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Search results “Open source text data mining tools”
Text mining with Voyant Tools, no R or any other coding required
 
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Please explore free and beautiful Voyant Tools that allow you to perform any text analysis or even mining - word frequency, clouds, co-occurrence (collocations), spider diagrams, context analysis - anything you dreamt of without any prior programming experience or need to buy expensive software. To those interested in reproducing what we've done and further analyzing comments to Indian political articles (dated March-April and January 2016), please use this link to get the ball rolling: http://voyant-tools.org/?corpus=0c17d82dbd8b04baae655f90db84a672 Lastly, creators of the video are eternally grateful to our Big Data class professor, who believed in us and kept us going despite any technical or analytical difficulties.
Views: 8412 Adventuruous Mind
A survey of open-source data mining tools - Survey Bots
 
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A survey of open-source data mining tools - Survey Bots
Views: 413 Sumeet Singh
Unboxing Six Open Source Annotation Tools - episode C01
 
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Think of this as an unboxing video for annotation software - this is the first time I've tried running any of this software. Don't expect any good demos, I'm just showing you where to find them along with some resources. GATE https://gate.ac.uk/family/ MAE2 https://keighrim.github.io/mae-annotation/ BRAT http://brat.nlplab.org/features.html WebAnno https://webanno.github.io/webanno/ Annis http://corpus-tools.org/annis/ SLATE https://bitbucket.org/dainkaplan/slate/ Works cited: Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications https://smile.amazon.com/Natural-Language-Annotation-Machine-Learning/dp/1449306667/ Overview of Annotation Creation: Processes & Tools. Finlayson, Mark & Erjavec, Tomaž. (2016). https://www.researchgate.net/publication/301847215_Overview_of_Annotation_Creation_Processes_Tools Handbook of Linguistic Annotation. "Collaborative Web-Based Tools for Multi-layer Text Annotation" pp 229-256 https://link.springer.com/chapter/10.1007/978-94-024-0881-2_8 Also, this is the document I meant to show at 14:21 in the video: Annotation Process Management Revisited Dain Kaplan, Ryu Iida, Takenobu Tokunaga Department of Computer Science, Tokyo Institute of Technology http://www.lrec-conf.org/proceedings/lrec2010/pdf/129_Paper.pdf
Views: 2141 Norman Gilmore
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 475313 Brandon Weinberg
QDA Miner Lite - Free Qualitative Data Analysis Software
 
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In this video, we are going to show you how to use QDA Miner Lite to code and analyze your documents and images. QDA Miner Lite is a free qualitative data analysis software made by Provalis Research.
Data Mining Tool essentials
 
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Basics of the Data Mining Tool. Includes experiment info, quality control, text search and standard search.
Views: 83 QMRIBioinf
The Trending 20: #17 - Text and Data Mining Tools
 
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#17 of 20 of a lightning round in a webinar format that shares brief updates on 20 trending initiatives and developments that are currently shaping the world of scholarly publishing. To view slides from this webinar, visit http://allenpress.com/system/files/pd... With the high level of innovation and creative change currently happening in our industry, it’s sometimes hard to step back from the daily blogs and emails to see how it all fits together. From CHORUS, ORCID, NISO, and CrossRef to a growing community of startups, exciting things are going on that will transform the way we research, communicate, and deliver content. In an effort to provide you with the big picture, this lightning round in a webinar format will share brief updates on 20 trending initiatives and developments that are currently shaping the world of scholarly publishing. Level: Introductory Hashtag #apweb36
Views: 49 AllenPress
Ankus (Data Mining and Machine Learning) Open Source User Guide Video
 
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하둡 기반의 Ankus (Data Mining and Machine Learning) 오픈소스의 전체 기능을 가이드한 동영상입니다.
Views: 419 전수현
Orange Data Mining tool
 
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For more information visit orange.biolab.si
Views: 9221 Deeksha Acharya
Voyant Tools Tutorial
 
