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WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},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,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},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",143909,2,"2026-04-07T11:33:18",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},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。",107888,"2026-04-06T11:32:50",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":10,"last_commit_at":59,"category_tags":60,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[35,15,13,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":76,"owner_website":76,"owner_url":79,"languages":76,"stars":80,"forks":81,"last_commit_at":82,"license":83,"difficulty_score":84,"env_os":85,"env_gpu":86,"env_ram":86,"env_deps":87,"category_tags":90,"github_topics":92,"view_count":32,"oss_zip_url":76,"oss_zip_packed_at":76,"status":17,"created_at":99,"updated_at":100,"faqs":101,"releases":102},5122,"wendelmarques\u002Fmateriais-de-estudos-sobre-data-science-deep-machine-learning","materiais-de-estudos-sobre-data-science-deep-machine-learning","💻 📓 Guia de estudos (iniciante) sobre Inteligência Artificial. Contém trilhas de aprendizagem, canais, cursos , livros etc. ","materiais-de-estudos-sobre-data-science-deep-machine-learning 是一个专为初学者打造的葡萄牙语人工智能学习指南。它系统性地整理了数据科学与机器学习领域的免费及付费资源，涵盖学习路径规划、精选视频频道、在线课程、专业书籍、数学基础、编程语言（Python\u002FR）教程以及实战数据集等全方位内容。\n\n该资源库主要解决了新手在面对海量且碎片化的学习资料时，难以构建清晰知识体系和寻找高质量入门材料的痛点。通过提供结构化的“学习路线图”，它帮助用户从理论基础平滑过渡到项目实战，甚至包含作品集构建指导和自由职业建议，极大地降低了入行门槛。\n\n虽然内容以葡萄牙语为主，但其独特的价值在于作者亲身验证的学习轨迹与经验总结。它不仅罗列链接，更分享了从完成基础课程到参与科研项目的真实成长历程，为学习者提供了可参考的榜样和避坑指南。\n\n非常适合想要入门数据科学、机器学习或深度学习的初学者使用，尤其是熟悉葡萄牙语或希望了解拉美地区 AI 教育资源的开发者与学生。对于需要系统化教学大纲的教育者而言，这也是一份极具参考价值的课程素材库。","\u003Ch1>Materiais de estudos sobre Data Science e Machine Learning (nível iniciante)\u003C\u002Fh1>\n\nO objetivo deste respositório é organizar materiais de estudos - majoritariamente gratuitos e em PT-BR - sobre ciência de dados e inteligência artificial. Inicialmente, o criei para organizar os vários links que encontrei durante minhas pesquisas por materiais. Atualmente, acrescento todos materiais que acredito ser importante para quem está iniciando na área. \n\t\nSinta-se a vontade para adicionar conteúdos.\n\n \n\u003Cbr>\n\u003Cbr>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fwendelmarques_materiais-de-estudos-sobre-data-science-deep-machine-learning_readme_90606e6cc2b8.gif\">\n\u003C\u002Fp>\u003C\u002Ffigcaption>\n\u003Cp align=\"center\">\n  Eu estudando na pandemia\n\u003C\u002Fp>\n\n\n\n\n\n\u003Ch1>LISTA DE CONTEÚDOS\u003C\u002Fh1>\n\n* [Minha trilha\u002F Jornada](#minhaTrilha)\n* [Um pouco sobre a área](#sobre)\n* [Motivação](#motivacao)\n* [Trilhas\u002F Dicas de estudos\u002F Roadmaps](#trilhas)\n* [Conteúdos para utilizar à medida que avançar](#avanco)\n* [Livros gratuidos e pagos](#livros)\n* [Matemática](#mat)\n\t* [Fundamentos de matemática](#f_mat)\n\t* [Matemática para Data Science](#mat_ds)\n\t* [Matemática para Machine\u002FDeep Learning](#mat_ml)\n* [Linguagem Python](#python)\n* [Linguagem R](#r)\n* [Aulas sobre algumas bibliotecas](#bibli)\n\t* [Tensoflow](#tensor)\n\t* [Pandas](#pandas)\n* [Fundamentos IA](#fund_ia)\n\t* [Machine Learning](#ml)\n\t* [Redes Neurais\u002F Deep Learning](#dp)\n\t* [Data Science](#ds)\n* [Canais do Youtube](#yt)\n* [Sites com desafios\u002F problemas](#desafios)\n* [Cursos Udemy\u002F Udacity\u002F Coursera](#cursos)\n\t* [Udemy](#ude)\n\t* [Udacity](#uda)\n\t* [Coursera](#coursera)\n* [Datasets para iniciantes](#dataset)\n* [Repositórios](#rep)\n* [Dicas para montar portifólio](#portifolio)\n* [Freelancer em Data Science](#freela)\n* [Mais ou menos off topic](#off)\n\t* [Representatividade](#repres)\n\t* [Sites úteis para desenvolvedores Python](#uteis)\n\t\t* [Links úteis](#l_uteis)\n\t* [Podcasts](#pod)\n\t* [Open source](#open)\n\t* [Artigos](#art)\n\t* [Possíveis áreas de especialização](#esp)\n\t\t* [Instituições](#inst)\n\n-------------------------------------------\n\u003Ch2 id=\"minhaTrilha\">MINHA TRILHA\u002F JORNADA\u003C\u002Fh2>\n\n\n**Cursos realizados e experiências profissinais\u002F acadêmicas**\n\n* **2020**\n  * [Concluído] [Python para Análise de Dados - Data Science Academy](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=python-fundamentos) [[certificado]](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcertificate\u002F57b4a75247d7dd688d8b456b\u002Fuser\u002F5eb4289ee32fc3728940687c)\n  * [Concluído] [Introdução ao Big Data - Fia Business School (Cousera)](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=python-fundamentos) [[certificado]](https:\u002F\u002Fwww.coursera.org\u002Faccount\u002Faccomplishments\u002Frecords\u002FU6WVRZY6CGQE)\n  * [Pausado] [Bootcamp Completo em Data Science com Python 2020](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fcurso-de-data-science-bootcamp-completo-em-data-science\u002F)\n  * [Pausado] [Programa de cursos integrados Ciência de dados aplicada com Python da University of Michigan](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fdata-science-python)\n  * Estágio na área de Engenharia de Dados: criação de scripts para apoiar os processos de ETL do Observátorio FIEG.\n\n* **2021**\n\t* Iniciação Cientifica na área de Minereação de Dados (2020\u002F2021) \n\t* [Em andamento] [Curso de Python 3 do Básico Ao Avançado - Luiz Miranda (Udemy)](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fpython-3-do-zero-ao-avancado\u002F)\n\t* [Em andamento] [Data Science do Zero - Stack Tecnologias (antigo Minerando Dados)](https:\u002F\u002Fstacktecnologias.com.br\u002Fcurso-data-science-do-zero\u002F)\n\t* Bolsista em um centro de pesquisa em Inteligência Artificial\n\n\n\n**Projetos desenvolvidos até o momento**\n\n* [Mapeamento de médias do ENEM com Folium:](https:\u002F\u002Fgithub.com\u002FWendelMarques\u002Fmapeamento-medias-enem-folium)\nMapeamento de médias do ENEM com Folium (uma biblioteca que facilita a visualização de dados em um mapa). A plotagem foi realizada levando-se em consideração os limites estaduais. Sendo assim, existem 27 grupos de escolas. Foram utilizados dois datasets. [[Medium]](https:\u002F\u002Fmedium.com\u002F@wendelmarques\u002Fmapeamento-de-m%C3%A9dias-do-enem-por-estado-com-folium-bf61fe23a3d8)\n\n* [Painel COVID GYN:](https:\u002F\u002Fgithub.com\u002Fwendelmarques\u002Fpainel-covid-goiania)\nO projeto utiliza abordagens de ciência dos dados para desenvolver um painel de monitoramento dos dados da COVID-19 em relação a casos confirmados e óbitos. O painel contém gráficos e mapa com dados de Goiânia.\n\n\n\n\n\n\u003Ch2 id=\"sobre\">UM POUCO SOBRE A ÁREA\u003C\u002Fh2>\n\n* [A Diferença Entre Inteligência Artificial, Machine Learning e Deep Learning - Data Science Brigade](https:\u002F\u002Fmedium.com\u002Fdata-science-brigade\u002Fa-diferen%C3%A7a-entre-intelig%C3%AAncia-artificial-machine-learning-e-deep-learning-930b5cc2aa42) [Medium]\n* [O que é Ciência de Dados? (QuebraDev)](https:\u002F\u002Fquebradev.com.br\u002Fo-que-e-ciencia-de-dados\u002F) [Podcast]\n* [[Online | DevAIWomen] Bate papo sobre Data Science, Data Analytics e Data Engineer](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=OiE7CVi1QCA)\n* [Quem quer ser uma cientista de dados? com Liliane Scandoleiro - AI Girls Comunidade](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=AcpTqGPExmU)\n* [Como iniciar na carreira de ciência de dados?](https:\u002F\u002Fmedium.com\u002F@mikaeriohana\u002Fcomo-iniciar-na-carreira-de-ci%C3%AAncia-de-dados-9b37aa525181)\n* [10 tipos de profissionais de dados : de engenheiros de dados a big data DevOps e analistas de dados , em qual dessas classificações você se encaixaria?](https:\u002F\u002Fmedium.com\u002F@luis.anderson.sp\u002F10-tipos-de-profissionais-de-dados-de-engenheiros-de-dados-a-big-data-devops-e-analistas-de-94259531270f)  [Medium]\n \n\u003Ch2 id=\"motivacao\">MOTIVAÇÃO\u003C\u002Fh2>\n\n* [\"Ciência de Dados do Zero à Kaggle Kernels Master\" - Leonardo Fereira](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fdata-science-from-zero-kaggle-kernel-master-leonardo-ferreira\u002F) | [Linkedin]\n* [#SprintPrograMaria - Casos Técnicos com Machine Learning](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=qlP98Ph3RaU&t=3109s) | [Youtube]\n* [#SprintPrograMaria | Tudo o que você queria saber sobre IA](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=uc5v-DmiY40)  | [Youtube] \n* [Quem quer ser uma engenheira de dados? com Pamela Santos (AI Girls)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CS5L6CJycuo)  | [Youtube]\n* [Como consegui me tornar uma cientista de dados sem ter formação em tecnologia? com Fernanda Santos (AI Girls)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=eXg2sVIbFdM)  | [Youtube]\n* [Tudo que você precisa saber para trabalhar com Inteligência Artificial - Computer World](https:\u002F\u002Fcomputerworld.com.br\u002F2019\u002F09\u002F29\u002Ftudo-que-voce-precisa-saber-para-trabalhar-com-inteligencia-artificial\u002F)\n* [Como eu me tornei um Engenheiro de Machine Learning\u002FDeep Learning - Arnaldo Gualberto](https:\u002F\u002Fmedium.com\u002Fensina-ai\u002Fcomo-eu-me-tornei-um-engenheiro-de-machine-learning-deep-learning-e5e98b793b66) | [Medium]\n* [Dilemas da escolha profissional - Kizzy Terra](https:\u002F\u002Fmedium.com\u002Fprogramacaodinamica\u002Fdilemas-da-escolha-profissional-49bf206af19a) | [Medium]\n* [Workshop