面向程序猿的数据科学与机器学习知识体系及资料合集
GretaColeba
7年前
<p>Table of Contents generated withDocToc</p> <ul> <li>DataScience & Machine Learning Reference</li> <li>Introduction & Overview:入门与概览</li> <li> <ul> <li>Collections:资源汇总帖</li> <li>Video Courses:视频教程</li> <li>Blogs & Forum:博客与论坛</li> </ul> </li> <li> <ul> <li>Data Process:数据处理</li> <li>Machine Learning:机器学习</li> <li>Nature Language Processing:自然语言处理</li> <li>Deep Learning:深度学习</li> </ul> </li> <li> <ul> <li>Recommend System:推荐系统</li> </ul> </li> <li>CrawlerSE:爬虫与搜索引擎 <ul> <li>Search Engine:搜索引擎</li> </ul> </li> <li>Data Visual:数据可视化</li> <li> <ul> <li>Collections:资源汇总帖 <ul> <li>跨学科数据库与搜索引擎</li> </ul> </li> <li>Social Network:社交网络</li> <li>Driving Data:驾驶数据</li> </ul> </li> <li> <ul> <li>Competition:机器学习相关竞赛</li> </ul> </li> </ul> <h2><strong>DataScience & Machine Learning Reference</strong></h2> <p>本文是笔者在学习DataScience过程中所有资源的汇总，本文着眼于各个领域的入门介绍以及综述性质资源的汇总，并不会过多的深挖前沿，若有兴趣了解更多，可以关注笔者的 程序猿的数据科学与机器学习实战手册 。本文主线从对数据科学与机器学习入门概览开始，继而提供一系列的资源、书籍与教程，然后介绍各个具体的领域内的参考文章，最后介绍一系列的实用工具。笔者的数据科学与机器学习世界观图解如下，其从属于笔者的编程世界观与方法论系列:</p> <p style="text-align: center;"><img src="https://simg.open-open.com/show/17e53670e31ed3c5e75f62809163a691.png"></p> <p>本文会随着笔者自身学习实践中格局与能力的提升而不断完善，笔者并非纯粹的机器学习与数据挖掘研究者，更多的是从工程的角度来寻找能够与工程相结合应用的方面。</p> <h3><strong>Introduction & Overview:入门与概览</strong></h3> <h3><strong>Introduction</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726302901339672" rel="nofollow,noindex">数据科学与机器学习导论</a></li> <li><a href="/misc/goto?guid=4959726302998781496" rel="nofollow,noindex">数据分析，数据挖掘，数据科学，机器学习与大数据之间的异同</a></li> <li><a href="/misc/goto?guid=4959726303081602006" rel="nofollow,noindex">如何向非计算机科学与技术的人解释机器学习与数据挖掘</a></li> </ul> <h3><strong>Machine Learning</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726303173939947" rel="nofollow,noindex">Visual Intro To Machine Learning</a> :图解如何基于决策树对于纽约与San Francisco的房产进行分类</li> <li><a href="/misc/goto?guid=4959726303258593199" rel="nofollow,noindex">A Gentle Guide to Machine Learning</a></li> <li><a href="/misc/goto?guid=4959726303336521054" rel="nofollow,noindex">Machine Learning basics for a newbie</a></li> <li><a href="https://www.油Tube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1" rel="nofollow,noindex">What is machine learning, and how does it work?</a></li> </ul> <h3><strong>Deep Learning</strong></h3> <ul> <li><a href="/misc/goto?guid=4959674720087619540" rel="nofollow,noindex">有趣的机器学习概念纵览：从多元拟合，神经网络到深度学习，给每个感兴趣的人</a></li> <li> <p><a href="http://www.hackcv.com/index.php/archives/104/?hmsr=toutiao.io&utm_medium=toutiao.io&utm_source=toutiao.io" rel="nofollow,noindex">[翻译] 神经网络的直观解释</a> :卷积神经网络的讲解非常通俗易懂。</p> </li> <li> <p><a href="/misc/goto?guid=4959726303630433405" rel="nofollow,noindex">Deep-Learning-Papers-Reading-Roadmap</a> :为每个对深度学习感兴趣的朋友整理的论文阅读路线图</p> </li> <li> <p><a href="http://mp.weixin.qq.com/s?__biz=MzA4ODMwMDcxMQ==&mid=2650891687&idx=1&sn=5cacb7cc40907c4b3080f95a2f007396&chksm=8bd9886fbcae0179ffb26e77cf448827870827fb24c3b4c6df7889f46ae59955f278eccba19a&mpshare=1&scene=2&srcid=1107xRQnB44aPEpI8Tvtatls&from=timeline&isappinstalled=0#wechat_redirect" rel="nofollow,noindex">程序员的深度学习入门指南</a> :来自费良宏在2016QCon全球软件开发大会（上海）上的演讲。</p> </li> </ul> <h3><strong>Statistics</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726303796643238" rel="nofollow,noindex">知乎：「数据会说谎」的真实例子有哪些？</a></li> </ul> <h3><strong>News:行业与新闻</strong></h3> <ul> <li><a href="/misc/goto?guid=4958988625466137034" rel="nofollow,noindex">深度学习框架大战正在进行，谁将夺取“深度学习工业标准”的荣耀？