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A brief overview of how to use open source text analysis software to teach literature.
Views: 6842 Tom Liam Lynch
40 Data Analysis New Tools - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 91 Data Analytics
Data Mining with WEKA
 
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Learn Data Mining with WEKA - Open Source Data Mining and Machine Learning platform Software: http://www.cs.waikato.ac.nz/ml/weka/ More about FX Data Mining: http://www.algonell.com/
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 168078 Timothy DAuria
SEO - Keyword discovery tool - Mozenda Data Mining - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 77 Data Analytics
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 483508 sentdex
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 169200 Siraj Raval
Introduction to text mining with Voyant
 
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In this introduction to text mining with Voyant I cover: 1) Data cleaning (text editors, Notepad++ and Sublime Text) 2) Loading your text into Voyant 3) Expectations, what Voyant can and cannot do 4) Working with common visualization tools and making possible connections 5) Exporting visualizations
Data Mining Tools for Extremely Large Datasets - WBTShowcase 2010
 
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Presentation on Data Mining Tools for Extremely Large Datasets by NDSU Research Foundation at the WBTShowcase on 3/15/2010.
Views: 801 InnovationArlington
Introduction to Weka
 
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This video cover Introduction to Weka: A Data Mining Tool. This tool is open source, freely available, very light and Java based. It can be used to apply data mining algorithms very easily by using simple GUI.
Executing Open Source Code in Machine Learning Pipelines
 
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Executing Open Source Code in Machine Learning Pipelines of SAS Visual Data Mining and Machine Learning http://support.sas.com/software/products/visual-data-mining-machine-learning/index.html Presenter: Radhikha Myneni Radhikha Myneni demonstrates how to execute open source code, specifically Python and R in SAS Visual Data Mining & Machine Learning pipelines. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 1628 SAS Software
How to Download and Install Orange Data Mining Tool
 
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Reach out to us on www.techontea.com Subscribe to our Channel and Follow us on Social Media to stay updated How to Download and Install Orange Data Mining Tool 1. Download Orange Tool Link: https://orange.biolab.si/download/ 2. Install Orange Data Mining Tool Installation Done , Now you can open any database like Excel DB or any other Database and use Orange Tool Documents from Orange: https://orange.biolab.si/docs/ About Orange Tool: Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative data analysis and interactive data visualization, and can also be used as a Python library. Subscribe and Stay connected with us! 👉 Subscribe on YouTube: https://goo.gl/hJCieo 👉 Like us on Facebook: https://www.facebook.com/techontea 👉 Follow us on Twitter: https://twitter.com/techontea 👉 Follow us on Instagram: https://www.instagram.com/techontea You can always ask for a solution buy just filling your query details on www.techontea.com We will make sure to provide you your solution as soon as possible About: Tech on Tea a show where we talk about Blogging, WordPress, SEO, Entrepreneurship. IT Solutions and give resolutions and tips for your IT Requirements. If you are looking for any technology solutions, learning new things and want to get a standard resolutions for your issues than, this channel is perfect fit for you. Subscribe to stay updated with channel updates.
Views: 125 Tech on Tea
PDF Data Extraction and Automation 3.1
 
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Learn how to read and extract PDF data. Whether in native text format or scanned images, UiPath allows you to navigate, identify and use PDF data however you need. Read PDF. Read PDF with OCR.
Views: 148252 UiPath
Prediction Using Weka Tool- Machine Learning Tutorial
 
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Weka is an Open source Machine Learning Application which helps to predict the required data as per the given parameters
Views: 7701 MOHIT RATNESH
Drag & Drop Data Science
 
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Build your own data science application step-by-step using the intuitive and visual user interface of KNIME Analytics Platform. KNIME Analytics Platform is a free, open source data analytics software which can be downloaded at https://www.knime.com/downloads
Views: 6110 KNIMETV
Data Mining Tool (RapidMiner)
 