Machine Learning com Fernanda Wanderley](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Jq4aKxaoLGM) | [Youtube]\n* [Trabalho de um cientista de dados - Café Debug\u002F Podcast](https:\u002F\u002Fsoundcloud.com\u002Fcafe-de-bug\u002F33-trabalho-de-um-cientista-de-dados)\n* [Investir em uma carreira em IA vale a pena?](https:\u002F\u002Fblogbrasil.westcon.com\u002Finvestir-em-uma-carreira-em-inteligencia-artificial-vale-a-pena)\n\n\u003Ch2 id=\"trilhas\">TRILHAS\u002F DICAS DE ESTUDOS\u002F ROADMAPS\u003C\u002Fh2>\n\u003Cp>Os conteúdos dos links podem ser utilizados para quem deseja montar um plano de estudos ou simplemente ter uma noção do que é necessário estudar. Me ajudaram a entender mais sobre a área, onde eu estava e por onde deveria seguir, por assim dizer. \u003C\u002Fp>\n\n* [Trilha para Cientista de Dados ou estudante de Machine Learning - Odemir Depieri Jr](https:\u002F\u002Fwww.linkedin.com\u002Ffeed\u002Fupdate\u002Furn:li:activity:6852692981191852032\u002F) | [LinkedIn]\n* [Trilha de Estatística para Data Science - Ronisson Lucas](https:\u002F\u002Fgithub.com\u002Fronissonlucas\u002FTrilha-Estatistica-Data-Science) | [GitHub]\n* [Como criar um PLANO DE ESTUDOS para se tornar um CIENTISTA DE DADOS? (Roadmap Ciência de Dados) - \nProgramação Dinâmica](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=o8NpsLSkKUo&t=464s) | [YouTube]\n* [Minha trilha de estudos para Data Science - Letícia Gerola\u002F Joguei os Dados](https:\u002F\u002Fmedium.com\u002Fjoguei-os-dados\u002Fminha-trilha-de-estudos-para-data-science-bbfddf3941eb) | [Medium]\n* [Como se tornar um Cientista de Dados - \nMarcos Silva](https:\u002F\u002Fmedium.com\u002Fteam-data-stone\u002Fcomo-se-tornar-um-cientista-de-dados-bdda45047be1) | [Medium]\n* [Para iniciar em Data Sciense (DS) - Letícia Silva - ColaboraDados](http:\u002F\u002Fcolaboradados.com.br\u002Fblogposts\u002Fpara-iniciar-em-data-science.html)\n* [Roadmap para Cientista de Dados l MÉTODO VOYAGER](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=oBJNbNn4Wn8)\n* [Seus primeiros passos como Data Scientist: Introdução ao Pandas! - Vinícios Figueiredo](https:\u002F\u002Fmedium.com\u002Fdata-hackers\u002Fuma-introdu%C3%A7%C3%A3o-simples-ao-pandas-1e15eea37fa1) [Medium]\n* [Siga Este Plano Para Aprender a Matemática Para Data Science - Live #31 - Mario Filho - Data Science](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XHnsS87bhuY&t=2599s) | [pt-br] [Youtube] \n* [Python para ciência de dados em 5 passos - Nana Raythz](https:\u002F\u002Fimasters.com.br\u002Fdata\u002Fpython-para-ciencia-de-dados-em-5-passos)\n* [Plano de estudos em machine learning completo](https:\u002F\u002Fgithub.com\u002Fitalojs\u002Fawesome-machine-learning-portugues) | [pt-br] [GitHub]\n* [Dicas de estudos para aprender Machine Learning](https:\u002F\u002Fjuliocprocha.wordpress.com\u002F2017\u002F04\u002F09\u002Fdicas-de-estudos-para-aprender-machine-learning\u002F) | [pt-br]\n* [Data Science e Machine Learning - Uma trilha de aprendizagem](https:\u002F\u002Fmedium.com\u002F@antonio.cavalcanti\u002Fdata-science-e-machine-learning-uma-trilha-de-aprendizagem-8f7207044014) | [pt-br] [Medium]\n* [Trilha de aprendizagem sobre Inteligência Artificial - Wesley Almeida](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Ftrilha-de-aprendizagem-sobre-intelig%C3%AAncia-artificial-wesley-almeida\u002F) | [pt-br][Linkedin]\n* [Listagem de conteúdos de cursos de IA](https:\u002F\u002Figoralcantara.com.br\u002Fcursos\u002F) |  [pt-br] (obs.: não estou indicando os cursos, até porque não os fiz, mas sim indicando a página para que a lista de conteúdos possa ser utilizada para montar um plano de estudos, por exemplo.)\n* [Afinal, o que de matemática você precisa saber para entrar de vez no Machine Learning?](https:\u002F\u002Fmedium.com\u002Flejoaoconte\u002Fafinal-o-que-de-matem%C3%A1tica-voc%C3%AA-precisa-saber-para-entrar-de-vez-no-machine-learning-bf8be40da8cf) | [pt-br][Medium]\n* [Aprender Deep Learning sem gastar nada](https:\u002F\u002Fmedium.com\u002Flejoaoconte\u002Faprenda-deep-learning-sem-gastar-nada-db1c275c0c13) | [pt-br][Medium]\n* [O Segredo Para Dominar o Machine Learning](https:\u002F\u002Fmedium.com\u002Flejoaoconte\u002Fo-segredo-para-dominar-o-machine-learning-b9d60ceef172) | [pt-br][Medium]\n* [A Quarentena do Cientista de Dados, o que estudar?](https:\u002F\u002Fmedium.com\u002Fdata-hackers\u002Fa-quarentena-do-cientista-de-dados-o-que-estudar-f6eefb0a7778) | [pt-br][Medium]\n* [Curso IA (2019) - USP](https:\u002F\u002Fedisciplinas.usp.br\u002Fcourse\u002Fview.php?id=71193https:\u002F\u002Fedisciplinas.usp.br\u002Fcourse\u002Fview.php?id=71193)\n* [Como começar em Data Science? (Seja Um Data Scientist)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=eXg2sVIbFdM)  | [Youtube]\n* [O QUE EU FARIA, SE TIVESSE QUE COMEÇAR DATA SCIENCE HOJE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=VlYDWOfiFuc)  | [Youtube]\n* [Siga esse mapa de estudos e aprenda Data Science (Seja Um Data Scientist)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2g7TBUDkDhM) | [Youtube]\n* [Matemática para Machine Learning - Didática Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MSHpE9dnIho&list=PLyqOvdQmGdTTYHKdxWRmt8oOhMwYhmxkM) | [pt-br] [Youtube] (Dicas de como estudar matemática)\n* [Finalmente: uma fonte segura mostra o salário de um Cientista de Dados no Brasil! (Mario Filho - Data Science)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zsEEFUJo0zQ) | [pt-br] [Youtube] \n* [Siga Este Plano Para Aprender a Matemática Para Data Science - Live #31 - Mario Filho - Data Science](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XHnsS87bhuY&t=2599s) | [pt-br] [Youtube] \n\n\n\n\u003Ch2 id=\"avanco\">CONTEÚDOS PARA UTILIZAR À MEDIDA QUE AVANÇAR NOS ESTUDOS\u003C\u002Fh2>\n\u003Cp>Exercícios e resumos que podem ser aproveitados durante os estudos.\n\n* [Workshop de Ciência de Dados para iniciantes - Nana Raythz](https:\u002F\u002Fgithub.com\u002FNatOps\u002FWorkshop-ciencia-de-dados) | [GitHub]\n* [Plano de estudos em machine learning com conteúdos em português](https:\u002F\u002Fgithub.com\u002Fitalojs\u002Fawesome-machine-learning-portugues) | [GitHub]\n* [Faster Data Science Education](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Foverview): \"Esses micro-cursos são a maneira mais rápida de obter as habilidades necessárias para realizar projetos independentes de ciência de dados.\" | [Kaggle][Inglês]\n\n\u003Ch2 id=\"livros\">LIVROS GRATUITOS E PAGOS\u003C\u002Fh2>\n\nLivros recomendados por profissionais da área. Peguei essas recomendações em lives e artigos no Medium. \n(PT-BR e em inglês)\n\n* [Python Data Science Handbook](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fjakevdp\u002FPythonDataScienceHandbook\u002Fblob\u002Fmaster\u002Fnotebooks\u002FIndex.ipynb#scrollTo=GBz3cb5ZbWT5) - \"Esta é a versão do caderno Jupyter do Python Data Science Handbook de Jake VanderPlas; o conteúdo está disponível no GitHub.\"\n* Python para análise de dados: Tratamento de dados com Pandas, NumPy e IPython\n* Data Science do Zero - Primeiras Regras Com o Python do autor Joel Grus\n* Análise de Dados com Python e Pandas - Daniel Chen\n* Como Mentir com Estatística -  Darrell Heff\n* Mãos à Obra: Aprendizado de Máquina com Scikit-Learn & TensorFlow\n* Estatística Prática para Cientistas de Dados -  Andrew Bruce, Peter C. Bruce\n* Storytelling com Dados: um guia sobre visualização de dados para profissionais de negócio - autora Cole Nussbaumer Knaflic\n* Business Intelligence e Análise de Dados para Gestão do Negócio - autor Dursun Delen\n* Essential Math for Data Science\n* Data Science Para Negócios: O que Você Precisa Saber Sobre Mineração de Dados e Pensamento Analítico de Dados\n* [Deep Learning - Ian Goodfellow\u002F Yoshua Bengio\u002F Aaron Courville](https:\u002F\u002Fwww.deeplearningbook.org\u002F): muito recomendado por profissionais da área. |  [Inglês]\n* [Deep Learning Book - Data Science Academy](http:\u002F\u002Fdeeplearningbook.com.br\u002F) |  [pt-br]\n* [Introdução à Ciência de Dados: Fundamentos e Aplicações - IME\u002F USP\u002F Pedro Morettin\u002F Julio Singer](https:\u002F\u002Fwww.ime.usp.br\u002F~pam\u002Fcdados.pdf)  |  [pt-br]\n* [Como funciona o Deep Learning - ICMC\u002F USP\u002F Moacir Ponti\u002FGabriel Costa](https:\u002F\u002Fsites.icmc.usp.br\u002Fmoacir\u002Fpapers\u002FPonti_Costa_Como-funciona-o-Deep-Learning_2017.pdf)  |  [pt-br]\n\n\u003Ch2 id=\"mat\">MATEMÁTICA\u003C\u002Fh2>\n\n\u003Cp>Pelas minhas pesquisas, uma ótima forma de estudar matemática é por demanda. Por exemplo, estudar conteúdos de matemática à medida que for necessário, porque assim você evita o esquecimento, o que provavelmente aconteceria se estudarmos todos os pré-requisitos antes de iniciar em IA. Porém, é uma boa dar uma olhada em matemática básica antes, se necessário.