</a></li> </ul> <h3><strong>Application:数据挖掘/机器学习/深度学习的实际应用案例</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726306291963827" rel="nofollow,noindex">深度学习带来的变革：深度学习的十个典型应用</a></li> <li><a href="/misc/goto?guid=4959726306381431721" rel="nofollow,noindex">2015 年 Quora关于其机器学习具体应用的讲解</a></li> </ul> <h2><strong>Resources:资源</strong></h2> <h3><strong>Collections:资源汇总帖</strong></h3> <ul> <li><a href="/misc/goto?guid=4958978639535485079" rel="nofollow,noindex">机器学习入门资源不完全汇总</a> :本文是 机器学习日报的一个专题合集。</li> <li><a href="/misc/goto?guid=4959726306498481538" rel="nofollow,noindex">Top-down learning path: Machine Learning for Software Engineers</a> :针对软件工程师的机器学习进阶之路</li> </ul> <h3><strong>Books:书籍</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726306580441142" rel="nofollow,noindex">2014 - DataScience From Scratch</a></li> <li><a href="/misc/goto?guid=4959726306669967301" rel="nofollow,noindex">2012 - 李航:统计方法学</a></li> <li><a href="/misc/goto?guid=4959726306753214720" rel="nofollow,noindex">2015 - Data Mining, The Textbook</a></li> <li><a href="/misc/goto?guid=4959726306839324935" rel="nofollow,noindex">2016 - 周志华 机器学习</a></li> <li><a href="/misc/goto?guid=4959726306921704912" rel="nofollow,noindex">2012 - Machine Learning A Probabilistic Perspective</a></li> <li><a href="/misc/goto?guid=4959726306993513864" rel="nofollow,noindex">2012 - 深入浅出机器学习 中文版</a></li> <li><a href="/misc/goto?guid=4959726307086287633" rel="nofollow,noindex">南京大学计算机科学与技术系 数据挖掘课程</a></li> </ul> <h3><strong>Video Courses:视频教程</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726307169457024" rel="nofollow,noindex">University of Illinois at Urbana-Champaign:Text Mining and Analytics</a></li> <li><a href="/misc/goto?guid=4959726307248519041" rel="nofollow,noindex">台大 机器学习技法</a></li> <li><a href="/misc/goto?guid=4959726307333367670" rel="nofollow,noindex">斯坦福 机器学习课程</a></li> <li><a href="/misc/goto?guid=4959726307417880494" rel="nofollow,noindex">CS224d: Deep Learning for Natural Language Processing</a></li> <li> <p><a href="/misc/goto?guid=4958978635884833865" rel="nofollow,noindex">Unsupervised Feature Learning and Deep Learning</a> :来自斯坦福的无监督特征学习与深度学习系列教程</p> </li> <li> <p><a href="/misc/goto?guid=4959726307519240249" rel="nofollow,noindex">小象 机器学习视频教程</a></p> </li> <li><a href="/misc/goto?guid=4959726307609709972" rel="nofollow,noindex">小象 深度学习视频教程</a></li> </ul> <h3><strong>Blogs & Forum:博客与论坛</strong></h3> <h3><strong>Methodology:方法论</strong></h3> <h3><strong>Data Process:数据处理</strong></h3> <h3><strong>Machine Learning:机器学习</strong></h3> <ul> <li> <p><a href="/misc/goto?guid=4959726307683924401" rel="nofollow,noindex">10 Machine Learning Algorithms Explained to an ‘Army Soldier’</a></p> </li> <li> <p><a href="/misc/goto?guid=4959726307767470748" rel="nofollow,noindex">Top 10 data mining algorithms in plain English</a></p> </li> <li> <p><a href="/misc/goto?guid=4959726307842968979" rel="nofollow,noindex">10 Machine Learning Terms Explained in Simple English</a></p> </li> <li> <p><a href="/misc/goto?guid=4958978647652973714" rel="nofollow,noindex">A Tour of Machine Learning Algorithms</a></p> </li> <li> <p><a href="/misc/goto?guid=4959726307966126763" rel="nofollow,noindex">The 10 Algorithms Machine Learning Engineers Need to Know</a></p> </li> <li> <p><a href="/misc/goto?guid=4959648856239100041" rel="nofollow,noindex">Comparing supervised learning algorithms</a></p> </li> </ul> <h3><strong>Nature Language Processing:自然语言处理</strong></h3> <h3><strong>Deep Learning:深度学习</strong></h3> <ul> <li><a href="http://www.jiqizhixin.com/article/1772?utm_source=tuicool&utm_medium=referral" rel="nofollow,noindex">重磅论文：解析深度卷积神经网络的14种设计模式</a></li> </ul> <h3><strong>Application:应用</strong></h3> <h3><strong>Recommend