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Data Mining Tool Exploration
Views: 1577 Alain Estaris
Answers from Big Data - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 255 Data Analytics
Intro to Web Scraping with Python and Beautiful Soup
 
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Web scraping is a very powerful tool to learn for any data professional. With web scraping the entire internet becomes your database. In this tutorial we show you how to parse a web page into a data file (csv) using a Python package called BeautifulSoup. In this example, we web scrape graphics cards from NewEgg.com. Sublime: https://www.sublimetext.com/3 Anaconda: https://www.anaconda.com/distribution/#download-section If you are not seeing the command line, follow this tutorial: https://www.tenforums.com/tutorials/72024-open-command-window-here-add-windows-10-a.html -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz5HN0 Watch the latest video tutorials here: https://hubs.ly/H0hz5SV0 See what our past attendees are saying here: https://hubs.ly/H0hz5K20 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo #webscraping #python
Views: 556059 Data Science Dojo
Hadoop data mining swiss army knife
 
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You sit on a big pile of data. You have lots of it and it’s great. But how on earth are you gonna make sense out of this awful amount of raw figures ? Hadoop is a great tool for storing and interrogating data, but it can be queried in various ways and it’s not always easy to choose the best one. In this Hands-On, we will play together with 4 typical query languages that will help you mine your data :- Hive- Pure Java Map/Reduce- Pig- Cascading After this session, you’ll be prepared to enter the wonderful world of Hadoop querying. Authors: Pablo Lopez Pablo a 8 ans d’expérience au cours desquels il s’est forgé une solide expérience d’architecte logiciel. Il dispose d’une très large compétence sur l’ensemble de l’écosystème JEE, et notamment les solutions du monde open-source. Opérationnel par goût, il intervient sur une large variété de missions, de l'analyse de performances en production au conseil en architecture logicielle. Bertrand Dechoux Consultant
Views: 50 Parleys
Fundamentals of Data Mining and the Open Software Question
 
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Speaker: Tom Khabaza Tom will introduce the fundamentals of data mining: its applications, the process and the underlying principles, and apply this to data analysis tools. Using a “magic quadrant” for analytical tool design, Tom will overview a number of open and commercial tools, compare their strengths and weaknesses, and suggest how to place open data analysis tools at the top of the data mining heap. Bio: Tom Khabaza, sometimes called “the Isaac Newton of Data Mining” is the Founding Chairman of the Society of Data Miners. A data mining veteran of 25 years and many industries and applications, Tom helped create the world-leading Clementine data mining workbench (now called IBM SPSS Modeler) and the industry standard CRISP-DM analytics methodology, and led the first integrations of data mining and text mining. His recent thought leadership includes the 9 Laws of Data Mining and Predictive Analytics Strategy. ---- SUBSCRIBE to get the latest meetup videos: http://bit.ly/MVUKSubscribe ---- Video by Meetupvideo ( https://www.meetupvideo.com )
Data Analytics Webinar Series: Manipulating Large Spatial Datasets with Free & Open Source Tools
 
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Large data sets often present unique challenges. Processes may fail or take long to complete. This session will cover how to use the free R package to manipulate large geographic data sets as a set of repeatable processes -- helping you to automate your workflow and overcome these unique challenges. Ready to improve your scripting kung fu? During this webinar, you will learn: -how to read and manipulate point datasets (geocoded locations) -how to aggregate points to polygons (such as a census geographies) -how to generate a heatmap -how to combine these steps into a reusable R script Audience: This webinar is targeted at a more technical audience and is appropriate for anyone who has some experience with command line tools or scripting. Level of Difficulty: Experienced
Views: 895 azavea
What does GATE do
 
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This is a example in GATE which shows the results of the default ANNIE pipeline on an English document. In this case the document is "That's what she said" that lovely catch phrase from Michael Scott in The Office TV show http://www.cs.washington.edu/homes/brun/pubs/pubs/Kiddon11.pdf it discusses humor recognition...
Views: 31482 cesine0
Best Practices in Data Mining & Link Analysis
 