\u003C\u002Fp>\n\n\u003Ch3 id=\"f_mat\">FUNDAMENTOS DE MATEMÁTICA\u003C\u002Fh3>\n\n* [Fundamentos de Matemática - Didática Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=JVoOF4hjPi8&list=PLyqOvdQmGdTRR5JfSyyeVO4XG7IkBcw5A) | [pt-br][Youtube]\n* [Pré-cálculo do Zuruba - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=huY40aEe30M&list=PL4OAe-tL47sbtMWKh_gOwgAURmja4v7cN) | [pt-br] [Youtube]\n* [Siga Este Plano Para Aprender a Matemática Para Data Science - Live #31 - Mario Filho - Data Science](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XHnsS87bhuY&t=2599s) | [pt-br] [Youtube] \n\n\n\u003Ch3 id=\"mat_ds\">MATEMÁTICA PARA DATA SCIENCE\u003C\u002Fh3>\n\n* [Trilha EstaTiDados](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjdDBZW3EmXe6hO2Rt5Q9I5wzRZ7j7K8P) (as primeiras aulas) | [pt-br] \n* [Curso de Estatística - Gratuito e Ilimitado - EstaTiDados](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjdDBZW3EmXedXYzH-whV58rML91kbwFC) | [Youtube]\n\n\u003Ch3 id=\"mat_ml\">MATEMÁTICA PARA MACHINE\u002FDEEP LEARNING\u003C\u002Fh3>\n\n* [Matemática e Programação para Aprendizado de Máquina](https:\u002F\u002Fmatheusfacure.github.io\u002F2017\u002F01\u002F15\u002Fpre-req-ml\u002F): Uma lista para cobrir rapidamente os pré-requisitos para aprendizado de máquina | [pt-br] [GitHub]\n* [Matemática para Machine Learning - Didática Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MSHpE9dnIho&list=PLyqOvdQmGdTTYHKdxWRmt8oOhMwYhmxkM) | [pt-br] [Youtube]\n* [Revisão de Machine Learning - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=I86gYeLmkT0&list=PL4OAe-tL47sZSCaprWZ6CrJhCTq2gUQCb) | [pt-br] [Youtube]\n* [Afinal, o que de matemática você precisa saber para entrar de vez no Machine Learning?](https:\u002F\u002Fmedium.com\u002Flejoaoconte\u002Fafinal-o-que-C-matem%C3%A1tica-voc%C3%AA-precisa-saber-para-entrar-de-vez-no-machine-learning-bf8be40da8cf) | [pt-br][Medium]\n* [Matemática para Machine Learning](https:\u002F\u002Fmedium.com\u002F@lucasoliveiras\u002Fmatem%C3%A1tica-para-machine-learning-7dc0893ba749) | [pt-br][Medium]\n* [Matemática básicas para Machine Learning](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=veATb_wuZSw&list=PLtyvX7Ge_YluwPLJ_qD9khzp0UU_gu59N&index=1)  | [espanhol] [Youtube]\n\n\u003Ch2 id=\"python\">LINGUAGEM PYTHON\u003C\u002Fh2>\n\n* [Curso de Python no Neps Academy](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU) | [pt-br] (gratuito)\n* [Introdução à Ciência da Computação com Python Parte 1](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fciencia-computacao-python-conceitos)\n* [Python para Análise de Dados - Data Science Academy (DSA)](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=python-fundamentos) | [pt-br] (gratuito)\n* [Curso Python para Machine Learning e Análise de Dados - Didática Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MmSXHCxDwBs&list=PLyqOvdQmGdTR46HUxDA6Ymv4DGsIjvTQ-) | [pt-br] [Youtube]\n* [Curso Python para Iniciantes - Didática Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bHn91RxiTjY&list=PLyqOvdQmGdTSEPnO0DKgHlkXb8x3cyglD) | [pt-br] [Youtube]\n* [O melhor Curso de Python - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bHn91RxiTjY&list=PLyqOvdQmGdTSEPnO0DKgHlkXb8x3cyglD) | [pt-br] [Youtube]\n* [Resolvendo Problemas (C e Python) - Universo Discreto](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=pTnLpcp-o1Q&list=PL-t7zzWJWPtx0UjvAgW-C4U1ZQz1almxx) | [pt-br] [Youtube]\n* [Cursos de Análise de Dados em Python para iniciantes](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqiFjCF_dtcymXtdjwAP4s7tRoW4CYwnH) [pt-br] [Youtube]\n* [Os 35 melhores cursos de Python gratuitos disponíveis pra você - Ninja do Linux](http:\u002F\u002Fninjadolinux.com.br\u002Fos-35-melhores-cursos-de-python-gratuitos\u002F) | [pt-br]\n* [Learn Python for Data Science - Siraj Raval](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU) | [inglês] [Youtube]\n\n\u003Ch2 id=\"r\">LINGUAGEM R\u003C\u002Fh2>\n\n* [Curso R para Machine Learning - Didática Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ID5Ui22F8HQ&list=PLyqOvdQmGdTSqkutrKDaVJlEv-ui1MyK4)  | [pt-br] [Youtube]\n* [Estatística com R - Universidade Federal Fluminense\u002FUFF](http:\u002F\u002Fwww.estatisticacomr.uff.br\u002F?page_id=38)\n* [Curso de Programação R - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=plJw9QFew5A&list=PL4OAe-tL47sbzCgtBTthtX50T30CLToEZ) | [pt-br] [Youtube]\n\n\u003Ch2 id=\"bibli\">AULAS SOBRE ALGUMAS BIBLIOTECAS\u003C\u002Fh2>\n\n\u003Ch3 id=\"tensor\">TENSOFLOW\u003C\u002Fh3>\n\n* [Curso TensorFlow para Iniciantes - Didática Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=JHsnHgb9hDo&list=PLyqOvdQmGdTR_X-BxOJCPIibdjQ_hXycV) | [pt-br] [Youtube]\n* [Intro to TensorFlow - Siraj Raval](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV) | [inglês] [Youtube]\n\n\u003Ch3 id=\"pandas\">PANDAS\u003C\u002Fh3>\n\n* [Pandas em Português - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=eQGEWo1vsKU&list=PL4OAe-tL47sa1McMctk5pdPd5eTAp3drk) | [pt-br] [Youtube]\n* [Uma introdução simples ao Pandas](https:\u002F\u002Fmedium.com\u002Fdata-hackers\u002Fuma-introdu%C3%A7%C3%A3o-simples-ao-pandas-1e15eea37fa1) | [Medium]\n* [Dicas de Pandas - Programação Dinâmica](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MVd1cs7TDgA&list=PL5TJqBvpXQv6SSsEgQrNwpOLTupXPuiMQ) | [pt-br] [Youtube]\n\n\n\u003Ch2 id=\"fund_ia\">FUNDAMENTOS IA\u003C\u002Fh2>\n\n* [Inteligência Artificial - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=m1-Hc5-H22M&list=PL4OAe-tL47sY1OgDs7__GJW8xBpPEeNfC) | [pt-br] [Youtube]\n* [Inteligência Artificial Fundamentos - Data Science Academy (DSA)](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=inteligencia-artificial-fundamentos) | [pt-br] (gratuito)\n* [Minicurso de Introdução à Machine Learning e Inteligência Artificial](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrakQQfctUYUQ2o-9Vop3osTdwWy871D1) | Também em diegonogare.net | [pt-br] [Youtube]\n\n\u003Ch3 id=\"ml\">MACHINE LEARNING\u003C\u002Fh3>\n\n* [Machine Learning para Cientista de Dados - LEG\u002FUFPR\u002FEduardo Ferreira)](http:\u002F\u002Fcursos.leg.ufpr.br\u002FML4all\u002F1parte\u002F)  | [pt-br] [Youtube]\n* [Introducação a Machine Learning - Didática Tech)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ID5Ui22F8HQ&list=PLyqOvdQmGdTSqkutrKDaVJlEv-ui1MyK4)  | [pt-br] [Youtube]\n* [Machine Learning - Zurubabel)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=pKc1J4RB_VQ&list=PL4OAe-tL47sb3xdFBVXs2w1BA2LRN5JU2)  | [pt-br] [Youtube]\n* [Algoritmos de Machine Learning - Didática Tech)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ID5Ui22F8HQ&list=PLyqOvdQmGdTSqkutrKDaVJlEv-ui1MyK4)  | [pt-br] [Youtube]\n* [Learn Machine Learning in 3 months - Siraj Raval](https:\u002F\u002Fgithub.com\u002FllSourcell\u002FLearn_Machine_Learning_in_3_Months) | [inglês] [Youtube]\n* [Machine Learning fo Hacckers - Siraj Raval](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2FOXR16mLow&list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj) | [inglês] [Youtube]\n* [Machine Learning - The University of British Columbia](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=w2OtwL5T1ow&index=1&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6) | [inglês][Youtube]\n* [Tutorial de Machine Learning com Titanic](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1UVizW6eFrY&list=PLwnip85KhroW8Q1JSNbgl06iNPeC0SDkx) | [pt-br] [Youtube]\n\n\n\u003Ch3 id=\"dp\">REDES NEURAIS\u002F DEEP LEARNING\u003C\u002Fh3>\n\n* [Curso Deep Learning - UFG - Deep Learning Brasil](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=6yYUc6nU3Cw&list=PLSZEVLiOtIgF19_cPrvhJC2bWn-dUh1zB) | [pt-br] [Youtube]\n* [Deep Learning em Português - Sandeco](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbmt8d_ueDMVUVlw9VZSdgAIi6W3u-7Zg) | [pt-br] [Youtube]\n* [Curso Deep Learning - UFG\u002FCyberlabs Academy](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tWB_2APSfaY&list=PL95sSdJCNga2vUe_WUFwCOsrPmJnhCCv9) | [pt-br] [Youtube]\n* [Deep Learning em Português - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XL31Z50dLF8&list=PL4OAe-tL47sbzwP6pWR6NQ5ESOt-Ktrih) | [pt-br] [Youtube]\n* [Machine Learning em Python - Programação Dinâmica](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=u8xgqvk16EA&list=PL5TJqBvpXQv5CBxLkdqmou_86syFK7U3Q) | [Youtube]\n* [I.A. e Machine Learning - Universo Discreto](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=p_SmODmFRUw&list=PL-t7zzWJWPtz29fAf72nG3KTJrRdvCmgn) | [pt-br] [Youtube]\n* [ Aulas USP | Inteligência Artificial em saúde: o uso de machine learning - Canal USP](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=EhpebH96Ek0&list=PLAudUnJeNg4tvUFZ8tXQDoAkFAASQzOHm) | [pt-br] [Youtube]\n* [Redes Neurais Artificiais - USP](https:\u002F\u002Fsites.icmc.usp.br\u002Fandre\u002Fresearch\u002Fneural\u002F) | [pt-br]\n* [CS224N: Natural Language Processing with Deep Learning | Winter 2019](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) [inglês][Youtube]\n* [MIT 6.S191: Introduction to Deep Learning](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5v1JnYv_yWs&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) [inglês] [Youtube]\n* [Intro to Deep Learning (Udacity Nanodegree) - Siraj Raval](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3) | [inglês] [Youtube]\n* [Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization) - Deepearning.ai](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CS4cs9xVecg&list=PLkDaE6sCZn6Ec-XTbcX1uRg2_u4xOEky0) | [inglês] [Youtube]\n* [Practical Deep Learning for Coders, v3](https:\u002F\u002Fwww.fast.ai) | [inglês] [Youtube]\n\n\u003Ch3 id=\"ds\">DATA SCIENCE\u003C\u002Fh3>\n\n* [Trilha EstaTiDados – Data Science (Estatística, Negócios, StoryTelling, Dashboards, Machine Learning, Raspagem, Análise de Sentimentos e Big Data)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjdDBZW3EmXe6hO2Rt5Q9I5wzRZ7j7K8P)  | [pt-br]] [Youtube]\n* [(Big Data Fundamentos 2.0 - Data Science Academy (DSA)](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=big-data-fundamentos) | [pt-br] (gratuito)\n* [Ciência de Dados Aplicada - Programação Dinâmica](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DeAuVrhKw58&list=PL5TJqBvpXQv78JrStmN5qp6xoEBT_-3zO) | [Youtube]\n* [Microsoft Power BI para Data Science - Data Science Academy (DSA)](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=microsoft-power-bi-para-data-science) | [pt-br] (gratuito)\n* [Introducação à Ciência de Dados 2.0 - Data Science Academy (DSA)](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=introduo--cincia-de-dados) | [pt-br] (gratuito)\n* [Ciência de Dados do Zuruba - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Y0L0CWTQWDw&list=PL4OAe-tL47sausWpn6QYcETtYltCe3nmp) | [pt-br] [Youtube]\n* [Análise Exploratória de Dados - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4SetLMXelUY&list=PL4OAe-tL47sak0KV_g6VNlPMscQGEAT8t) | [pt-br] [Youtube]\n* [Data Science Your Way - Jose A Dianes\u002F GitHub](https:\u002F\u002Fgithub.com\u002Fjadianes\u002Fdata-science-your-way) | [inglês] [Youtube]\n\n\u003Ch2 id=\"yt\">CANAIS DO YOUTUBE\u003C\u002Fh2>\n\nConteúdos diversos sobre IA.