System:推荐系统</strong></h3> <h3><strong>CrawlerSE:爬虫与搜索引擎</strong></h3> <h3><strong>Crawler:爬虫</strong></h3> <h3><strong>Search Engine:搜索引擎</strong></h3> <h3><strong>Toolkits:工具</strong></h3> <h3><strong>Language</strong></h3> <h3><strong>Python</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726308149574761" rel="nofollow,noindex">Jupyter</a> :交互式编程与数据展示</li> <li><a href="/misc/goto?guid=4959647224887656067" rel="nofollow,noindex">data-science-ipython-notebooks</a> :一系列基于IPython的数据科学代码展示</li> <li><a href="/misc/goto?guid=4959629558478406945" rel="nofollow,noindex">The Open Source Data Science Masters</a></li> </ul> <h3><strong>Java</strong></h3> <h3><strong>Matlab</strong></h3> <h3><strong>R</strong></h3> <h3><strong>ClusterComputing</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726308291225812" rel="nofollow,noindex">Madout</a> <ul> <li>MLib ## DeepLearning:深度学习工具集</li> </ul> </li> <li><a href="/misc/goto?guid=4959726308364313641" rel="nofollow,noindex">Evaluation of Deep Learning Toolkits</a></li> <li><a href="http://geek.csdn.net/news/detail/62429?utm_source=tuicool&utm_medium=referral" rel="nofollow,noindex">代码解析深度学习系统编程模型：TensorFlow vs. CNTK</a></li> <li><a href="/misc/goto?guid=4959670886028938781" rel="nofollow,noindex">tensorflow-playground</a> :Play with neural networks! <img src="https://simg.open-open.com/show/2ab6f5744ac239b456f1d56de67c2937.jpg"></li> <li>dl-docker:将常用的深度学习工具打包在了一个Docker镜像中</li> <li>deep-learning-models:Keras code and weights files for popular deep learning models.</li> <li><a href="/misc/goto?guid=4959726308572143956" rel="nofollow,noindex">Top Deep Learning Projects</a> -</li> </ul> <h3><strong>Data Visual:数据可视化</strong></h3> <h3><strong>Books:书籍</strong></h3> <h3><strong>Video Courses:视频教程</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726308648799943" rel="nofollow,noindex">John C. Hart Coursera</a></li> </ul> <h3><strong>Toolkits:工具</strong></h3> <h3><strong>Data Sets</strong></h3> <h3><strong>Collections:资源汇总帖</strong></h3> <ul> <li><a href="/misc/goto?guid=4959629577380136930" rel="nofollow,noindex">awesome-public-datasets</a> :An awesome list of high-quality open datasets in public domains (on-going).</li> <li><a href="/misc/goto?guid=4959726308757453598" rel="nofollow,noindex">Wikimedia Dumps</a> :Wiki上的数据打包下载</li> <li><a href="/misc/goto?guid=4959726308844130736" rel="nofollow,noindex">Reddit Datasets</a> :Reddit上关于数据集的讨论板块 | Militarized Interstate Disputes | Nearly 200 years of international threats, conflicts, etc. for modelling or prediction. Includes action taken, level of hostility, fatalities, and outcomes. | Multiple datasets, e.g., 962KB, 179KB | <a href="/misc/goto?guid=4959726308928250407" rel="nofollow,noindex">http://www.correlatesofwar.org/data-sets/MIDs</a> |</li> </ul> <h3><strong>单一数据库</strong></h3> <ul> <li><a href="/misc/goto?guid=4959645897310510780" rel="nofollow,noindex">http://archive.ics.uci.edu/ml/</a></li> <li><a href="/misc/goto?guid=4959726309037717370" rel="nofollow,noindex">http://crawdad.org/</a></li> <li><a href="/misc/goto?guid=4959726309119734340" rel="nofollow,noindex">http://data.austintexas.gov</a></li> <li><a href="/misc/goto?guid=4959726309195466689" rel="nofollow,noindex">http://snap.stanford.edu/data/index.html</a></li> <li><a href="/misc/goto?guid=4959726309278522728" rel="nofollow,noindex">http://data.cityofchicago.org</a></li> <li><a href="/misc/goto?guid=4959726309351095378" rel="nofollow,noindex">http://data.govloop.com</a></li> <li><a href="/misc/goto?guid=4959726309439955137" rel="nofollow,noindex">http://data.gov.uk/data.gov.in</a></li> <li><a