在这篇文章中，我们通过另一个项目详细讲解了机器学习聚类（第一部分）。\n第28天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-28-60-days-of-data-science-and-machine-learning-series-ee7e4f3b6b46)\n\n----------\n\n**第29天：机器学习聚类详解与项目2（第二部分）**\n\n在这篇文章中，我们通过另一个项目详细讲解了机器学习聚类（第二部分）。\n\n第29天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-29-60-days-of-data-science-and-machine-learning-series-a31184450ce5)\n\n----------\n**第30天：机器学习聚类详解与项目2（第三部分）**\n\n在这篇文章中，我们通过另一个项目详细讲解了机器学习聚类（第三部分）。\n\n第30天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-30-60-days-of-data-science-and-machine-learning-series-823fa9447928)\n\n----------\n**第31天：机器学习回归详解与项目**\n\n在这篇文章中，我们通过一个项目讲解了单变量线性回归。\n\n第31天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-31-60-days-of-data-science-and-machine-learning-series-7c211301bab0)\n\n----------\n**第32天：多元线性回归与项目**\n\n在这篇文章中，我们通过一个项目讲解了多元线性回归。同时，我们还使用R²和RMSE等数值指标评估了模型的拟合程度和准确性。\n\n第32天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-32-60-days-of-data-science-and-machine-learning-series-c4a1205d37ff)\n\n----------\n**第33天：逻辑回归与项目**\n\n在这篇文章中，我们通过一个项目讲解了逻辑回归。\n\n第33天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-33-60-days-of-data-science-and-machine-learning-series-79830d11b365)\n\n----------\n**第34天：逻辑回归与另一个项目**\n\n在这篇文章中，我们通过另一个项目讲解了逻辑回归。\n\n第34天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-34-60-days-of-data-science-and-machine-learning-series-420df19d1ec0)\n\n----------\n**第35天：带项目的主成分分析**\n\n在这篇文章中，我们通过一个项目讲解了主成分分析。\n\n第35天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-35-60-days-of-data-science-and-machine-learning-series-63819382660)\n\n----------\n**第36天：带项目的高级回归技术（上）**\n\n在这篇文章中，我们通过一个项目讲解了高级回归技术。\n\n第36天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-36-60-days-of-data-science-and-machine-learning-series-7219a2bf77fc)\n\n----------\n**第37天：带项目的高级回归技术（下）**\n\n在这篇文章中，我们通过一个项目讲解了高级回归技术。\n\n第37天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-37-60-days-of-data-science-and-machine-learning-series-2e78afca9680)\n\n----------\n**第38天：带项目的支持向量机**\n\n在这篇文章中，我们通过一个项目讲解了支持向量机。\n\n第38天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-38-60-days-of-data-science-and-machine-learning-series-6f9175b0d12)\n\n----------\n**第39天：带项目的Scikit-learn**\n\n在这篇文章中，我们通过一个项目讲解了Scikit-learn的基础知识。\n\n第39天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-39-60-days-of-data-science-and-machine-learning-series-95af4ac9ac68)\n\n----------\n**第40天：带项目的TensorFlow**\n\n在这篇文章中，我们通过一个项目讲解了TensorFlow的基础知识。\n\n第40天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-40-60-days-of-data-science-and-machine-learning-series-2f1214969836)\n\n----------\n**第41天：带项目的神经网络**\n\n在这篇文章中，我们通过TensorFlow和一个项目讲解了神经网络的基础知识。\n\n第41天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-41-60-days-of-data-science-and-machine-learning-series-d0b6649587c9)\n\n----------\n**第42天：带项目的RNN和TensorFlow**\n\n在这篇文章中，我们通过一个项目讲解了RNN和TensorFlow的基础知识。\n\n第42天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-42-60-days-of-data-science-and-machine-learning-series-d82a53d13cf7)\n\n----------\n**第43天：带项目的TensorFlow回归**\n\n在这篇文章中，我们通过一个项目讲解了使用TensorFlow进行回归的方法。