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In January, 2016, Crime Tech Solutions - Your Source for Investigation Software - proudly offered a free informational webinar to members of International Association of Crime Analysts (IACA). With renowned expert and published author Chris Westphal as speaker, the webinar seats immediately filled. This is an abbreviated version for those either unable to attend, or simply interested in best practices in data mining and link visualization.
Views: 1805 Doug Wood
K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Edureka
 
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** Python Training for Data Science: https://www.edureka.co/python ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session: 1. What is Clustering? 2. Types of Clustering 3. What is K-Means Clustering? 4. How does a K-Means Algorithm works? 5. K-Means Clustering Using Python Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 52287 edureka!
Extract Facebook Data and save as CSV
 
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Extract data from the Facebook Graph API using the facepager tool. Much easier for those of us who struggle with API keys ;) . Blog Post: http://davidsherlock.co.uk/using-facepager-find-comments-facebook-page-posts/
Views: 211860 David Sherlock
Owen Zhang at ODSC Boston 2015 - Open Source Tools and Data Science Competitions
 
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Owen Zhang - #1 Ranked Kaggle Data Scientist, Chief Product Officer at DataRobot Link to slides: http://www.slideshare.net/odsc/owen-zhangopen-sourcetoolsanddscompetitions1 This talk shares the presenter’s experience with open source tools in data science competitions. In the past several years Kaggle and other competitions have created a large online community of data scientists. In addition to competing with each other for fame and glory, members of this community also generously share knowledge, insights using forum and open source code. The open competition and sharing have resulted in rapid progress in the sophistication of the entire community. This presentation will briefly cover this journey from a competitor’s perspective, and share hands on tips on some open source tools proven popular and useful in recent competitions.
Views: 1423 Open Data Science
Data Science and Open Communities | Fernando Perez | TEDxBerkeley
 
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Fernando Perez, creator of the open source computing tool Jupyter, highlights how open source software and the ease of access to data will enhance human-centered computing, collaboration, and transparency across many areas of society. Fernando Pérez is an assistant professor in Statistics at UC Berkeley and a Faculty Scientist in the Department of Data Science and Technology at Lawrence Berkeley National Laboratory. His research focuses on creating tools for modern computational research and data science across domain disciplines, with an emphasis on high-level languages, interactive and literate computing, and reproducible research. He created IPython while a graduate student in 2001 and co-founded its successor, Project Jupyter. He is a National Academy of Science Kavli Frontiers of Science Fellow and a Senior Fellow and founding co-investigator of the Berkeley Institute for Data Science. He is a co-founder of the NumFOCUS Foundation, and a member of the Python Software Foundation. He is a recipient of the 2012 FSF Award for the Advancement of Free Software, and of the 2017 ACM Software System Award. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Views: 399 TEDx Talks
04 Predictive Analytics Training with Weka (Building a classifier)
 
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Data Mining and Predictive Analytics training course using the open source Weka tool. Videos producted by the University of Waikato, New Zealand. Posted by Rapid Progress Marketing and Modeling, LLC (RPM2) under CC BY 3.0 RPM2 is a full-service Predictive Analytics and Data Sciences Services company specializing in Model Development, Consulting, Direct Marketing Services, and Professional Training. Visit us at http://www.RPMSquared.com/
Views: 16398 Predictive Analytics
CLAVIN -- an open source geoparser
 
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CLAVIN is an award-winning open source software library for document geotagging and geoparsing that employs context-based geographic entity resolution. It automatically extracts place names from text documents and intelligently resolves those names against a gazetteer, disambiguating ambiguous references like "Springfield" based on the surrounding context and enhancing the original unstructured text with data-rich geospatial entities corresponding to the places mentioned in the document. In this tech talk, Charlie Greenbacker, CLAVIN's creator, will describe how the software works, demonstrate a simple application based on CLAVIN, and discuss how it can be used to enable geospatial analysis on unstructured text.
Views: Andrea Ross
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 81233 edureka!
How To Connect Google Webmaster Tools To Google Analytics - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 83 Data Analytics
Big Data Tools and Solutions: The Myths and the Reality
 