href="/misc/goto?guid=4959726309513623272" rel="nofollow,noindex">http://data.medicare.gov</a></li> <li><a href="/misc/goto?guid=4959726309596622164" rel="nofollow,noindex">http://www.dados.gov.pt/pt/catalogodados/catalogodados.aspx</a></li> <li><a href="/misc/goto?guid=4959726309681181172" rel="nofollow,noindex">http://data.sfgov.org</a></li> <li><a href="/misc/goto?guid=4959726309756051072" rel="nofollow,noindex">http://data.sunlightlabs.com</a></li> <li><a href="/misc/goto?guid=4959726309839331205" rel="nofollow,noindex">https://datamarket.azure.com/</a></li> <li><a href="/misc/goto?guid=4959726309927242708" rel="nofollow,noindex">http://econ.worldbank.org/datasets</a></li> <li><a href="/misc/goto?guid=4959726310003502151" rel="nofollow,noindex">http://gettingpastgo.socrata.com</a></li> <li><a href="/misc/goto?guid=4959726310081561752" rel="nofollow,noindex">http://public.resource.org/</a></li> <li><a href="/misc/goto?guid=4959726310163033587" rel="nofollow,noindex">http://timetric.com/public-data/</a></li> <li><a href="/misc/goto?guid=4959726310239955990" rel="nofollow,noindex">http://www.bls.gov/</a></li> <li><a href="/misc/goto?guid=4959726310326020387" rel="nofollow,noindex">http://www.crunchbase.com/</a></li> <li><a href="/misc/goto?guid=4959726310404369263" rel="nofollow,noindex">http://www.dartmouthatlas.org/</a></li> <li><a href="/misc/goto?guid=4959726310484687737" rel="nofollow,noindex">http://www.data.gov/</a></li> <li><a href="/misc/goto?guid=4959726310575159854" rel="nofollow,noindex">http://www.datakc.org</a></li> <li><a href="/misc/goto?guid=4959726310647947199" rel="nofollow,noindex">http://dbpedia.org</a></li> <li><a href="/misc/goto?guid=4959726310733016457" rel="nofollow,noindex">http://www.factual.com/</a></li> <li><a href="/misc/goto?guid=4959726310807085101" rel="nofollow,noindex">http://www.freebase.com/</a></li> <li><a href="/misc/goto?guid=4959726310896084409" rel="nofollow,noindex">http://www.infochimps.com</a></li> <li><a href="/misc/goto?guid=4959726310980654792" rel="nofollow,noindex">http://build.kiva.org/</a></li> <li><a href="/misc/goto?guid=4959726311066338240" rel="nofollow,noindex">http://www.imdb.com/interfaces</a></li> <li><a href="/misc/goto?guid=4959726311150667442" rel="nofollow,noindex">http://knoema.com</a></li> <li><a href="/misc/goto?guid=4959726311226144989" rel="nofollow,noindex">http://daten.berlin.de/</a></li> <li><a href="/misc/goto?guid=4959726311311969203" rel="nofollow,noindex">http://www.qunb.com</a></li> <li><a href="/misc/goto?guid=4959726311392687024" rel="nofollow,noindex">http://databib.org/</a></li> <li><a href="/misc/goto?guid=4959726311478878373" rel="nofollow,noindex">http://datacite.org/</a></li> <li><a href="/misc/goto?guid=4959726311562052681" rel="nofollow,noindex">http://data.reegle.info/</a></li> <li><a href="/misc/goto?guid=4959726311648646221" rel="nofollow,noindex">http://data.wien.gv.at/</a></li> <li><a href="/misc/goto?guid=4959726311731730915" rel="nofollow,noindex">http://data.gov.bc.ca</a></li> </ul> <h3><strong>跨学科数据库与搜索引擎</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726311815578789" rel="nofollow,noindex">https://www..com/datasets</a></li> <li><a href="/misc/goto?guid=4959726311896020595" rel="nofollow,noindex">http://usgovxml.com</a></li> <li><a href="/misc/goto?guid=4959726311980270784" rel="nofollow,noindex">http://aws.amazon.com/datasets</a></li> <li><a href="/misc/goto?guid=4959726312061442713" rel="nofollow,noindex">http://databib.org</a></li> <li><a