\n\n第43天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-43-60-days-of-data-science-and-machine-learning-series-299818452cea)\n\n----------\n\n**第44天：使用Keras的长短期记忆网络（LSTM）**\n\n在这篇文章中，我们通过一个项目讲解了使用Keras实现长短期记忆网络（LSTM）的基础知识。\n\n第44天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-44-60-days-of-data-science-and-machine-learning-series-eee5568c4e97)\n\n----------\n**第45天：带项目的循环神经网络**\n\n在这篇文章中，我们通过一个项目讲解了循环神经网络的基础知识。\n\n第45天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-45-60-days-of-data-science-and-machine-learning-series-241136b9412e)\n\n----------\n**第46天：带项目的语言分类**\n\n在这篇文章中，我们通过一个项目讲解了多项式朴素贝叶斯的基础知识。\n\n第46天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-46-60-days-of-data-science-and-machine-learning-series-c7bbbb6750b2)\n\n----------\n**第47天：带项目的RNN和LSTM**\n\n在这篇文章中，我们通过一个项目讲解了RNN和LSTM的基础知识。\n\n第47天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-47-60-days-of-data-science-and-machine-learning-series-919df5d831db)\n\n----------\n**第48天：带项目的多层感知机**\n\n在这个项目中，我们使用Keras实现了多层感知机模型。\n\n第48天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-48-60-days-of-data-science-and-machine-learning-series-b22b0c9bf384)\n\n----------\n**第49天：用于自然语言处理的Yellowbrick**\n\n在这篇文章中，我们使用Yellowbrick对文本数据进行了分析，并评估了文档相似性、主题建模等与文档“相似性”相关的任务。\n\n第49天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-49-60-days-of-data-science-and-machine-learning-series-311ab1d62bc2)\n\n----------\n**第50天：带项目的来自变换器的双向编码器表示（BERT）**\n\n在这篇文章中，我们学习了如何对BERT进行微调以用于文本分类。\n\n第50天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-50-60-days-of-data-science-and-machine-learning-series-33a30369d91a)\n\n----------\n**第51天：带项目的Yellowbrick**\n\n在这个项目中，我们使用Yellowbrick实现了可视化。\n\n第51天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-51-60-days-of-data-science-and-machine-learning-series-b82a72fd1bd4)\n\n----------\n**第52天：第二次带项目的Yellowbrick**\n\n在这个项目中，我们通过一个项目使用Yellowbrick实现了可视化。\n\n第52天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-52-60-days-of-data-science-and-machine-learning-series-4e7788c3245e)\n\n----------\n**第53天：第三次带项目的Yellowbrick**\n\n在这个项目中，我们通过一个项目使用Yellowbrick实现了可视化。\n\n第53天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-53-60-days-of-data-science-and-machine-learning-series-d42724810a11)\n\n----------\n**第54天：带项目的PyTorch和ResNet**\n\n在这篇文章中，我们学习了PyTorch（我最喜欢的库之一）和ResNet的基础知识。\n\n第54天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-54-60-days-of-data-science-and-machine-learning-series-86491f964a0e)\n\n----------\n**第55天：带项目的朴素贝叶斯自然语言处理**\n\n在这篇文章中，我们通过一个项目学习并实现了基于朴素贝叶斯的自然语言处理基础知识。\n\n第55天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-55-60-days-of-data-science-and-machine-learning-series-7393ff714992)\n\n----------\n**第56天：带项目的人工神经网络、线性回归、决策树回归和随机森林**\n\n在这篇文章中，我们通过一个项目讲解了人工神经网络、线性回归、决策树回归和随机森林。\n\n第56天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-56-60-days-of-data-science-and-machine-learning-series-71774a7fe5a1)\n\n----------\n**第57天：深度学习和BERT**\n\n在这篇文章中，我们学习了如何使用BERT进行情感分析。