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@@ -28,296 +28,83 @@ <h1>AlgoWiki - Best Sources at one Place!</h1> | |
| </ul> | ||
| </header> | ||
| <section> | ||
| <h3> | ||
| <a id="index" class="anchor" href="#index" aria-hidden="true"><span class="octicon octicon-link"></span></a>Index</h3> | ||
| <h1> | ||
| <a id="algowiki" class="anchor" href="#algowiki" aria-hidden="true"><span class="octicon octicon-link"></span></a>AlgoWiki</h1> | ||
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| <ul> | ||
| <li><a href="#data-science-introduction">Data Science Introduction</a></li> | ||
| <li><a href="#big-data-processing">Data Processing</a></li> | ||
| <li> | ||
| <a href="#big-data-analysis">Data Analysis</a> | ||
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| <ul> | ||
| <li><a href="#fundamentals">Fundamentals</a></li> | ||
| <li><a href="#network-analysis">Network Analysis</a></li> | ||
| <li><a href="#statistics">Statistics</a></li> | ||
| <li><a href="#data-mining">Data Mining</a></li> | ||
| <li><a href="#machine-learning">Machine Learning</a></li> | ||
| </ul> | ||
| </li> | ||
| <li> | ||
| <a href="#big-data-application">Data Science Application</a> | ||
| <p>On Internet, There is a lot of knowledge on different topics scarred over different sources. Most of the knowledge is either online on the web or with the people as human knowledge. I want to create this repository as a platform to bind this scarred piece of knowledge at one place! </p> | ||
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| <ul> | ||
| <li><a href="#data-visualization">Data Visualization</a></li> | ||
| </ul> | ||
| </li> | ||
| <li><a href="#uncategorized">Uncategorized</a></li> | ||
| <li> | ||
| <a href="#moocs">MOOCs about Data Science</a><br> | ||
| </li> | ||
| </ul> | ||
| <h1> | ||
| <a id="navigation" class="anchor" href="#navigation" aria-hidden="true"><span class="octicon octicon-link"></span></a>Navigation</h1> | ||
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| <h3> | ||
| <a id="data-science-introduction" class="anchor" href="#data-science-introduction" aria-hidden="true"><span class="octicon octicon-link"></span></a>Data Science Introduction</h3> | ||
| <p><strong>Some of the links below contain only Pdfs books we will add websites and video resources soon!</strong></p> | ||
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| <ul> | ||
| <li> | ||
| <a href="http://www.amazon.com/Big-Data-Now-2012-Edition-ebook/dp/B0097E4EBQ">Big Data Now: 2012 Edition</a> - O'Reilly Media Inc. - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://en.wikibooks.org/wiki/Data_Science:_An_Introduction">Data Science: An Introduction</a> - Wikibook - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.amazon.com/Disruptive-Possibilities-Data-Changes-Everything-ebook/dp/B00CLH387W">Disruptive Possibilities: How Big Data Changes Everything</a> - Jeffrey Needham - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://jsresearch.net/">Introduction to Data Science</a> - Jeffery Stanton - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.amazon.com/Real-Time-Big-Data-Analytics-Architecture-ebook/dp/B00DO33RSW">Real-Time Big Data Analytics: Emerging Architecture</a> - Mike Barlow - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.amazon.com/The-Evolution-Data-Products-ebook/dp/B005QEKQUY/ref=sr_1_63?s=digital-text&ie=UTF8&qid=1351898530&sr=1-63">The Evolution of Data Products</a> - Mike Loukides - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.aspeninstitute.org/sites/default/files/content/docs/pubs/The_Promise_and_Peril_of_Big_Data.pdf">The Promise and Peril of Big Data</a> - David Bollier - <code>Beginner</code> | ||
| </li> | ||
| <li><a href="https://github.com/vicky002/AlgoWiki/blob/35ba2bc1ff92673eea81dc6eda8bb476719f00b5/Competitive-Programming/Competitive-Programming.md">All about Competitve-Programming</a></li> | ||
| <li><a href="https://github.com/vicky002/AlgoWiki/blob/gh-pages/Algorithms/Sources.md">Algoriths and Their Implement from different sources</a></li> | ||