\n* [Sandeco](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCIQne9yW4TvCCNYQLszfXCQ)\n* [Mario Filho - Data Science](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCIFd_i2iwYox1PPm9rD8wFA)\n* [Seja um Data Scientist](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCar5Cr-pVz08GY_6I3RX9bA\u002Fvideos)\n* [Peixebabel](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002FCanalPeixeBabel\u002Fvideos)\n* [PrograMaria](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC6N7eSdbT5DDdrqZVeN0KGw\u002Ffeatured)\n* [AI Girls Comunidade](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC_QxmLPZQRJDjjtN1M-gfnQ\u002Fvideos)\n* [Universo Programado](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCf_kacKyoRRUP0nM3obzFbg)\n* [Programacao Dinâmica](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC70mr11REaCqgKke7DPJoLg)\n* [Universo Discreto](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCEn6kONg6EC_Ylh0RlInsMw\u002Fvideos)\n* [AI Brasil Community](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCS5QayXigvan2fIDGN8UfpQ)\n* [DevelopersBR](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCGhSrtP0-1qq0XPbnMpi2kQ)\n* [Diogo Cortiz](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC5MXrSUoLW0JRd2j7q1ef7Q)\n* [Mikaeri Ohana](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fmiohanars)\n* [O Computeiro](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=d8U7ygZ48Sc)\n* [Vini Mesel - #MaisQueDevs](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mAIRkkItPSc)\n* [Epidemio Fora da Curva - R](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCl5H4LMBYJB1Hu3HgCmgyCg\u002Fvideos)\n\n\u003Ch2 id=\"desafios\">SITES COM DESAFIOS\u002F PROBLEMAS\u003C\u002Fh2>\n\n* [HarckerRank](https:\u002F\u002Fwww.hackerrank.com\u002F)\n* [Kaggle](https:\u002F\u002Fwww.kaggle.com\u002F)\n* [Exercism](https:\u002F\u002Fexercism.io\u002F)\n* [URI Jugde](https:\u002F\u002Fwww.urionlinejudge.com.br\u002Fjudge\u002Fpt\u002Flogin?redirect=%2Fpt)\n\n\u003Ch2 id=\"cursos\">CURSOS DA UDEMY\u002F UDACITY\u002F COURSERA\u003C\u002Fh2>\n\nAlguns gratuitos (sem certificado) e outros pagos.\n\n\n\n\u003Ch3 id=\"ude\">UDEMY\u003C\u002Fh3>\n\n* [Manual Prático do Deep Learning - Redes Neurais Profundas - Arnaldo Gualberto](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fredes-neurais\u002F?referralCode=34C61CFBEACD87D2FD37)\n* [Cursos do Fernando Amaral](https:\u002F\u002Fwww.udemy.com\u002Fuser\u002Ffernando-amaral-3\u002F)\n* [Cursos do Jones Granaty](https:\u002F\u002Fwww.udemy.com\u002Fuser\u002Fjones-granatyr\u002F)  (tbm em iaexpert.com.br)\n* [Deep Learning A-Z™: Hands-On Artificial Neural Networks -  Kirill Eremenko\u002F Hadelin de Ponteves](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fdeeplearning\u002F)\n* [Data Science: Deep Learning in Python - Lazy Programmer Inc.](https:\u002F\u002Fwww.udemy.com\u002Fdata-science-deep-learning-in-python\u002F)\n* [Machine Learning e Data Science com Python - Marcos Castro\u002FGileno Alves](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fmachine-learning-e-data-science-com-python\u002F)\n* [Data Science de A a Z - Extraçao e Exibição dos Dados - Felipe Mafra](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fcurso-data-science-completo\u002F)\n\n\u003Ch3 id=\"coursera\">COURSERA\u003C\u002Fh3>\n\n* [IA para todos - Andrew Ng](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fai-for-everyone-es)\n* [Machine Learning - Stanford](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning)\n* [Introdução à Ciência da Computação com Python Parte 1](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fciencia-computacao-python-conceitos)\n\n\u003Ch3 id=\"uda\">UDACITY\u003C\u002Fh3>\n\n* [Machine Learning - Georgia Tech](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fmachine-learning--ud262)\n* [Introduction to Machine Learning Course](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-machine-learning--ud120)\n* [AWS Machine Learning Scholarship Program](https:\u002F\u002Fwww.udacity.com\u002Fscholarships\u002Faws-machine-learning-scholarship-program?bsft_eid=f4e0e426-7315-28ce-d23c-28ab2213e706&utm_campaign=sch_600_2020-04-30_ndxxx_aws-ml-pre-reg-announcement_global&utm_source=blueshift&utm_medium=email&bsft_clkid=013f9465-2976-455b-9866-39d4d8174f61&bsft_uid=068492e1-225e-49de-8c64-3fcc3f7b0fd3&bsft_mid=d585ba8f-b40f-4048-ae8c-ccaa1672cdf1&bsft_ek=2020-05-03T00:32:38Z&bsft_mime_type=html)\n\n\n\u003Ch2 id=\"rep\">REPÓSITORIOS\u003C\u002Fh2>\n\nRepósitorios com conteúdos, trilhas, dicas e exercícios, ou seja, possuem muuuitos materiais sobre IA.\n\n* [Plano de estudos em machine learning completo](https:\u002F\u002Fgithub.com\u002Fitalojs\u002Fawesome-machine-learning-portugues) [pt-br]\n* [Workshop de Ciência de Dados de iniciantes a intermediário](https:\u002F\u002Fgithub.com\u002FNatOps\u002FWorkshop-ciencia-de-dados\u002F) [pt-br]\n* [Guia do Cientista de Dados das Galáxias](https:\u002F\u002Fgithub.com\u002FPizzaDeDados\u002Fdatascience-pizza\u002F) [pt-br]\n* [The Catcher in the Data Science](https:\u002F\u002Fgithub.com\u002FBrunoComitre\u002Ffavorite-datascience) [pt-br]\n* [Pandas Exercises](https:\u002F\u002Fgithub.com\u002Fguipsamora\u002Fpandas_exercises) (Inglês)\n* [Top-down learning path: Machine Learning for Software Engineers](https:\u002F\u002Fgithub.com\u002Fguipsamora\u002Fpandas_exercises) (Inglês)\n* [Manual Prático do Deep Learning (código-fonte do curso do Arnaldo Gualberto)](https:\u002F\u002Fgithub.com\u002Farnaldog12\u002FManual-Pratico-Deep-Learning) [pt-br]\n* [Materiais de estudos sobre Machine Learning](https:\u002F\u002Fgithub.com\u002Funiville-machine-learning\u002Fmateriais-de-estudo-sobre-machine-learning) [pt-br]\n\n\u003Ch2 id=\"dataset\">DATASETS PARA INICIANTES\u003C\u002Fh2>\n(organizar)\n* [UCI Machine Learning Repository: Data Sets](https:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fdatasets.php)\n* [Google Dataset Search](https:\u002F\u002Fdatasetsearch.research.google.com\u002F)\n\n\n\u003Ch2 id=\"portifolio\">DICAS PARA MONTAR PORTIFÓLIO\u003C\u002Fh2>\n\n* [Os 5 tipos de projetos obrigátorios para o portifólio de Data Science (Seja Um Data Scientist)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=LJrK4B7bNWA) | [Youtube]\nhttps:\u002F\u002Fblog.academiain1.com.br\u002Fbig-data-voce-conhece-os-4-tipos-de-analise-de-dados\u002F\nhttps:\u002F\u002Fblog.toccato.com.br\u002Faprenda-como-fazer-uma-analise-de-dados-eficiente-em-6-passos\u002F (organizar)\nhttps:\u002F\u002Fblog-in1-com-br.cdn.ampproject.org\u002Fv\u002Fs\u002Fblog.in1.com.br\u002Fcomo-criar-uma-modelagem-de-dados-de-forma-eficaz?hs_amp=true&amp_js_v=0.1#referrer=https%3A%2F%2Fwww.google.com&amp_tf=Fonte%3A%20%251%24s&ampshare=https%3A%2F%2Fblog.in1.com.br%2Fcomo-criar-uma-modelagem-de-dados-de-forma-eficaz (organizar)\nhttps:\u002F\u002Fsigmoidal.ai\u002Fguia-basico-de-pre-processamento-de-dados\u002F (organizar)\nhttps:\u002F\u002Fsigmoidal.ai\u002Fcomo-tratar-dados-ausentes-com-pandas\u002F\nhttps:\u002F\u002Fmedium.com\u002Fdatabootcamp\u002Fmeu-checklist-de-projetos-de-aprendizado-de-m%C3%A1quina-34328850d7ab\n\n\u003Ch2 id=\"freela\">FREELANCER EM DATA SCIENCE\u003C\u002Fh2>\n\n* [Como ser Freelancer em Data Science - Mario Filho](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ggdXJJNh7-k)\n\n\n\u003Ch2 id=\"off\">MAIS OU MENOS OFF-TOPIC\u003C\u002Fh2>\n\n\u003Ch3 id=\"repres\">REPRESENTATIVIDADE\u003C\u002Fh3>\n\n* [R-Ladies](https:\u002F\u002Frladies.org\u002F)\n* [Black in AI](https:\u002F\u002Fblackinai.github.io\u002F#\u002Fprograms\u002Fsummer-research-programs)\n* [Pyladies](https:\u002F\u002Fbrasil.pyladies.com\u002F)\n* [Tecnogueto](https:\u002F\u002Ftecnogueto.com.br\u002F)\n* [QuebraDev](https:\u002F\u002Fquebradev.com.br\u002F)\n* [AfroPython](https:\u002F\u002Fafropython.org\u002F)\n* [perifaCode](https:\u002F\u002Fperifacode.com\u002F)\n* [PrograMaria](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC6N7eSdbT5DDdrqZVeN0KGw\u002Ffeatured)\n* [AI Girls Comunidade](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC_QxmLPZQRJDjjtN1M-gfnQ\u002Fvideos)\n* [DevAIWomen - DevelopersBR](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCGhSrtP0-1qq0XPbnMpi2kQ\u002Fsearch?query=DevAIWomen)\n* [BlackPowerData](https:\u002F\u002Fblackpowerdata.com\u002F)\n* [PodProgramar](https:\u002F\u002Fpodprogramar.com.br\u002F)\n\n\n\u003Ch3 