\n\n第57天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-57-60-days-of-data-science-and-machine-learning-series-43f3a687603c)\n\n----------\n**第58天：带项目的RNN和LSTM**\n\n在这篇文章中，我们通过一个项目讲解了RNN和LSTM的基础知识。\n\n第58天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-58-60-days-of-data-science-and-machine-learning-series-2df3f4e03a55)\n\n----------\n\n**第59天：自然语言处理与卷积**\n\n在这篇文章中，我们学习并实现了用于自然语言处理中文本特征提取的一维卷积。\n\n第59天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-59-60-days-of-data-science-and-machine-learning-series-3786d513fcbd)\n\n----------\n\n**第60天：迁移学习与文本分类**\n\n在这个项目中，我们学习并实践了如何使用迁移学习对模型进行微调，以及如何利用 TensorFlow Hub 中预训练的 NLP 文本嵌入模型。\n\n第60天的文章链接：[链接](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-60-60-days-of-data-science-and-machine-learning-series-29f72bd88c8c)\n\n----------\n\n\n\n# 其他一些优质系列-\n\n[完整版 60 天数据科学与机器学习系列 ](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-1-day-60-quick-recap-of-60-days-of-data-science-and-ml-6fc021643d1?sk=4e75e043b7630a9f963562ebac94e129)\n\n[30 天机器学习运维系列](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-1-of-30-days-of-machine-learning-ops-7c299e4b09be?sk=4ab48350a5c359fc157109e48b1d738f)\n\n[30 天自然语言处理（NLP）系列](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fquick-recap-30-days-of-natural-language-processing-nlp-with-projects-series-ceb674e3c09b?sk=ca09b27b3d5867f23ab4dc367b6c0c32)\n\n[数据科学与机器学习研究（论文）简化版 **](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-1-data-science-and-ml-research-papers-simplified-a68b00a3b1c4?sk=56136229ff738bd734f19d2b6953f78c)\n\n[30 天数据工程与项目系列](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-1-of-30-days-of-data-engineering-894822fcb128?sk=76ba558bfe2d9f85cbe741e505295531)\n\n[60 天数据科学与机器学习系列及项目](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fday-1-day-60-quick-recap-of-60-days-of-data-science-and-ml-6fc021643d1?sk=4e75e043b7630a9f963562ebac94e129)\n\n[100 天：你的数据科学与机器学习学位系列及项目](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002F100-days-your-data-science-and-ml-degree-part-3-c621ecfdf711?sk=1a8c7b0c204d73432d56b7d1a3a26474)\n\n[你应该掌握的 23 种数据科学技巧](https:\u002F\u002Fai.plainenglish.io\u002F23-data-science-techniques-you-should-know-61bc2c9d1b3a?sk=1680c36193eb22198974c9008d62a33c)\n\n[技术面试系列 — 精选编程题列表](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fmega-post-tech-interview-the-only-list-of-questions-you-need-to-practice-ee349ea197bb?sk=fac3614684daff4b50a70c0a71e4d528)\n\n[包含最热门问题的完整系统设计系列](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fsystem-design-made-easy-quick-recap-of-complete-system-design-34af7e3aedfb?sk=bdd6a19edc1f3ce4a5064923f5b68721)\n\n[包含项目的完整数据可视化与预处理系列](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fcomplete-data-preprocessing-and-data-visualization-with-projects-mega-compilation-part-2-41584ef0920e?sk=842390da51689b8d43148c3980570db0)\n\n[包含项目的完整 Python 系列](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fcomplete-python-and-projects-mega-compilation-7ec8f7adfe71?sk=ee0ecf43f23c6dd44dd35d984b3e5df4)\n\n[包含项目的完整高级 Python 