| <li><a href="https://github.com/vicky002/AlgoWiki/blob/gh-pages/Free-Books/Algorithms-Data_Structures.md">Free Algorithm Books</a></li> | ||
| <li><a href="https://github.com/vicky002/AlgoWiki/blob/gh-pages/Free-Books/Data%20Science.md">Free Data Science Book pdfs</a></li> | ||
| <li><a href="https://github.com/vicky002/AlgoWiki/blob/gh-pages/Machine-Learning/Sources.md">Machine Learning</a></li> | ||
| <li><a href="https://github.com/vicky002/AlgoWiki/blob/gh-pages/C/sources.md">C~pdf books and websites</a></li> | ||
| <li><a href="https://github.com/vicky002/AlgoWiki/blob/gh-pages/C_plus_plus/resources.md">C++</a></li> | ||
| <li><a href="https://github.com/vicky002/AlgoWiki/blob/gh-pages/HTML_CSS/html_resources.md">HTML</a></li> | ||
| <li><a href="https://github.com/vicky002/AlgoWiki/blob/gh-pages/Interviews/resources.md">Interview Preparation</a></li> | ||
| <li><a href="https://github.com/vicky002/AlgoWiki/blob/gh-pages/JAVASCRIPT/resources.md">JavaScript</a></li> | ||
| <li><a href="https://github.com/airbnb/javascript/blob/master/README.md">JavaScript Guide</a></li> | ||
| </ul> | ||
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| <h3> | ||
| <a id="data-processing" class="anchor" href="#data-processing" aria-hidden="true"><span class="octicon octicon-link"></span></a>Data Processing</h3> | ||
| <h1> | ||
| <a id="how-to-contribute" class="anchor" href="#how-to-contribute" aria-hidden="true"><span class="octicon octicon-link"></span></a>How to Contribute</h1> | ||
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| <ul> | ||
| <li> | ||
| <a href="http://lintool.github.io/MapReduceAlgorithms/MapReduce-book-final.pdf">Data-Intensive Text Processing with MapReduce</a> - Jimmy Lin and Chris Dyer - <code>Intermediate</code> | ||
| </li> | ||
| </ul> | ||
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| <h3> | ||
| <a id="data-analysis" class="anchor" href="#data-analysis" aria-hidden="true"><span class="octicon octicon-link"></span></a>Data Analysis</h3> | ||
| <p>We are collecting Websites and resources! Send files,pdfs,printed articles or your bookmark folder @<a href="mailto:[email protected]">[email protected]</a> </p> | ||
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| <h4> | ||
| <a id="fundamentals" class="anchor" href="#fundamentals" aria-hidden="true"><span class="octicon octicon-link"></span></a>Fundamentals</h4> | ||
| <h1> | ||
| <a id="read-our-contribution-page" class="anchor" href="#read-our-contribution-page" aria-hidden="true"><span class="octicon octicon-link"></span></a>Read Our Contribution Page</h1> | ||
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| <ul> | ||
| <li> | ||
| <a href="http://ads.harvard.edu/books/1990fnmd.book/">Fundamental Numerical Methods and Data Analysis</a> - George W. Collins - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.getty.edu/research/publications/electronic_publications/intrometadata/index.html">Introduction to Metadata</a> - Murtha Baca - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://cran.r-project.org/doc/manuals/R-intro.pdf">Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics</a> - W. N. Venables, D. M. Smith, and the R Core Team - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://modelingwithdata.org/about_the_book.html">Modeling with Data: Tools and Techniques for Scientific Computing</a> - Ben Klemens - <code>Beginner</code> | ||
| </li> | ||
| <li><a href="https://github.com/vicky002/Wiki_Knowledge/wiki/Contribution">Contributing to this repository</a></li> | ||
| <li>You can Add links of different sources on different topics</li> | ||
| <li>You can also create topics and add diffrent resources to it.</li> | ||
| <li>Make sure that links that you provide should be related to the topic!</li> | ||
| </ul> | ||
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| <h4> | ||
| <a id="network-analysis" class="anchor" href="#network-analysis" aria-hidden="true"><span class="octicon octicon-link"></span></a>Network Analysis</h4> | ||
| <h1> | ||
| <a id="topics" class="anchor" href="#topics" aria-hidden="true"><span class="octicon octicon-link"></span></a>Topics</h1> | ||