href="/misc/goto?guid=4959726312147642675" rel="nofollow,noindex">http://datacite.org</a></li> <li><a href="/misc/goto?guid=4959726312231233946" rel="nofollow,noindex">http://figshare.com</a></li> <li><a href="/misc/goto?guid=4959726312314888207" rel="nofollow,noindex">http://linkeddata.org</a></li> <li><a href="/misc/goto?guid=4959726312400864241" rel="nofollow,noindex">http://thewebminer.com/</a></li> <li><a href="/misc/goto?guid=4959726312472566878" rel="nofollow,noindex">http://thedatahub.org</a></li> <li><a href="/misc/goto?guid=4959726312563530577" rel="nofollow,noindex">http://ckan.net</a></li> <li><a href="/misc/goto?guid=4959726312653460202" rel="nofollow,noindex">http://quandl.com</a></li> <li>Open Data Inception（这里有 2500+ 开源接口）</li> </ul> <h3><strong>Text:文本</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726312733870465" rel="nofollow,noindex">20 Newsgroups</a> :The text from 20000 messages taken from 20 Usenet newsgroups for text analysis, classification, etc. 61.6MB</li> <li><a href="/misc/goto?guid=4959726312818662894" rel="nofollow,noindex">Amazon Reviews</a> :Over 142 million product reviews for sentiment analysis, recommender systems, and more.20GB | SMS Spam Collection | A collection of 5,574 SMS (text) messages, some spam, some normal, for spam filtering. | 204KB | <a href="/misc/goto?guid=4959726312904872308" rel="nofollow,noindex">http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/</a> |</li> </ul> <h3><strong>Social Network:社交网络</strong></h3> <ul> <li><a href="/misc/goto?guid=4959616086399491501" rel="nofollow,noindex">http://enigma.io</a></li> <li><a href="/misc/goto?guid=4959726313024544543" rel="nofollow,noindex">http://www.ufindthem.com/</a></li> <li><a href="/misc/goto?guid=4959726313116290247" rel="nofollow,noindex">http://NetworkRepository.com（有视觉互动分析的机器学习数据库）</a></li> <li><a href="/misc/goto?guid=4959726313198078624" rel="nofollow,noindex">http://MLvis.com</a></li> <li><a href="/misc/goto?guid=4959726313282376758" rel="nofollow,noindex">Yahoo Instant Messenger Friends Connectivity Graph</a> :Connections between Yahoo users who communicate with each other using Yahoo messenger, can be used to identify key social contacts/influencers. Add dataset to cart to access. 共 28MB。</li> </ul> <h3><strong>Media:影音图片</strong></h3> <ul> <li><a href="/misc/goto?guid=4959663884255715483" rel="nofollow,noindex">Labeled Faces in the Wild</a> :13,000 named faces for facial recognition. Multiple training and test sets. 共173MB</li> <li><a href="/misc/goto?guid=4959726313401630331" rel="nofollow,noindex">Mushroom Identification</a> :For hypothetically classifying mushrooms as edible or poisonous based on its characteristics.3 files, 480KB</li> <li><a href="/misc/goto?guid=4959726313484621808" rel="nofollow,noindex">NORB 3D Object Recognition</a> :Binocular images of 50 toy figurines for 3D object recognition from image.Multiple files, over 5GB total</li> <li><a href="/misc/goto?guid=4959726313555681276" rel="nofollow,noindex">One Million Songs</a> :Audio features and metadata for a subset (10,000) of the one million popular songs dataset for recognition/classification.1.8GB</li> <li><a href="/misc/goto?guid=4959726313642623450" rel="nofollow,noindex">Hate Speech Identification</a> :A sampling of 推ter posts that have been judged based on whether they are offensive or contain hate speech, as a training set for text analysis.2.66MB</li> <li><a href="/misc/goto?guid=4959726313734139298" rel="nofollow,noindex">Hidden Beauty of Flickr Pictures</a> :15,000 Flickr photo IDs that have received ratings based on aesthetics, for image analysis.138KB, use Flickr API to get