系列](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fcomplete-advanced-python-with-projects-mega-compilation-part-6-729c1826032b?sk=7faffe20f8039fa57099f7a372b6d665)\n\n[Kaggle 最佳笔记本，教你最多知识](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002Fmy-list-of-kaggle-best-notebooks-topic-wise-data-science-and-machine-learning-part-2-84772863e9ae?sk=5ed02e419854a6c11add3ddc1e52947f)\n\n[完整的 Git 开发者指南](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fthe-complete-developers-guide-to-git-6a23125996e1?sk=e30479bbe713930ea93018e1a46d9185)\n\n[杰出的 GitHub 仓库 — 第一部分](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002F6-exceptional-github-repos-for-all-developers-part-1-21e8fa04e150?sk=9140b249af6fe73d45717185fad48962)\n\n[杰出的 GitHub 仓库 — 第二部分](https:\u002F\u002Fmedium.com\u002Fcoders-mojo\u002F6-exceptional-github-repos-for-all-developers-part-2-3eec9a68c31c?sk=8e31d0eb7eb1d2d0bbbcecaa66bd4e7e)\n\n[所有数据科学与机器学习资源](https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fbest-resources-for-data-science-and-machine-learning-full-list-5ceb9a2791bf?sk=cf85b2cef95560c58509877a794577ff)\n\n[210 个机器学习项目](https:\u002F\u002Fmedium.datadriveninvestor.com\u002F210-machine-learning-projects-with-source-code-that-you-can-build-today-721b035649e0?sk=da5f593572a0261a6314afad99a0356c)\n\n-------\n\n\n# 6 款强烈推荐的数据科学与机器学习课程，你一定要参加（附证书） - \n\n1. 完整数据科学家：https:\u002F\u002Fbit.ly\u002F3wiIo8u\n\n学习运行数据管道、设计实验、构建推荐系统，并将解决方案部署到云端。\n\n----\n\n2. 完整数据工程师：https:\u002F\u002Fbit.ly\u002F3A9oVs5\n\n学习设计数据模型、构建数据仓库和数据湖、自动化数据管道，以及处理海量数据。\n\n-----\n\n3. 完整机器学习工程师：https:\u002F\u002Fbit.ly\u002F3Tir8ub\n\n学习先进的机器学习技术和算法——包括如何打包并将模型部署到生产环境。\n\n-----\n\n4. 完整数据产品经理：https:\u002F\u002Fbit.ly\u002F3QGUtwi\n\n利用数据打造能够为正确用户在恰当时间提供合适体验的产品。领导开发以数据驱动为核心的产品，帮助企业在市场中占据优势地位。\n\n------\n\n5. 完整自然语言处理：https:\u002F\u002Fbit.ly\u002F3T7J8qY\n\n基于真实数据构建模型，并通过实践掌握情感分析、机器翻译等技能。\n\n------\n\n6. 完整深度学习：https:\u002F\u002Fbit.ly\u002F3T5ppIo\n\n学习使用深度学习框架 PyTorch 实现神经网络。\n\n------","# Complete-Machine-Learning 快速上手指南\n\n本项目并非一个可直接安装的 Python 库，而是一套为期 60 天的**数据科学与机器学习学习路径与实战项目系列**。它通过每日教程涵盖从 Python 基础、数学统计到高级机器学习算法（回归、分类、聚类）及系统设计的完整内容。\n\n以下是基于该系列内容的学习与实战环境搭建指南。\n\n## 环境准备\n\n在开始本系列的学习与代码复现前，请确保你的开发环境满足以下要求：\n\n*   **操作系统**：Windows, macOS 或 Linux\n*   **Python 版本**：推荐 Python 3.8 及以上版本\n*   **核心依赖库**：\n    *   `numpy` (数值计算)\n    *   `pandas` (数据处理与分析)\n    *   `matplotlib` \u002F `seaborn` (数据可视化)\n    *   `scikit-learn` (机器学习算法实现)\n    *   `jupyter` (交互式笔记本，推荐用于跟随教程运行代码)\n\n## 安装步骤\n\n建议使用 `conda` 或 `pip` 创建独立的虚拟环境以避免依赖冲突。国内用户推荐使用清华源或阿里源加速下载。\n\n### 方案 A：使用 Conda (推荐)\n\n```bash\n# 1. 创建名为 ml_60days 的虚拟环境，指定 Python 3.9\nconda create -n ml_60days python=3.9 -y\n\n# 2. 激活环境\nconda activate ml_60days\n\n# 3. 使用清华源安装核心数据科学栈\nconda install numpy pandas matplotlib seaborn scikit-learn jupyter -c https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002Fanaconda\u002Fpkgs\u002Fmain\u002F -y\n```\n\n### 方案 B：使用 Pip\n\n```bash\n# 1. 创建虚拟环境 (可选但推荐)\npython -m venv ml_60days\nsource ml_60days\u002Fbin\u002Factivate  # Windows 用户请使用: ml_60days\\Scripts\\activate\n\n# 2. 