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| <ul> | ||
| <li> | ||
| <a href="http://faculty.ucr.edu/%7Ehanneman/nettext/">Introduction to Social Network Methods</a> - Robert A. Hanneman and Mark Riddle - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.cs.cornell.edu/home/kleinber/networks-book/">Networks, Crowds, and Markets: Reasoning About a Highly Connected World</a> - David Easley and Jon Kleinberg - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://barabasilab.neu.edu/networksciencebook/downlPDF.html">Network Science</a> - Sarah Morrison - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.benkler.org/Benkler_Wealth_Of_Networks.pdf">The Wealth of Networks</a> - Yochai Benkler - <code>Beginner</code> | ||
| </li> | ||
| </ul> | ||
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| <h4> | ||
| <a id="statistics" class="anchor" href="#statistics" aria-hidden="true"><span class="octicon octicon-link"></span></a>Statistics</h4> | ||
| <li>Algorithms</li> | ||
| <li>Operating System</li> | ||
| <li>Competitive Programming</li> | ||
| <li>Web Languages | ||
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| <ul> | ||
| <li> | ||
| <a href="http://www.stat.cmu.edu/%7Ecshalizi/ADAfaEPoV/ADAfaEPoV.pdf">Advanced Data Analysis from an Elementary Point of View</a> - Cosma Rohilla Shalizi - <code>Veternan</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://cran.r-project.org/doc/manuals/R-intro.pdf">An Introduction to R</a> - W. N. Venables, D. M. Smith, and the R Core Team - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.ualberta.ca/%7Ebaayen/publications/baayenCUPstats.pdf">Analyzing Linguistic Data: a practical introduction to statistics</a> - R. H. Baayan - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://columbia-applied-data-science.github.io/appdatasci.pdf">Applied Data Science</a> - Ian Langmore and Daniel Krasner - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://vassarstats.net/textbook/">Concepts and Applications of Inferential Statistics</a> - Richard Lowry - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="https://www.otexts.org/fpp/">Forecasting: Principles and Practice</a> - Rob J. Hyndman and George Athanasopoulos - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.math.umass.edu/%7Elavine/Book/book.html">Introduction to Probability</a> - Charles M. Grinstead and J. Laurie Snell - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.math.umass.edu/%7Elavine/Book/book.pdf">Introduction to Statistical Thought</a> - Michael Lavine - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.openintro.org/stat/textbook.php">OpenIntro Statistics - Second Edition</a> - David M. Diez, Christopher D. Barr, and Mine Cetinkaya-Rundel - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf">simpleR - Using R for Introductory Statistics</a> - John Verzani - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://upload.wikimedia.org/wikipedia/commons/8/82/Statistics.pdf">Statistics</a> - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.greenteapress.com/thinkstats/thinkstats.pdf">Think Stats: Probability and Statistics for Programmers</a> - Allen B. Downey - <code>Beginner</code> | ||
| </li> | ||
| <li> Html</li> | ||
| <li> Javascipt</li> | ||
| <li> JQuery</li> | ||
| <li> php, etc!</li> | ||
| </ul> | ||
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| <h4> | ||
| <a id="data-mining" class="anchor" href="#data-mining" aria-hidden="true"><span class="octicon octicon-link"></span></a>Data Mining</h4> | ||
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| <ul> | ||
| <li> | ||