images</li> </ul> <h3><strong>Recognition</strong></h3> <p>| Human Activity Recognition with Smartphones | Sensor data for recognizing the human activity - walking, sitting, etc. | 25MB | <a href="/misc/goto?guid=4959726313819382610" rel="nofollow,noindex">https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones</a> |</p> <h3><strong>Driving Data:驾驶数据</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726313903168847" rel="nofollow,noindex">UDA City 开源的223G的关于自动驾驶的历史数据</a></li> </ul> <h3><strong>Domain:领域数据</strong></h3> <h3><strong>Sports:体育</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726313985994169" rel="nofollow,noindex">Football Strategy</a> :Thousands of scenarios to make the best coaching decisions. 共876KB</li> <li><a href="/misc/goto?guid=4959726314072501445" rel="nofollow,noindex">Horses for Courses</a> :Horse-racing data for predicting race results. 共 19MB</li> <li><a href="/misc/goto?guid=4959726314152543167" rel="nofollow,noindex">NBA & MLB Stats</a> :Current and past season stats for teams and players for fantasy sports predictions.</li> </ul> <h3><strong>Medicines:医药</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726314240941731" rel="nofollow,noindex">National Survey on Drug Use and Health</a> :Predict drug use based on health survey questions. 共2GB</li> <li><a href="/misc/goto?guid=4959726314333034584" rel="nofollow,noindex">Prostate Cancer</a> :Tumor and nontumor samples, used to recognize prostate cancer. 共 4.8MB</li> <li><a href="/misc/goto?guid=4959726314410717941" rel="nofollow,noindex">Record of Heart Sound</a> :Recordings of normal and abnormal heartbeats, used to recognize heart murmur, etc. 共47.7MB</li> </ul> <h3><strong>Alien:外星人</strong></h3> <ul> <li>UFO Reports:80,000 historic reports for classification or regression. This dataset has been standardized from the source data at nuforc.org 共14.6MB。</li> </ul> <h3><strong>Foods:饮食</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726314495118218" rel="nofollow,noindex">Wine Quality</a> :Chemical properties of red and white wines (separately) and quality, for classification. 3个文件，共343KB。</li> </ul> <h3><strong>Finance:金融</strong></h3> <h3><strong>Others:其他</strong></h3> <h3><strong>Competition:机器学习相关竞赛</strong></h3> <ul> <li><a href="https://tianchi.shuju.aliyun.com/getStart/index.htm?spm=5176.100065.111.3.jgYTrv&id=&_lang=zh_CN" rel="nofollow,noindex">阿里天池 新人实战赛</a></li> <li><a href="/misc/goto?guid=4959726314670153850" rel="nofollow,noindex">Kaggle</a> :官方新人赛，不错的入门学习</li> <li><a href="/misc/goto?guid=4959726314769326052" rel="nofollow,noindex">Kaggle Tutorial</a> :基于旅馆推荐比赛实例的完整Tutorial</li> <li><a href="/misc/goto?guid=4959726314860951535" rel="nofollow,noindex">Driven Data</a></li> <li><a href="/misc/goto?guid=4958849146267171119" rel="nofollow,noindex">Innocentive</a></li> <li><a href="/misc/goto?guid=4959726314974449044" rel="nofollow,noindex">Crowdanalytix</a></li> <li><a href="/misc/goto?guid=4959726315062710746" rel="nofollow,noindex">Tunedit</a></li> <li><a href="/misc/goto?guid=4959726315145885961" rel="nofollow,noindex">DataFountain</a> :DF,CCF指定中国专业的数据竞赛平台</li> </ul> <h3><strong>Career:职业</strong></h3> <ul> <li><a href="/misc/goto?guid=4959726315232142352" rel="nofollow,noindex">Quora 关于机器学习的招聘启事</a></li> <li><a href="https://www.google.com/about/careers/search?_ga=1.89288795.153537653.1473158707#!t=jo&jid=28625001&" rel="nofollow,noindex">Google 关于机器学习与人工智能岗位的招聘启事</a></li> </ul> <p> </p> <p>来自：https://github.com/wxyyxc1992/DataScience-And-MachineLearning-Handbook-For-Coders/blob/master/DataScience-Reference.md</p> <p> </p>