id=\"uteis\">SITES ÚTEIS PARA DESENVOLVEDORES PYTHON\u003C\u002Fh3>\n\n* [Pydata](https:\u002F\u002Fpydata.org\u002F)\n* [Python Café](https:\u002F\u002Fpythoncafe.com.br\u002F)\n* [Pyjobs](https:\u002F\u002Fwww.pyjobs.com.br\u002F)\n\n\u003Ch3 id=\"l_uteis\">LINKS ÚTEIS\u003C\u002Fh3>\n\n* [Posts Programação Dinâmica - Medium](https:\u002F\u002Fmedium.com\u002Fprogramacaodinamica)\n* [The Four “Pure” Learning Styles in Machine Learning](https:\u002F\u002Ftowardsdatascience.com\u002Fmachine-learning\u002Fhome)\n* [The Netflix Data Scientist Interview](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-netflix-data-scientist-interview-35093d4c20aa)\n* [Classificação de textos com Python - Alura\u002F Yuri Matheus](https:\u002F\u002Fwww.alura.com.br\u002Fartigos\u002Fclassificando-textos-com-python)\n* [Classificando textos com Redes Neurais e TensorFlow - Deborah Mesquita](https:\u002F\u002Fwww.deborahmesquita.com\u002F2017-05-07\u002Fclassificando-textos-com-redes-neurais-e-tensorflow)\n* [Posts Rafel Sakurai](http:\u002F\u002Frafaelsakurai.github.io\u002F)\n* [ConsuData](https:\u002F\u002Fconsudata.com.br\u002Fblog)\n* [The Four “Pure” Learning Styles in Machine Learning](https:\u002F\u002Ftowardsdatascience.com\u002Fmachine-learning\u002Fhome)\n* [The Netflix Data Scientist Interview](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-netflix-data-scientist-interview-35093d4c20aa)\n* [Classificação de textos com Python - Alura\u002F Yuri Matheus](https:\u002F\u002Fwww.alura.com.br\u002Fartigos\u002Fclassificando-textos-com-python)\n* [Classificando textos com Redes Neurais e TensorFlow - Deborah Mesquita](https:\u002F\u002Fwww.deborahmesquita.com\u002F2017-05-07\u002Fclassificando-textos-com-redes-neurais-e-tensorflow)\n* [Posts Rafel Sakurai](http:\u002F\u002Frafaelsakurai.github.io\u002F)\n* [ConsuData](https:\u002F\u002Fconsudata.com.br\u002Fblog)\n\n\u003Ch3 id=\"pod\">PODCASTS\u003C\u002Fh3>\n\n* [Pizza de Dados](pizzadedados.com\u002F)\n* [PodProgramar](https:\u002F\u002Fmundopodcast.com.br\u002Fpodprogramar\u002F79-ciencia-de-dados\u002F)\n* [Café Debug](https:\u002F\u002Fsoundcloud.com\u002Fcafe-de-bug)\n* [QuebraDev](https:\u002F\u002Fquebradev.com.br\u002F)\n* [Dev na Estrada](https:\u002F\u002Fquebradev.com.br\u002F)\n* [DataHackers](https:\u002F\u002Fdatahackers.com.br\u002Fpodcast)\n* [Hipsters Ponto Tech](https:\u002F\u002Fhipsters.tech\u002F)\n\n\u003Ch3 id=\"open\">OPEN SOURCE\u003C\u002Fh3>\n\n* [Guia: Como contribuir em Open Source](https:\u002F\u002Fwillianjusten.com.br\u002Fguia-como-contribuir-em-open-source\u002F)\n\n\u003Ch3 id=\"art\">ARTIGOS\u003C\u002Fh3>\n\n* [ICML](https:\u002F\u002Ficml.cc\u002F)\n* [ARXIV](https:\u002F\u002Farxiv.org\u002F)\n* [ARXIV](http:\u002F\u002Fwww.arxiv-sanity.com\u002F)\n* [KDNuggets](https:\u002F\u002Fwww.kdnuggets.com\u002Feducation\u002Fonline.html)\n* [OpenAI](https:\u002F\u002Fopenai.com\u002Frequests-for-research\u002F)\n\n\n\n\n\n\n","\u003Ch1>数据科学与机器学习学习资料（入门级）\u003C\u002Fh1>\n\n本仓库旨在整理关于数据科学和人工智能的学习资料，其中大部分为免费资源，并以巴西葡萄牙语提供。最初，我创建这个仓库是为了归类在搜索资料过程中找到的各种链接。如今，我会不断添加我认为对初学者重要的各类资源。\n\n欢迎大家贡献内容。\n\n\u003Cbr>\n\u003Cbr>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fwendelmarques_materiais-de-estudos-sobre-data-science-deep-machine-learning_readme_90606e6cc2b8.gif\">\n\u003C\u002Fp>\u003C\u002Ffigcaption>\n\u003Cp align=\"center\">\n  我在疫情期间学习\n\u003C\u002Fp>\n\n\n\n\n\n\u003Ch1>内容列表\u003C\u002Fh1>\n\n* [我的学习路径\u002F旅程](#minhaTrilha)\n* [关于该领域的简介](#sobre)\n* [学习动机](#motivacao)\n* [学习路径\u002F建议\u002F路线图](#trilhas)\n* [进阶时可使用的资源](#avanco)\n* [免费及付费书籍](#livros)\n* [数学](#mat)\n\t* [数学基础](#f_mat)\n\t* [数据科学中的数学](#mat_ds)\n\t* [机器学习\u002F深度学习中的数学](#mat_ml)\n* [Python语言](#python)\n* [R语言](#r)\n* [部分库的教程](#bibli)\n\t* [TensorFlow](#tensor)\n\t* [Pandas](#pandas)\n* [人工智能基础](#fund_ia)\n\t* [机器学习](#ml)\n\t* [神经网络\u002F深度学习](#dp)\n\t* [数据科学](#ds)\n* [YouTube频道](#yt)\n* [包含挑战\u002F问题的网站](#desafios)\n* [Udemy\u002FUdacity\u002FCoursera课程](#cursos)\n\t* [Udemy](#ude)\n\t* [Udacity](#uda)\n\t* [Coursera](#coursera)\n* [面向初学者的数据集](#dataset)\n* [代码仓库](#rep)\n* [构建作品集的技巧](#portifolio)\n* [数据科学自由职业者](#freela)\n* [略显离题的内容](#off)\n\t* [代表性问题](#repres)\n\t* [对Python开发者有用的网站](#uteis)\n\t\t* [实用链接](#l_uteis)\n\t* [播客](#pod)\n\t* [开源项目](#open)\n\t* [文章](#art)\n\t* [可能的专业方向](#esp)\n\t\t* [相关机构](#inst)\n\n-------------------------------------------\n\u003Ch2 id=\"minhaTrilha\">我的学习路径\u002F旅程\u003C\u002Fh2>\n\n\n**已完成的课程及专业\u002F学术经历**\n\n* **2020**\n  * [已完成] [数据分析Python - 数据科学学院](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=python-fundamentos) [[证书]](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcertificate\u002F57b4a75247d7dd688d8b456b\u002Fuser\u002F5eb4289ee32fc3728940687c)\n  * [已完成] [大数据导论 - FIA商学院（Coursera）](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=python-fundamentos) [[证书]](https:\u002F\u002Fwww.coursera.org\u002Faccount\u002Faccomplishments\u002Frecords\u002FU6WVRZY6CGQE)\n  * [暂停中] [2020年Python数据科学全栈训练营](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fcurso-de-data-science-bootcamp-completo-em-data-science\u002F)\n  * [暂停中] [密歇根大学Python应用数据科学综合课程项目](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fdata-science-python)\n  * 在数据工程领域实习：编写脚本以支持FIEG观测站的ETL流程。\n\n* **2021**\n\t* 数据挖掘领域的科研启蒙项目（2020\u002F2021）\n\t* [进行中] [Luiz Miranda的Python 3从基础到高级课程（Udemy）](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fpython-3-do-zero-ao-avancado\u002F)\n\t* [进行中] [Stack Tecnologias的零基础数据科学课程（原Minerando Dados）](https:\u002F\u002Fstacktecnologias.com.br\u002Fcurso-data-science-do-zero\u002F)\n\t* 人工智能研究中心奖学金获得者\n\n\n\n**目前完成的项目**\n\n* [使用Folium绘制高考平均分地图：](https:\u002F\u002Fgithub.com\u002FWendelMarques\u002Fmapeamento-medias-enem-folium)\n利用Folium库（一种便于在地图上可视化数据的工具）绘制高考平均分地图。绘图时考虑了各州的边界，因此共有27个学校组。使用了两个数据集。[[Medium]](https:\u002F\u002Fmedium.com\u002F@wendelmarques\u002Fmapeamento-de-m%C3%A9dias-do-enem-por-estado-com-folium-bf61fe23a3d8)\n\n* [戈亚尼亚市COVID-19仪表盘：](https:\u002F\u002Fgithub.com\u002Fwendelmarques\u002Fpainel-covid-goiania)\n该项目采用数据科学方法，开发了一个用于监测戈亚尼亚市COVID-19确诊病例和死亡人数的仪表盘。该仪表盘包含图表和地图，展示了当地的相关数据。\n\n\n\n\n\n\u003Ch2 id=\"sobre\">关于该领域的简介\u003C\u002Fh2>\n\n* [人工智能、机器学习和深度学习的区别 - 数据科学旅团](https:\u002F\u002Fmedium.com\u002Fdata-science-brigade\u002Fa-diferen%C3%A7a-entre-intelig%C3%AAncia-artificial-machine-learning-e-deep-learning-930b5cc2aa42) [Medium]\n* [什么是数据科学？（QuebraDev）](https:\u002F\u002Fquebradev.com.br\u002Fo-que-e-ciencia-de-dados\u002F) [播客]\n* [[线上 | DevAIWomen] 关于数据科学、数据分析和数据工程师的对话](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=OiE7CVi1QCA)\n* [谁想成为一名数据科学家？与Liliane Scandoleiro一起 - AI Girls社区](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=AcpTqGPExmU)\n* [如何开始数据科学职业生涯？](https:\u002F\u002Fmedium.com\u002F@mikaeriohana\u002Fcomo-iniciar-na-carreira-de-ci%C3%AAncia-de-dados-9b37aa525181)\n* [10种数据专业人士：从数据工程师到大数据DevOps和数据分析师，你属于哪一类？](https:\u002F\u002Fmedium.com\u002F@luis.anderson.sp\u002F10-tipos-de-profissionais-de-dados-de-engenheiros-de-dados-a-big-data-devops-e-analistas-de-94259531270f)  [Medium]\n \n\u003Ch2 id=\"motivacao\">学习动机\u003C\u002Fh2>\n\n* [\"从零开始的数据科学到Kaggle Kernels大师\" - 莱昂纳多·费雷拉](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fdata-science-from-zero-kaggle-kernel-master-leonardo-ferreira\u002F) | [LinkedIn]\n* [#SprintPrograMaria - 机器学习技术案例](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=qlP98Ph3RaU&t=3109s) | [YouTube]\n* [#SprintPrograMaria | 关于人工智能你所有想知道的](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=uc5v-DmiY40)  | [YouTube] \n* [谁想成为一名数据工程师？与帕梅拉·桑托斯（AI Girls）](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CS5L6CJycuo)  | [YouTube]\n* [我是如何在没有技术背景的情况下成为数据科学家的？与费尔南达·桑托斯（AI Girls）](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=eXg2sVIbFdM)  | [YouTube]\n* [你需要了解的一切：如何从事人工智能工作 - Computer World](https:\u002F\u002Fcomputerworld.com.br\u002F2019\u002F09\u002F29\u002Ftudo-que-voce-precisa-saber-para-trabalhar-com-inteligencia-artificial\u002F)\n* [我是如何成为一名机器学习\u002F深度学习工程师的？ - 阿尔纳尔多·瓜尔贝托](https:\u002F\u002Fmedium.com\u002Fensina-ai\u002Fcomo-eu-me-tornei-um-engenheiro-de-machine-learning-deep-learning-e5e98b793b66) | [Medium]\n* [职业选择的困境 - 基齐·特拉](https:\u002F\u002Fmedium.com\u002Fprogramacaodinamica\u002Fdilemas-da-escolha-profissional-49bf206af19a) | [Medium]\n* [费尔南达·万德利的机器学习工作坊](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Jq4aKxaoLGM) | [YouTube]\n* [数据科学家的工作 - Café Debug\u002F播客](https:\u002F\u002Fsoundcloud.com\u002Fcafe-de-bug\u002F33-trabalho-de-um-cientista-de-dados)\n* [投资人工智能职业值得吗？](https:\u002F\u002Fblogbrasil.westcon.com\u002Finvestir-em-uma-carreira-em-inteligencia-artificial-vale-a-pena)\n\n\u003Ch2 id=\"trilhas\">学习路径\u002F学习建议\u002F路线图\u003C\u002Fh2>\n\u003Cp>这些链接中的内容可供希望制定学习计划或简单了解所需学习内容的人参考。它们帮助我更好地理解了这个领域，让我清楚自己目前所处的位置以及接下来应该往哪个方向发展。\u003C\u002Fp>\n\n* [数据科学家或机器学习学生的成长路径 - 奥德米尔·德皮耶里 Jr](https:\u002F\u002Fwww.linkedin.com\u002Ffeed\u002Fupdate\u002Furn:li:activity:6852692981191852032\u002F) | [LinkedIn]\n* [数据科学统计学习路径 - 罗尼森·卢卡斯](https:\u002F\u002Fgithub.com\u002Fronissonlucas\u002FTrilha-Estatistica-Data-Science) | [GitHub]\n* [如何制定成为数据科学家的学习计划？（数据科学路线图） - 动态编程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=o8NpsLSkKUo&t=464s) | [YouTube]\n* [我的数据科学学习路径 - 莱蒂西亚·杰罗拉\u002F掷骰子](https:\u002F\u002Fmedium.com\u002Fjoguei-os-dados\u002Fminha-trilha-de-estudos-para-data-science-bbfddf3941eb) | [Medium]\n* [如何成为一名数据科学家 - 马科斯·席尔瓦](https:\u002F\u002Fmedium.com\u002Fteam-data-stone\u002Fcomo-se-tornar-um-cientista-de-dados-bdda45047be1) | [Medium]\n* [入门数据科学（DS） - 莱蒂西亚·席尔瓦 - ColaboraDados](http:\u002F\u002Fcolaboradados.com.br\u002Fblogposts\u002Fpara-iniciar-em-data-science.html)\n* [数据科学家路线图 l VOYAGER方法](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=oBJNbNn4Wn8)\n* [作为数据科学家的第一步：Pandas简介！ - 维尼修斯·菲格雷多](https:\u002F\u002Fmedium.com\u002Fdata-hackers\u002Fuma-introdu%C3%A7%C3%A3o-simples-ao-pandas-1e15eea37fa1) | [Medium]\n* [按照这个计划学习数据科学所需的数学知识 - 第31期直播 - 马里奥·菲略 - 数据科学](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XHnsS87bhuY&t=2599s) | [pt-br] [YouTube] \n* [用5步掌握数据科学中的Python - 娜娜·雷思兹](https:\u002F\u002Fimasters.com.br\u002Fdata\u002Fpython-para-ciencia-de-dados-em-5-passos)\n* [完整的机器学习学习计划](https:\u002F\u002Fgithub.com\u002Fitalojs\u002Fawesome-machine-learning-portugues) | [pt-br] [GitHub]\n* [学习机器学习的学习技巧](https:\u002F\u002Fjuliocprocha.wordpress.com\u002F2017\u002F04\u002F09\u002Fdicas-de-estudos-para-aprender-machine-learning\u002F) | [pt-br]\n* [数据科学与机器学习 - 一条学习路径](https:\u002F\u002Fmedium.com\u002F@antonio.cavalcanti\u002Fdata-science-e-machine-learning-uma-trilha-de-aprendizagem-8f7207044014) | [pt-br] [Medium]\n* [人工智能学习路径 - 韦斯利·阿尔梅达](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Ftrilha-de-aprendizagem-sobre-intelig%C3%AAncia-artificial-wesley-almeida\u002F) | [pt-br][LinkedIn]\n* [人工智能课程内容列表](https:\u002F\u002Figoralcantara.com.br\u002Fcursos\u002F) | [pt-br]（注：我并不是在推荐这些课程，因为我本人也没有上过，而是提供这个页面，以便大家可以根据其中的内容来制定学习计划等。）\n* [到底需要掌握哪些数学知识才能真正进入机器学习领域？](https:\u002F\u002Fmedium.com\u002Flejoaoconte\u002Fafinal-o-que-de-matem%C3%A1tica-voc%C3%AA-precisa-saber-para-entrar-de-vez-no-machine-learning-bf8be40da8cf) | [pt-br][Medium]\n* [无需花费即可学习深度学习](https:\u002F\u002Fmedium.com\u002Flejoaoconte\u002Faprenda-deep-learning-sem-gastar-nada-db1c275c0c13) | [pt-br][Medium]\n* [掌握机器学习的秘诀](https:\u002F\u002Fmedium.com\u002Flejoaoconte\u002Fo-segredo-para-dominar-o-machine-learning-b9d60ceef172) | [pt-br][Medium]\n* [数据科学家的隔离期，该学些什么？](https:\u002F\u002Fmedium.com\u002Fdata-hackers\u002Fa-quarentena-do-cientista-de-dados-o-que-estudar-f6eefb0a7778) | [pt-br][Medium]\n* [人工智能课程（2019年） - USP](https:\u002F\u002Fedisciplinas.usp.br\u002Fcourse\u002Fview.php?id=71193https:\u002F\u002Fedisciplinas.usp.br\u002Fcourse\u002Fview.php?id=71193)\n* [如何入门数据科学？（成为一名数据科学家）](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=eXg2sVIbFdM)  | [YouTube]\n* [如果今天要重新开始学习数据科学，我会怎么做](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=VlYDWOfiFuc)  | [YouTube]\n* [遵循这份学习地图，学习数据科学（成为一名数据科学家）](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2g7TBUDkDhM) | [YouTube]\n* [机器学习中的数学 - Didática Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MSHpE9dnIho&list=PLyqOvdQmGdTTYHKdxWRmt8oOhMwYhmxkM) | [pt-br] [YouTube]（关于如何学习数学的建议）\n* [终于：可靠来源公布了巴西数据科学家的薪资！（马里奥·菲略 - 数据科学）](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zsEEFUJo0zQ) | [pt-br] [YouTube] \n* [按照这个计划学习数据科学所需的数学知识 - 第31期直播 - 马里奥·菲略 - 数据科学](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XHnsS87bhuY&t=2599s) | [pt-br] [YouTube] \n\n\n\n\u003Ch2 id=\"avanco\">随着学习进展可使用的内容\u003C\u002Fh2>\n\u003Cp>这些练习和总结可以在学习过程中加以利用。\u003C\u002Fp>\n\n* [面向初学者的数据科学工作坊 - 娜娜·雷思兹](https:\u002F\u002Fgithub.com\u002FNatOps\u002FWorkshop-ciencia-de-dados) | [GitHub]\n* [包含葡萄牙语内容的机器学习学习计划](https:\u002F\u002Fgithub.com\u002Fitalojs\u002Fawesome-machine-learning-portugues) | [GitHub]\n* [更快的数据科学教育](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Foverview)：“这些微型课程是获得独立开展数据科学项目所需技能的最快方式。” | [Kaggle][英语]\n\n\u003Ch2 id=\"livros\">免费与付费书籍\u003C\u002Fh2>\n\n由业内专业人士推荐的书籍。这些推荐来自直播和Medium上的文章。\n（葡萄牙语和英语）\n\n* [Python数据科学手册](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fjakevdp\u002FPythonDataScienceHandbook\u002Fblob\u002Fmaster\u002Fnotebooks\u002FIndex.ipynb#scrollTo=GBz3cb5ZbWT5) - “这是杰克·范德普拉斯的《Python数据科学手册》的Jupyter笔记本版本；内容可在GitHub上获取。”\n* Python数据分析：使用Pandas、NumPy和IPython处理数据\n* 从零开始的数据科学——用Python掌握入门规则，作者乔尔·格鲁斯\n* 使用Python和Pandas进行数据分析——丹尼尔·陈\n* 如何用统计学说谎——达雷尔·赫夫\n* 动手实践：使用Scikit-Learn和TensorFlow的机器学习\n* 数据科学家实用统计学——安德鲁·布鲁斯、彼得·C·布鲁斯\n* 数据故事讲述：面向商业专业人士的数据可视化指南——作者科尔·努斯鲍默·克纳夫利克\n* 商业智能与数据分析在企业管理中的应用——作者杜尔孙·德伦\n* 数据科学必备数学\n* 数据科学在商业中的应用：你需要了解的数据挖掘与分析思维\n* [深度学习——伊恩·古德费洛\u002F约书亚·本吉奥\u002F阿伦·库维尔](https:\u002F\u002Fwww.deeplearningbook.org\u002F)：备受业内人士推荐。| 英文\n* [深度学习书籍——数据科学学院](http:\u002F\u002Fdeeplearningbook.com.br\u002F) | 葡萄牙语\n* [数据科学导论：基础与应用——IME\u002F USP\u002F 佩德罗·莫雷廷\u002F 朱利奥·辛格](https:\u002F\u002Fwww.ime.usp.br\u002F~pam\u002Fcdados.pdf) | 葡萄牙语\n* [深度学习的工作原理——ICMC\u002F USP\u002F 莫阿西尔·蓬蒂\u002F 加布里埃尔·科斯塔](https:\u002F\u002Fsites.icmc.usp.br\u002Fmoacir\u002Fpapers\u002FPonti_Costa_Como-funciona-o-Deep-Learning_2017.pdf) | 葡萄牙语\n\n\u003Ch2 id=\"mat\">数学\u003C\u002Fh2>\n\n\u003Cp>根据我的研究，学习数学的一个好方法是按需学习。例如，在需要时再学习相关数学内容，这样可以避免遗忘，而如果我们先学完所有人工智能所需的前置知识再开始学习，很可能会出现遗忘的情况。不过，如果有必要，最好还是先简单回顾一下基础数学。\u003C\u002Fp>\n\n\u003Ch3 id=\"f_mat\">数学基础\u003C\u002Fh3>\n\n* [数学基础——Didática Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=JVoOF4hjPi8&list=PLyqOvdQmGdTRR5JfSyyeVO4XG7IkBcw5A) | 葡萄牙语[YouTube]\n* [祖鲁巴尔的预科课程——祖鲁巴贝尔](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=huY40aEe30M&list=PL4OAe-tL47sbtMWKh_gOwgAURmja4v7cN) | 葡萄牙语 [YouTube]\n* [按照这个计划学习数据科学所需的数学——第31期直播——马里奥·菲略——数据科学](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XHnsS87bhuY&t=2599s) | 葡萄牙语 [YouTube]\n\n\n\u003Ch3 id=\"mat_ds\">数据科学中的数学\u003C\u002Fh3>\n\n* [EstaTiDados学习路径](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjdDBZW3EmXe6hO2Rt5Q9I5wzRZ7j7K8P)（前几节课） | 葡萄牙语\n* [EstaTiDados提供的免费且无限量的统计学课程](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjdDBZW3EmXedXYzH-whV58rML91kbwFC) | YouTube\n\n\u003Ch3 id=\"mat_ml\">机器学习\u002F深度学习中的数学\u003C\u002Fh3>\n\n* [机器学习的数学与编程](https:\u002F\u002Fmatheusfacure.github.io\u002F2017\u002F01\u002F15\u002Fpre-req-ml\u002F)：一份快速覆盖机器学习前置知识的清单 | 葡萄牙语 [GitHub]\n* [机器学习中的数学——Didática Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MSHpE9dnIho&list=PLyqOvdQmGdTTYHKdxWRmt8oOhMwYhmxkM) | 葡萄牙语 [YouTube]\n* [机器学习复习——祖鲁巴贝尔](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=I86gYeLmkT0&list=PL4OAe-tL47sZSCaprWZ6CrJhCTq2gUQCb) | 葡萄牙语 [YouTube]\n* [到底你需要掌握哪些数学知识才能真正进入机器学习领域？](https:\u002F\u002Fmedium.com\u002Flejoaoconte\u002Fafinal-o-que-C-matem%C3%A1tica-voc%C3%AA-precisa-saber-para-entrar-de-vez-no-machine-learning-bf8be40da8cf) | 葡萄牙语 [Medium]\n* [机器学习中的数学](https:\u002F\u002Fmedium.com\u002F@lucasoliveiras\u002Fmatem%C3%A1tica-para-machine-learning-7dc0893ba749) | 葡萄牙语 [Medium]\n* [机器学习的基础数学](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=veATb_wuZSw&list=PLtyvX7Ge_YluwPLJ_qD9khzp0UU_gu59N&index=1) | 西班牙语 [YouTube]\n\n\u003Ch2 id=\"python\">Python语言\u003C\u002Fh2>\n\n* [Neps Academy的Python课程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU) | 葡萄牙语（免费）\n* [使用Python的计算机科学导论 第一部分](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fciencia-computacao-python-conceitos)\n* [数据科学学院（DSA）的Python数据分析课程](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=python-fundamentos) | 葡萄牙语（免费）\n* [Didática Tech的机器学习与数据分析Python课程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MmSXHCxDwBs&list=PLyqOvdQmGdTR46HUxDA6Ymv4DGsIjvTQ-) | 葡萄牙语 [YouTube]\n* [Didática Tech的Python初学者课程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bHn91RxiTjY&list=PLyqOvdQmGdTSEPnO0DKgHlkXb8x3cyglD) | 葡萄牙语 [YouTube]\n* [最好的Python课程——祖鲁巴贝尔](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bHn91RxiTjY&list=PLyqOvdQmGdTSEPnO0DKgHlkXb8x3cyglD) | 葡萄牙语 [YouTube]\n* [解决问题（C和Python）——离散宇宙](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=pTnLpcp-o1Q&list=PL-t7zzWJWPtx0UjvAgW-C4U1ZQz1almxx) | 葡萄牙语 [YouTube]\n* [面向初学者的Python数据分析课程系列](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqiFjCF_dtcymXtdjwAP4s7tRoW4CYwnH) | 葡萄牙语 [YouTube]\n* [为你准备的35门最佳免费Python课程——Linux忍者](http:\u002F\u002Fninjadolinux.com.br\u002Fos-35-melhores-cursos-de-python-gratuitos\u002F) | 葡萄牙语\n* [学习Python用于数据科学——Siraj Raval](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU) | 英语 [YouTube]\n\n\u003Ch2 id=\"r\">R语言\u003C\u002Fh2>\n\n* [Didática Tech的R语言机器学习课程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ID5Ui22F8HQ&list=PLyqOvdQmGdTSqkutrKDaVJlEv-ui1MyK4) | 葡萄牙语 [YouTube]\n* [弗鲁米嫩塞联邦大学\u002FUFF的R语言统计学课程](http:\u002F\u002Fwww.estatisticacomr.uff.br\u002F?page_id=38)\n* [祖鲁巴贝尔的R语言编程课程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=plJw9QFew5A&list=PL4OAe-tL47sbzCgtBTthtX50T30CLToEZ) | 葡萄牙语 [YouTube]\n\n\u003Ch2 id=\"bibli\">部分库的相关课程\u003C\u002Fh2>\n\n\u003Ch3 id=\"tensor\">TensorFlow\u003C\u002Fh3>\n\n* [Didática Tech的TensorFlow初学者课程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=JHsnHgb9hDo&list=PLyqOvdQmGdTR_X-BxOJCPIibdjQ_hXycV) | 葡萄牙语 [YouTube]\n* [TensorFlow简介——Siraj Raval](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV) | 英语 [YouTube]\n\n\u003Ch3 id=\"pandas\">Pandas\u003C\u002Fh3>\n\n* [Pandas 葡萄牙语教程 - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=eQGEWo1vsKU&list=PL4OAe-tL47sa1McMctk5pdPd5eTAp3drk) | [pt-br] [Youtube]\n* [Pandas 简单入门](https:\u002F\u002Fmedium.com\u002Fdata-hackers\u002Fuma-introdu%C3%A7%C3%A3o-simples-ao-pandas-1e15eea37fa1) | [Medium]\n* [Pandas 技巧 - 动态编程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MVd1cs7TDgA&list=PL5TJqBvpXQv6SSsEgQrNwpOLTupXPuiMQ) | [pt-br] [Youtube]\n\n\n\u003Ch2 id=\"fund_ia\">人工智能基础\u003C\u002Fh2>\n\n* [人工智能 - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=m1-Hc5-H22M&list=PL4OAe-tL47sY1OgDs7__GJW8xBpPEeNfC) | [pt-br] [Youtube]\n* [人工智能基础 - 数据科学学院 (DSA)](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=inteligencia-artificial-fundamentos) | [pt-br]（免费）\n* [机器学习与人工智能入门迷你课程](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrakQQfctUYUQ2o-9Vop3osTdwWy871D1) | 也可见于 diegonogare.net | [pt-br] [Youtube]\n\n\u003Ch3 id=\"ml\">机器学习\u003C\u002Fh3>\n\n* [面向数据科学家的机器学习 - LEG\u002FUFPR\u002FEduardo Ferreira)](http:\u002F\u002Fcursos.leg.ufpr.br\u002FML4all\u002F1parte\u002F)  | [pt-br] [Youtube]\n* [机器学习入门 - Didática Tech)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ID5Ui22F8HQ&list=PLyqOvdQmGdTSqkutrKDaVJlEv-ui1MyK4)  | [pt-br] [Youtube]\n* [机器学习 - Zurubabel)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=pKc1J4RB_VQ&list=PL4OAe-tL47sb3xdFBVXs2w1BA2LRN5JU2)  | [pt-br] [Youtube]\n* [机器学习算法 - Didática Tech)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ID5Ui22F8HQ&list=PLyqOvdQmGdTSqkutrKDaVJlEv-ui1MyK4)  | [pt-br] [Youtube]\n* [3个月内学会机器学习 - Siraj Raval](https:\u002F\u002Fgithub.com\u002FllSourcell\u002FLearn_Machine_Learning_in_3_Months) | [英语] [Youtube]\n* [黑客的机器学习 - Siraj Raval](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2FOXR16mLow&list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj) | [英语] [Youtube]\n* [不列颠哥伦比亚大学的机器学习](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=w2OtwL5T1ow&index=1&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6) | [英语][Youtube]\n* [泰坦尼克号数据集的机器学习教程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1UVizW6eFrY&list=PLwnip85KhroW8Q1JSNbgl06iNPeC0SDkx) | [pt-br] [Youtube]\n\n\n\u003Ch3 id=\"dp\">神经网络\u002F深度学习\u003C\u002Fh3>\n\n* [UFG 深度学习课程 - Deep Learning Brasil](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=6yYUc6nU3Cw&list=PLSZEVLiOtIgF19_cPrvhJC2bWn-dUh1zB) | [pt-br] [Youtube]\n* [葡萄牙语深度学习 - Sandeco](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbmt8d_ueDMVUVlw9VZSdgAIi6W3u-7Zg) | [pt-br] [Youtube]\n* [UFG\u002FCyberlabs Academy 深度学习课程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tWB_2APSfaY&list=PL95sSdJCNga2vUe_WUFwCOsrPmJnhCCv9) | [pt-br] [Youtube]\n* [葡萄牙语深度学习 - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XL31Z50dLF8&list=PL4OAe-tL47sbzwP6pWR6NQ5ESOt-Ktrih) | [pt-br] [Youtube]\n* [Python 中的机器学习 - 动态编程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=u8xgqvk16EA&list=PL5TJqBvpXQv5CBxLkdqmou_86syFK7U3Q) | [Youtube]\n* [人工智能与机器学习 - 离散宇宙](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=p_SmODmFRUw&list=PL-t7zzWJWPtz29fAf72nG3KTJrRdvCmgn) | [pt-br] [Youtube]\n* [圣保罗大学课程 | 健康领域的人工智能：机器学习的应用 - Canal USP](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=EhpebH96Ek0&list=PLAudUnJeNg4tvUFZ8tXQDoAkFAASQzOHm) | [pt-br] [Youtube]\n* [圣保罗大学人工神经网络](https:\u002F\u002Fsites.icmc.usp.br\u002Fandre\u002Fresearch\u002Fneural\u002F) | [pt-br]\n* [CS224N：使用深度学习进行自然语言处理 | 2019年冬季](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) [英语][Youtube]\n* [MIT 6.S191：深度学习导论](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5v1JnYv_yWs&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) [英语] [Youtube]\n* [深度学习入门（Udacity Nanodegree）- Siraj Raval](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3) | [英语] [Youtube]\n* [神经网络与深度学习（深度学习专项课程第一课）- Deepearning.ai](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CS4cs9xVecg&list=PLkDaE6sCZn6Ec-XTbcX1uRg2_u4xOEky0) | [英语] [Youtube]\n* [实用深度学习（面向编码人员，第3版）](https:\u002F\u002Fwww.fast.ai) | [英语] [Youtube]\n\n\u003Ch3 id=\"ds\">数据科学\u003C\u002Fh3>\n\n* [EstaTiDados 学习路径 – 数据科学（统计学、商业、故事讲述、仪表盘、机器学习、网页抓取、情感分析和大数据）](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjdDBZW3EmXe6hO2Rt5Q9I5wzRZ7j7K8P)  | [pt-br]] [Youtube]\n* [(大数据基础 2.0 - 数据科学学院 (DSA)](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=big-data-fundamentos) | [pt-br]（免费）\n* [应用数据科学 - 动态编程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DeAuVrhKw58&list=PL5TJqBvpXQv78JrStmN5qp6xoEBT_-3zO) | [Youtube]\n* [微软 Power BI 用于数据科学 - 数据科学学院 (DSA)](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=microsoft-power-bi-para-data-science) | [pt-br]（免费）\n* [数据科学入门 2.0 - 数据科学学院 (DSA)](https:\u002F\u002Fwww.datascienceacademy.com.br\u002Fcourse?courseid=introduo--cincia-de-dados) | [pt-br]（免费）\n* [Zuruba 的数据科学 - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Y0L0CWTQWDw&list=PL4OAe-tL47sausWpn6QYcETtYltCe3nmp) | [pt-br] [Youtube]\n* [数据探索性分析 - Zurubabel](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4SetLMXelUY&list=PL4OAe-tL47sak0KV_g6VNlPMscQGEAT8t) | [pt-br] [Youtube]\n* [按你的方式做数据科学 - Jose A Dianes\u002FGitHub](https:\u002F\u002Fgithub.com\u002Fjadianes\u002Fdata-science-your-way) | [英语] [Youtube]\n\n\u003Ch2 id=\"yt\">YouTube 频道\u003C\u002Fh2>\n\n关于人工智能的各种内容。\n* [Sandeco](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCIQne9yW4TvCCNYQLszfXCQ)\n* [Mario Filho - 数据科学](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCIFd_i2iwYox1PPm9rD8wFA)\n* [成为一名数据科学家](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCar5Cr-pVz08GY_6I3RX9bA\u002Fvideos)\n* [Peixebabel](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002FCanalPeixeBabel\u002Fvideos)\n* [PrograMaria](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC6N7eSdbT5DDdrqZVeN0KGw\u002Ffeatured)\n* [AI Girls 社区](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC_QxmLPZQRJDjjtN1M-gfnQ\u002Fvideos)\n* [程序化宇宙](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCf_kacKyoRRUP0nM3obzFbg)\n* [动态编程](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC70mr11REaCqgKke7DPJoLg)\n* [离散宇宙](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCEn6kONg6EC_Ylh0RlInsMw\u002Fvideos)\n* [AI 巴西社区](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCS5QayXigvan2fIDGN8UfpQ)\n* [DevelopersBR](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCGhSrtP0-1qq0XPbnMpi2kQ)\n* [Diogo Cortiz](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC5MXrSUoLW0JRd2j7q1ef7Q)\n* [Mikaeri Ohana](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fmiohanars)\n* [The Computeiro](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=d8U7ygZ48Sc)\n* [Vini Mesel - #MaisQueDevs](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mAIRkkItPSc)\n* [疫情之外 - R](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCl5H4LMBYJB1Hu3HgCmgyCg\u002Fvideos)\n\n\u003Ch2 id=\"desafios\">包含挑战\u002F问题的网站\u003C\u002Fh2>\n\n* [哈克Rank](https:\u002F\u002Fwww.hackerrank.com\u002F)\n* [Kaggle](https:\u002F\u002Fwww.kaggle.com\u002F)\n* [Exercism](https:\u002F\u002Fexercism.io\u002F)\n* [URI 判题](https:\u002F\u002Fwww.urionlinejudge.com.br\u002Fjudge\u002Fpt\u002Flogin?redirect=%2Fpt)\n\n\u003Ch2 id=\"cursos\">优德米\u002F优达学城\u002F Coursera 课程\u003C\u002Fh2>\n\n有些是免费的（无证书），另一些则需要付费。\n\n\n\n\u003Ch3 id=\"ude\">优德米\u003C\u002Fh3>\n\n* [深度学习实用手册——深度神经网络——阿纳尔多·瓜尔贝托](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fredes-neurais\u002F?referralCode=34C61CFBEACD87D2FD37)\n* [费尔南多·阿马拉尔的课程](https:\u002F\u002Fwww.udemy.com\u002Fuser\u002Ffernando-amaral-3\u002F)\n* [琼斯·格拉纳蒂的课程](https:\u002F\u002Fwww.udemy.com\u002Fuser\u002Fjones-granatyr\u002F)  (也在 iaexpert.com.br 上)\n* [深度学习 A-Z™：动手实践人工神经网络 - 基里尔·埃雷缅科\u002F 阿德林·德·蓬特韦斯](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fdeeplearning\u002F)\n* [数据科学：Python 中的深度学习 - Lazy Programmer Inc.](https:\u002F\u002Fwww.udemy.com\u002Fdata-science-deep-learning-in-python\u002F)\n* [机器学习与数据科学：Python 实战 - 马科斯·卡斯特罗\u002F吉列诺·阿尔维斯](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fmachine-learning-e-data-science-com-python\u002F)\n* [数据科学从 A 到 Z：数据提取与展示 - 费利佩·马夫拉](https:\u002F\u002Fwww.udemy.com\u002Fcourse\u002Fcurso-data-science-completo\u002F)\n\n\u003Ch3 id=\"coursera\">Coursera\u003C\u002Fh3>\n\n* [人工智能人人皆可学 - 吴恩达](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fai-for-everyone-es)\n* [斯坦福大学机器学习课程](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning)\n* [Python 计算机科学导论 第一部分](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fciencia-computacao-python-conceitos)\n\n\u003Ch3 id=\"uda\">优达学城\u003C\u002Fh3>\n\n* [乔治亚理工学院机器学习课程](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fmachine-learning--ud262)\n* [机器学习入门课程](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-machine-learning--ud120)\n* [AWS 机器学习奖学金项目](https:\u002F\u002Fwww.udacity.com\u002Fscholarships\u002Faws-machine-learning-scholarship-program?bsft_eid=f4e0e426-7315-28ce-d23c-28ab2213e706&utm_campaign=sch_600_2020-04-30_ndxxx_aws-ml-pre-reg-announcement_global&utm_source=blueshift&utm_medium=email&bsft_clkid=013f9465-2976-455b-9866-39d4d8174f61&bsft_uid=068492e1-225e-49de-8c64-3fcc3f7b0fd3&bsft_mid=d585ba8f-b40f-4048-ae8c-ccaa1672cdf1&bsft_ek=2020-05-03T00:32:38Z&bsft_mime_type=html)\n\n\n\u003Ch2 id=\"rep\">代码仓库\u003C\u002Fh2>\n\n这些仓库包含内容、学习路径、技巧和练习，也就是说，它们拥有大量关于人工智能的资料。