| <a href="http://www2.dcc.ufmg.br/livros/miningalgorithms/files/pdf/dmafca.pdf">Data Mining and Analysis: Fundamental Concepts and Algorithms</a> - Mohammed J. Zaki and Wagner Meira Jr. - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.intechopen.com/books/data_mining_and_knowledge_discovery_in_real_life_applications">Data Mining and Knowledge Discovery in Real Life Applications</a> - Julio Ponce and Adem Karahoca - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://link.springer.com/book/10.1007%2F978-1-4419-6287-4">Data Mining for Social Network Data</a> - Springer - <code>Veteran</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://infolab.stanford.edu/%7Eullman/mmds/book.pdf">Mining of Massive Datasets</a> - Anand Rajaraman, Jure Leskovec, and Jeffrey D. Ullman - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.intechopen.com/books/knowledge-oriented-applications-in-data-mininge">Knowledge-Oriented Applications in Data Mining</a> - Kimito Funatsu - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.intechopen.com/books/new-fundamental-technologies-in-data-mining">New Fundamental Technologies in Data Mining</a> - Kimito Funatsu - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://cran.r-project.org/doc/contrib/Zhao_R_and_data_mining.pdf">R and Data Mining: Examples and Case Studies</a> - Yanchang Zhao - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://statweb.stanford.edu/%7Etibs/ElemStatLearn/">The Elements of Statistical Learning</a> - Trevor Hastie, Robert Tibshirani, and Jerome Friedman - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.intechopen.com/books/theory-and-applications-for-advanced-text-mining">Theory and Applications for Advanced Text Mining</a> - Shigeaki Sakurai - <code>Intermediate</code> | ||
| </li> | ||
| </ul> | ||
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| <h4> | ||
| <a id="machine-learning" class="anchor" href="#machine-learning" aria-hidden="true"><span class="octicon octicon-link"></span></a>Machine Learning</h4> | ||
| <li>Programming Language | ||
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| <ul> | ||
| <li> | ||
| <a href="http://ciml.info/">A Course in Machine Learning</a> - Hal Daume - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="https://www.ics.uci.edu/%7Ewelling/teaching/273ASpring10/IntroMLBook.pdf">A First Encounter with Machine Learning</a> - Max Welling - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/031013.pdf">Bayesian Reasoning and Machine Learning</a> - David Barber - <code>Veteran</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.gaussianprocess.org/gpml/chapters/">Gaussian Processes for Machine Learning</a> - Carl Edward Rasmussen and Christopher K. I. Williams - <code>Veteran</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://alex.smola.org/drafts/thebook.pdf">Introduction to Machine Learning</a> - Alex Smola and S.V.N. Vishwanathan - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/">Probabilistic Programming & Bayesian Methods for Hackers</a> - Cam Davidson-Pilon (main author) - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.lionsolver.com/LIONbook/">The LION Way: Machine Learning plus Intelligent Optimization</a> - Robert Battiti and Mauro Brunato - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.greenteapress.com/thinkbayes/">Thinking Bayes</a> - Allen B. Downey - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://nbviewer.ipython.org/github/jakevdp/sklearn_scipy2013/tree/master/notebooks/">Sklearn Basics</a> - <code>Beginner</code> | ||
| </li> | ||
| <li>C</li> | ||
| <li>C++</li> | ||
| <li>Java</li> | ||
| </ul> | ||
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| <h3> | ||
| <a id="data-science-application" class="anchor" href="#data-science-application" aria-hidden="true"><span class="octicon octicon-link"></span></a>Data Science Application</h3> | ||
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| <h4> | ||
| <a id="information-retrieval" class="anchor" href="#information-retrieval" aria-hidden="true"><span class="octicon octicon-link"></span></a>Information Retrieval</h4> | ||
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| <ul> | ||
| <li> | ||
| <a href="http://nlp.stanford.edu/IR-book/">Introduction to Information Retrival</a> - Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze - <code>Intermediate</code> | ||
| </li> | ||
| </ul> | ||
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| <h4> | ||
| <a id="data-visualization" class="anchor" href="#data-visualization" aria-hidden="true"><span class="octicon octicon-link"></span></a>Data Visualization</h4> | ||