使用阿里云镜像源升级 pip 并安装依赖\npip install -i https:\u002F\u002Fmirrors.aliyun.com\u002Fpypi\u002Fsimple\u002F --upgrade pip\npip install -i https:\u002F\u002Fmirrors.aliyun.com\u002Fpypi\u002Fsimple\u002F numpy pandas matplotlib seaborn scikit-learn jupyter\n```\n\n## 基本使用\n\n本项目的“使用”方式是跟随每日教程链接，在本地 Jupyter Notebook 中复现代码并进行实验。\n\n### 1. 启动开发环境\n\n在终端中运行以下命令启动 Jupyter Notebook：\n\n```bash\njupyter notebook\n```\n\n浏览器将自动打开，点击 \"New\" -> \"Python 3\" 创建一个新的笔记本文件。\n\n### 2. 最小化运行示例 (对应 Day 9-11 内容)\n\n你可以复制以下代码到一个新的 Cell 中运行，验证环境是否配置正确，并体验基础的 Pandas 与 Sklearn 流程（参考教程中的数据处理与回归部分）：\n\n```python\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\n\n# 1. 生成模拟数据 (模拟 Day 14 回归任务)\nnp.random.seed(42)\nX = np.random.rand(100, 1) * 10\ny = 2.5 * X.squeeze() + np.random.randn(100) * 2\n\n# 2. 创建 DataFrame (模拟 Day 9-10 Pandas 操作)\ndf = pd.DataFrame({'Feature': X.squeeze(), 'Target': y})\nprint(\"数据预览:\")\nprint(df.head())\n\n# 3. 划分训练集与测试集\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# 4. 训练模型 (模拟 Day 14 线性回归)\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\n# 5. 预测与评估\ny_pred = model.predict(X_test)\nmse = mean_squared_error(y_test, y_pred)\n\nprint(f\"\\n模型均方误差 (MSE): {mse:.4f}\")\nprint(f\"模型系数: {model.coef_[0]:.4f}\")\n```\n\n### 3. 跟随学习路径\n\n访问 README 中提供的每日链接（如 Day 1 Python 基础至 Day 60 系统设计），将教程中的代码块复制到你的 Notebook 中，结合上述环境进行逐步练习。重点关注数据预处理（Day 12-13）、特征工程及各类模型（回归、分类、聚类）的项目实战部分。","刚毕业的数据科学专业学生李明，正面临紧迫的校招面试压力，急需在两个月内系统梳理从 Python 基础到机器学习系统设计的完整知识体系。\n\n### 没有 Complete-Machine-Learning- 时\n- **学习路径碎片化**：需要在 GitHub、博客和论坛间反复跳转寻找资料，难以区分哪些是面试高频考点，导致复习重点偏离。\n- **理论与代码脱节**：看懂了统计学公式或装饰器原理，却找不到对应的工业级代码实现，面试手写代码时经常卡壳。\n- **缺乏系统设计方案**：面对“如何设计推荐系统”这类开放性问题，只能零散回答算法模型，无法构建包含数据流、监控和扩展性的完整架构。\n- **时间管理失控**：由于缺乏明确的每日计划，容易在基础语法上耗费过多时间，导致后期高阶内容来不及深入。\n\n### 使用 Complete-Machine-Learning- 后\n- **拥有结构化路线图**：直接跟随\"60 天数据科学与机器学习”系列，按天执行从 Python 基础到高级特性的精准复习，确保覆盖所有核心考点。\n- **代码与理论同步掌握**：每天通过仓库提供的具体代码实现（如第 6 天的装饰器与生成器、第 7 天的统计应用），将抽象概念转化为可运行的脚本，提升手写代码能力。\n- **掌握系统设计方法论**：利用仓库中的 ML 系统设计案例研究和专题文章，学会从业务需求出发拆解问题，能够流畅阐述端到端的系统架构。\n- **高效备战面试**：结合配套的技术面试速查表和 YouTube 项目演示，在有限时间内最大化提升实战技能，建立行业对齐的知识库。\n\nComplete-Machine-Learning- 将原本杂乱无章的自学过程转化为一条清晰、可执行的 60 天进阶路径，帮助求职者高效构建起符合工业界标准的机器学习能力闭环。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCoder-World04_Complete-Machine-Learning-_77f25f0c.png","Coder-World04","Ignito","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FCoder-World04_5fcb8361.png","Everything in Tech! Your one stop learning place for anything and everything in Tech",null,"Global","https:\u002F\u002Fnaina0405.substack.com\u002F","https:\u002F\u002Fgithub.com\u002FCoder-World04",589,95,"2026-04-15T10:14:18","MIT","","未说明",{"notes":91,"python":92,"dependencies":93},"该项目并非单一的可执行软件工具，而是一个为期 60 天的数据科学与机器学习学习系列教程。内容涵盖从 Python 基础、统计学、数学基础到 Pandas、Numpy 数据处理，以及回归、分类、聚类等机器学习算法的代码实现。所有代码示例和详细教程均托管在外部链接（Medium 文章）中，README 本身不包含具体的安装脚本、版本锁定文件或硬件加速需求。用户需自行配置通用的 Python 数据科学环境来运行相关代码。","未说明 (内容涉及 Python 基础及数据科学库，隐含需要 Python 环境)",[94,95,96,97],"pandas","numpy","scikit-learn (隐含于回归、分类、聚类算法)","matplotlib\u002Fseaborn (隐含于可视化)",[18],"2026-03-27T02:49:30.150509","2026-04-16T08:19:17.114287",[],[]]