\n\n* [完整的机器学习学习计划](https:\u002F\u002Fgithub.com\u002Fitalojs\u002Fawesome-machine-learning-portugues) [葡萄牙语]\n* [从入门到中级的数据科学工作坊](https:\u002F\u002Fgithub.com\u002FNatOps\u002FWorkshop-ciencia-de-dados\u002F) [葡萄牙语]\n* [银河系数据科学家指南](https:\u002F\u002Fgithub.com\u002FPizzaDeDados\u002Fdatascience-pizza\u002F) [葡萄牙语]\n* [数据科学中的捕手](https:\u002F\u002Fgithub.com\u002FBrunoComitre\u002Ffavorite-datascience) [葡萄牙语]\n* [Pandas 练习](https:\u002F\u002Fgithub.com\u002Fguipsamora\u002Fpandas_exercises) (英语)\n* [自顶向下学习路径：面向软件工程师的机器学习](https:\u002F\u002Fgithub.com\u002Fguipsamora\u002Fpandas_exercises) (英语)\n* [深度学习实用手册（阿纳尔多·瓜尔贝托课程源代码）](https:\u002F\u002Fgithub.com\u002Farnaldog12\u002FManual-Pratico-Deep-Learning) [葡萄牙语]\n* [机器学习学习资料](https:\u002F\u002Fgithub.com\u002Funiville-machine-learning\u002Fmateriais-de-estudo-sobre-machine-learning) [葡萄牙语]\n\n\u003Ch2 id=\"dataset\">初学者用数据集\u003C\u002Fh2>\n(整理)\n* [UCI 机器学习库：数据集](https:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fdatasets.php)\n* [谷歌数据集搜索](https:\u002F\u002Fdatasetsearch.research.google.com\u002F)\n\n\n\u003Ch2 id=\"portifolio\">搭建作品集的建议\u003C\u002Fh2>\n\n* [数据科学作品集必备的 5 种项目类型（成为数据科学家）](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=LJrK4B7bNWA) | [YouTube]\nhttps:\u002F\u002Fblog.academiain1.com.br\u002Fbig-data-voce-conhece-os-4-tipos-de-analise-de-dados\u002F\nhttps:\u002F\u002Fblog.toccato.com.br\u002Faprenda-como-fazer-uma-analise-de-dados-eficiente-em-6-passos\u002F (整理)\nhttps:\u002F\u002Fblog-in1-com-br.cdn.ampproject.org\u002Fv\u002Fs\u002Fblog.in1.com.br\u002Fcomo-criar-uma-modelagem-de-dados-de-forma-eficaz?hs_amp=true&amp_js_v=0.1#referrer=https%3A%2F%2Fwww.google.com&amp_tf=Fonte%3A%20%251%24s&ampshare=https%3A%2F%2Fblog.in1.com.br%2Fcomo-criar-uma-modelagem-de-dados-de-forma-eficaz (整理)\nhttps:\u002F\u002Fsigmoidal.ai\u002Fguia-basico-de-pre-processamento-de-dados\u002F (整理)\nhttps:\u002F\u002Fsigmoidal.ai\u002Fcomo-tratar-dados-ausentes-com-pandas\u002F\nhttps:\u002F\u002Fmedium.com\u002Fdatabootcamp\u002Fmeu-checklist-de-projetos-de-aprendizado-de-m%C3%A1quina-34328850d7ab\n\n\u003Ch2 id=\"freela\">数据科学自由职业者\u003C\u002Fh2>\n\n* [如何成为数据科学自由职业者 - 马里奥·菲略](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ggdXJJNh7-k)\n\n\n\u003Ch2 id=\"off\">或多或少偏离主题的内容\u003C\u002Fh2>\n\n\u003Ch3 id=\"repres\">代表性\u003C\u002Fh3>\n\n* [R 女士](https:\u002F\u002Frladies.org\u002F)\n* [AI 中的黑人](https:\u002F\u002Fblackinai.github.io\u002F#\u002Fprograms\u002Fsummer-research-programs)\n* [PyLadies](https:\u002F\u002Fbrasil.pyladies.com\u002F)\n* [Tecnogueto](https:\u002F\u002Ftecnogueto.com.br\u002F)\n* [QuebraDev](https:\u002F\u002Fquebradev.com.br\u002F)\n* [AfroPython](https:\u002F\u002Fafropython.org\u002F)\n* [perifaCode](https:\u002F\u002Fperifacode.com\u002F)\n* [PrograMaria](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC6N7eSdbT5DDdrqZVeN0KGw\u002Ffeatured)\n* [AI Girls 社区](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC_QxmLPZQRJDjjtN1M-gfnQ\u002Fvideos)\n* [DevAIWomen - DevelopersBR](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCGhSrtP0-1qq0XPbnMpi2kQ\u002Fsearch?query=DevAIWomen)\n* [BlackPowerData](https:\u002F\u002Fblackpowerdata.com\u002F)\n* [PodProgramar](https:\u002F\u002Fpodprogramar.com.br\u002F)\n\n\n\u003Ch3 id=\"uteis\">对 Python 开发者有用网站\u003C\u002Fh3>\n\n* [Pydata](https:\u002F\u002Fpydata.org\u002F)\n* [Python Café](https:\u002F\u002Fpythoncafe.com.br\u002F)\n* [Pyjobs](https:\u002F\u002Fwww.pyjobs.com.br\u002F)\n\n\u003Ch3 id=\"l_uteis\">有用链接\u003C\u002Fh3>\n\n* [动态规划文章 - Medium](https:\u002F\u002Fmedium.com\u002Fprogramacaodinamica)\n* [机器学习中的四种“纯粹”学习风格](https:\u002F\u002Ftowardsdatascience.com\u002Fmachine-learning\u002Fhome)\n* [Netflix 数据科学家访谈](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-netflix-data-scientist-interview-35093d4c20aa)\n* [使用 Python 进行文本分类 - Alura\u002F 尤里·马修斯](https:\u002F\u002Fwww.alura.com.br\u002Fartigos\u002Fclassificando-textos-com-python)\n* [使用神经网络和 TensorFlow 进行文本分类 - 黛博拉·梅斯基塔](https:\u002F\u002Fwww.deborahmesquita.com\u002F2017-05-07\u002Fclassificando-textos-com-redes-neurais-e-tensorflow)\n* [拉斐尔·萨库赖的文章](http:\u002F\u002Frafaelsakurai.github.io\u002F)\n* [ConsuData](https:\u002F\u002Fconsudata.com.br\u002Fblog)\n* [机器学习中的四种“纯粹”学习风格](https:\u002F\u002Ftowardsdatascience.com\u002Fmachine-learning\u002Fhome)\n* [Netflix 数据科学家访谈](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-netflix-data-scientist-interview-35093d4c20aa)\n* [使用 Python 进行文本分类 - Alura\u002F 尤里·马修斯](https:\u002F\u002Fwww.alura.com.br\u002Fartigos\u002Fclassificando-textos-com-python)\n* [使用神经网络和 TensorFlow 进行文本分类 - 黛博拉·梅斯基塔](https:\u002F\u002Fwww.deborahmesquita.com\u002F2017-05-07\u002Fclassificando-textos-com-redes-neurais-e-tensorflow)\n* [拉斐尔·萨库赖的文章](http:\u002F\u002Frafaelsakurai.github.io\u002F)\n* [ConsuData](https:\u002F\u002Fconsudata.com.br\u002Fblog)\n\n\u003Ch3 id=\"pod\">播客\u003C\u002Fh3>\n\n* [数据披萨](pizzadedados.com\u002F)\n* [编程播客](https:\u002F\u002Fmundopodcast.com.br\u002Fpodprogramar\u002F79-ciencia-de-dados\u002F)\n* [调试咖啡](https:\u002F\u002Fsoundcloud.com\u002Fcafe-de-bug)\n* [开发者解惑](https:\u002F\u002Fquebradev.com.br\u002F)\n* [在路上的开发者](https:\u002F\u002Fquebradev.com.br\u002F)\n* [数据黑客](https:\u002F\u002Fdatahackers.com.br\u002Fpodcast)\n* [技术极客点](https:\u002F\u002Fhipsters.tech\u002F)\n\n\u003Ch3 id=\"open\">开源项目\u003C\u002Fh3>\n\n* [指南：如何参与开源贡献](https:\u002F\u002Fwillianjusten.com.br\u002Fguia-como-contribuir-em-open-source\u002F)\n\n\u003Ch3 id=\"art\">文章\u003C\u002Fh3>\n\n* [ICML](https:\u002F\u002Ficml.cc\u002F)\n* [ArXiv](https:\u002F\u002Farxiv.org\u002F)\n* [ArXiv 搜索](http:\u002F\u002Fwww.arxiv-sanity.com\u002F)\n* [KDnuggets](https:\u002F\u002Fwww.kdnuggets.com\u002Feducation\u002Fonline.html)\n* [OpenAI](https:\u002F\u002Fopenai.com\u002Frequests-for-research\u002F)","# materiais-de-estudos-sobre-data-science-deep-machine-learning 快速上手指南\n\n**注意**：本项目并非一个可安装的软件工具或代码库，而是一个**精选学习资源清单**。它主要汇集了面向初学者的数据科学（Data Science）和机器学习（Machine Learning）学习资料，且内容绝大多数为**葡萄牙语（PT-BR）**。\n\n因此，本指南将指导你如何获取并利用这份资源清单来构建你的学习路径，而非执行传统的软件安装命令。\n\n## 1. 环境准备\n\n由于本项目是文档和资源链接的集合，无需特定的操作系统或复杂的依赖环境。你只需要具备以下基础条件即可开始：\n\n*   **硬件要求**：任意能运行浏览器的电脑（Windows, macOS, Linux）。\n*   **前置知识**：\n    *   基础的计算机操作能力。\n    *   **语言提示**：原仓库资源主要为葡萄牙语。如果你不熟悉葡语，建议配合浏览器翻译插件（如 Chrome 自带翻译或 DeepL）使用，或者将其作为寻找通用技术概念（如 Python, Pandas, TensorFlow）的索引，再结合中文社区资料深入学习。\n*   **推荐工具**：\n    *   Git（用于克隆仓库到本地）\n    *   Markdown 阅读器（可选，用于在本地舒适地阅读 `README.md`）\n    *   Python 环境（当你跟随清单中的教程开始实践时需要，建议安装 [Anaconda](https:\u002F\u002Fwww.anaconda.com\u002F) 或 [Miniconda](https:\u002F\u002Fdocs.conda.io\u002Fen\u002Flatest\u002Fminiconda.html)）\n\n## 2. 获取资源步骤\n\n你可以通过以下两种方式访问这份学习清单：\n\n### 方式一：在线直接浏览（推荐）\n直接访问 GitHub 仓库页面查看整理好的目录和链接：\n*   仓库地址：[materiais-de-estudos-sobre-data-science-deep-machine-learning](https:\u002F\u002Fgithub.com\u002FWendelMarques\u002Fmateriais-de-estudos-sobre-data-science-deep-machine-learning)\n\n### 方式二：克隆到本地\n如果你希望离线阅读或贡献内容，可以使用 Git 克隆仓库：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FWendelMarques\u002Fmateriais-de-estudos-sobre-data-science-deep-machine-learning.git\ncd materiais-de-estudos-sobre-data-science-deep-machine-learning\n```\n\n## 3. 基本使用指南\n\n本项目的核心用法是**按照目录结构制定学习计划**。以下是基于仓库内容的推荐学习流程：\n\n### 第一步：了解领域概况\n在开始深入技术细节前，先阅读 **\"UM POUCO SOBRE A ÁREA\" (关于该领域)** 部分的文章，理解人工智能、机器学习和深度学习之间的区别。\n*   *行动*：点击 README 中对应的 Medium 文章或播客链接进行科普阅读。\n\n### 第二步：制定学习路线图\n参考 **\"TRILHAS\u002F DICAS DE ESTUDOS\u002F ROADMAPS\" (学习路径\u002F建议\u002F路线图)** 章节。这里列出了多位从业者推荐的学习顺序。\n*   *行动*：选择一条适合你当前水平的路线图（例如：\"Trilha para Cientista de Dados\"），将其作为你的主学习大纲。\n\n### 第三步：夯实数学与编程基础\n根据路线图，进入 **\"Matemática\" (数学)** 和 **\"Linguagem Python\" (Python 语言)** 章节。\n*   **数学**：涵盖从基础数学到机器学习专用数学的内容。\n*   **编程**：重点学习 Python 及其数据科学库（Pandas, NumPy 等）。虽然链接多为葡语教程，但代码是通用的。\n    *   *实践示例*（跟随教程学习后，在本地 Python 环境中尝试）：\n    ```python\n    import pandas as pd\n\n    # 加载数据集 (参考仓库中 \"Datasets para iniciantes\" 部分获取数据)\n    df = pd.read_csv('seu_dataset.csv')\n\n    # 查看前几行数据\n    print(df.head())\n\n    # 进行简单的数据统计\n    print(df.describe())\n    ```\n\n### 第四步：进阶学习与实战\n当掌握基础后，利用 **\"Fundamentos IA\" (AI 基础)** 和 **\"Sites com desafios\u002F problemas\" (挑战\u002F问题网站)** 部分。\n*   **核心内容**：机器学习算法、神经网络、深度学习框架（TensorFlow 等）。\n*   **实战**：前往 Kaggle 或其他挑战网站，应用所学知识解决实际问题。仓库作者也分享了自己的项目案例（如 \"Mapeamento de médias do ENEM\"），可供参考。\n\n### 第五步：构建作品集与求职\n参考 **\"Dicas para montar portifólio\" (作品集建议)** 和 **\"Freelancer em Data Science\"** 部分，了解如何将你的练习项目转化为职业资本。\n\n---\n\n**特别提示**：\n由于该仓库资源主要针对巴西社区（PT-BR），对于中国开发者而言，最佳的“打开方式”是将其作为一个**全面的主题索引**。你可以利用它发现未知的优质概念或开源项目，然后利用这些关键词在中文社区（如知乎、CSDN、GitHub 中文区）或国际英文社区寻找更匹配你语言习惯的详细教程。","一名零基础的葡萄牙语学生想转行数据科学，面对海量且分散的学习资源感到无从下手。\n\n### 没有 materiais-de-estudos-sobre-data-science-deep-machine-learning 时\n- **资源筛选困难**：在谷歌和 YouTube 上盲目搜索\"Python 教程”或“机器学习入门”，被大量过时、付费或英文内容淹没，难以辨别质量。\n- **学习路径缺失**：不清楚该先学数学基础还是直接写代码，缺乏系统性的路线图，导致学习碎片化，经常半途而废。\n- **本地化内容匮乏**：很难找到高质量的葡萄牙语（PT-BR）免费教材和课程，语言障碍大大增加了入门门槛。\n- **实战方向迷茫**：不知道去哪里找适合新手的练习数据集，也不了解如何构建第一个作品集项目来证明能力。\n\n### 使用 materiais-de-estudos-sobre-data-science-deep-machine-learning 后\n- **精选资源直达**：直接获取仓库中整理好的免费 PT-BR 核心链接，涵盖从基础数学到 TensorFlow 的优质频道与书籍，节省 90% 的搜索时间。\n- **清晰成长路线**：参考作者亲测的“学习旅程”和推荐路线图，按部就班地从 Python 基础过渡到深度学习，建立结构化知识体系。\n- **母语无障碍学习**：依托专门筛选的葡语内容池，包括 Udemy 课程和本地博客，让初学者能用最熟悉的语言攻克复杂概念。\n- **实战项目指引**：利用推荐的初学者数据集和作品集构建建议，快速复现如\"ENEM 成绩地图可视化”等具体案例，积累求职资本。\n\nmateriais-de-estudos-sobre-data-science-deep-machine-learning 将散乱的知识点编织成清晰的葡语学习地图，让数据科学入门从“大海捞针”变为“按图索骥”。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fwendelmarques_materiais-de-estudos-sobre-data-science-deep-machine-learning_71f18e56.png","wendelmarques","Wendel Marques","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fwendelmarques_89911e05.jpg","computer science @ ufg | data science | gdg organizer\r\n",null,"Goiânia, Goiás, Brasil","wendelmjs@discente.ufg.br","https:\u002F\u002Fgithub.com\u002Fwendelmarques",659,129,"2026-04-05T18:41:24","MIT",1,"","未说明",{"notes":88,"python":86,"dependencies":89},"该仓库并非一个可运行的 AI 软件工具，而是一个关于数据科学和机器学习（初学者级别）的学习资料汇总列表。内容主要包含指向外部课程、书籍、视频、文章和数据集的链接（多为葡萄牙语）。因此，它没有操作系统、GPU、内存或特定 Python 库的安装需求。用户只需具备浏览器即可访问所列资源，若需实践链接中的教程，则需根据具体教程的要求自行配置环境（通常涉及 Python、Pandas、TensorFlow 等基础数据科学库）。",[],[91,16,14],"其他",[93,94,95,96,97,98],"guia-de-estudos","machine-learning","data-science","ciencia-de-dados","deep-learning","inteligencia-artificial","2026-03-27T02:49:30.150509","2026-04-08T01:10:50.770506",[],[]]