| <li>Machine Learning</li> | ||
| <li>Artificial Intelligence</li> | ||
| <li>Discrete Mathematics | ||
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| <ul> | ||
| <li> | ||
| <a href="http://chimera.labs.oreilly.com/books/1230000000345/index.html">Interactive Data Visualization for the Web</a> - Scott Murray - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://nbviewer.ipython.org/urls/gist.github.com/fonnesbeck/5850463/raw/a29d9ffb863bfab09ff6c1fc853e1d5bf69fe3e4/3.+Plotting+and+Visualization.ipynb">Plotting and Visualization in Python</a> - <code>Beginner</code> | ||
| </li> | ||
| <li>Number Theory</li> | ||
| <li>Graph Theory</li> | ||
| <li>Combinatorics</li> | ||
| <li>Game Theory etc!</li> | ||
| </ul> | ||
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| <h3> | ||
| <a id="uncategorized" class="anchor" href="#uncategorized" aria-hidden="true"><span class="octicon octicon-link"></span></a>Uncategorized</h3> | ||
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| <ul> | ||
| <li> | ||
| <a href="http://datajournalismhandbook.org/1.0/en/">Data Journalism Handbook</a> - Jonathan Gray, Liliana Bounegru, and Lucy Chambers - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://assets.en.oreilly.com/1/eventseries/23/Building-Data-Science-Teams.pdf">Building Data Science Teams</a> - DJ Patil - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.inference.phy.cam.ac.uk/itprnn/book.html">Information Theory, Inference, and Learning Algorithms</a> - David MacKay - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/readings/MIT6_042JF10_notes.pdf">Mathematics for Computer Science</a> - Eric Lehman, Thomas Leighton, and Albert R. Meyer - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://www.boozallen.com/media/file/The-Field-Guide-to-Data-Science.pdf">The Field Guide to Data Science</a> - <code>Beginner</code> | ||
| </li> | ||
| </ul> | ||
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| <h3> | ||
| <a id="moocs-about-data-science" class="anchor" href="#moocs-about-data-science" aria-hidden="true"><span class="octicon octicon-link"></span></a>MOOCs about Data Science</h3> | ||
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| <ul> | ||
| <li> | ||
| <a href="http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/">Data Mining with Weka</a> - Ian H. Witten - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="https://class.coursera.org/datasci-001/class">Introduction to Data Science</a> - Bill Howe (Coursera) - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="https://www.udacity.com/course/ud617">Introduction to Hadoop and MapReduce</a> - Udacity - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="https://class.coursera.org/ml-003/class">Machine Learning</a> - Andrew Ng (Coursera) - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="https://class.coursera.org/ntumlone-001">Machine Learning Foundatiaons (taught in Chinese)</a> - Hsuan-Tien Lin - <code>Beginner</code> | ||
| </li> | ||
| <li> | ||
| <a href="http://work.caltech.edu/library/#!?goback=.gde_35222_member_5810981726511443971">Machine Learning Video Library</a> - Yaser Abu-Mostafa - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="https://class.coursera.org/nlp/lecture/preview">Natural Language Processing</a> - Dan Jurafsky and Christopher Manning (Coursera) - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="https://class.coursera.org/networksonline-001/class">Social and Economic Networks: Models and Analysis</a> - Matthew O. Jackson (Coursera) - <code>Intermediate</code> | ||
| </li> | ||
| <li> | ||
| <a href="https://class.coursera.org/sna-003/class">Social Network Analysis</a> - Lada Adamic (Coursera) - <code>Intermediate</code> | ||
| </li> | ||
| </ul> | ||
| <p>There are many topics, you can contribute to it, create topics add resources to it!</p> | ||
| </section> | ||
| <footer> | ||
| <p>This project is maintained by <a href="https://github.com/vicky002">vicky002</a></p> | ||
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