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Moderator: Usama Fayyad, Oasis500 Panelists: Richard Rovner, MathWorks, Inc. Ingo Mierswa, Rapid-I GmbH Dan Steinberg, Salford Systems Udo Sglavo, SAS Institute Inc. Abstract: The panel is intended to address a fundamental issue - What roles are the commercial data mining tools having in enabling Data Science? In a world, where most new Data Science is utilizing open source R as the primary tools library, and where in large enterprises analysis efforts are heavily reliant on established classical tools like SAS and MathWorks/MatLab, a question poses itself: Have there been recent changes or successes enabled by new tools? Are tools essential? Do we have the right tools for the modern #BigData world? More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 216 KDD2016 video
CKAN as an open-source data management solution for open data
 
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CKAN is a powerful data management system that makes data accessible – by providing tools to streamline publishing, sharing, finding and using data. CKAN is aimed at data publishers (national and regional governments, companies and organizations) wanting to make their data open and available.
Views: 5834 AIMS CIARD
Data Science Tutorial for Beginners - 1 | What is Data Science? | Data Analytics Tools | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) Data Science Blog Series: https://goo.gl/1CKTyN http://www.edureka.co/data-science Please write back to us at [email protected] or call us at +91-8880862004 for more information. Data Science is all about extracting knowledge from data. Data Science is the integration of methods from mathematics, probability models, machine learning, computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modelling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. This interdisciplinary and cross-functional field leads to decisions that move an organization forward in terms of proposed investment, decisions regarding a product or business strategy. Data Science is a buzzword, often used interchangeably with analytics or big data. At times, Analytics is synonymous with Data Science, but at times it represents something else. A Data Scientist using raw data to build a predictive behaviour model, falls in to the category of analytics. About the Data Science Course at edureka! - This Data Science course is designed to provide knowledge and skills to become a successful Data Scientist. The course covers a range of Hadoop, R and Machine Learning Techniques encompassing the complete Data Science study. Course Objectives After the completion of the Data science Course at Edureka, you should be able to: Gain an insight into the 'Roles' played by a Data Scientist. Analyse Big Data using Hadoop and R. Understand the Data Analysis Life Cycle. Use tools such as 'Sqoop' and 'Flume' for acquiring data in Hadoop Cluster. Acquire data with different file formats like JSON, XML, CSV and Binary. Learn tools and techniques for sampling and filtering data, and data transformation. Understand techniques of Natural Language Processing and Text Analysis. Statistically analyse and explore data using R. Create predictive using Hadoop Mappers and Reducers. Understand various Machine Learning Techniques and their implementation these using Apache Mahout. Gain insight into the visualisation and optimisation of data. Who should go for this course? This course is designed for all those who want to learn machine learning techniques and wish to apply these techniques on Big Data. The course is amalgamation of two powerful open source tools: 'R' language and Hadoop software framework. You will learn how to explore data quantitatively using tools like Sqoop and Flume, write Hadoop MapReduce Jobs, perform Text Analysis and implement Language Processing, learn Machine Learning techniques using Mahout, and optimize and visualize the results using programming language 'R' and Apache Mahout. This course is for you if you are: A SAS, SPSS Analytics Professional. A Hadoop Professional working on Database management and streaming of Big Data. An 'R' professional who wants to apply Statistical techniques on Big Data. A Statistician who wants to understand Data Science methodologies to implement the statistics methods and techniques on Big data. Any Business Analyst who is working on creating reports and dashboards. Pre-requisites Some of the prerequisites for learning Data Science are familiarity with Hadoop, Machine Learning and knowledge of R (recommended not mandatory as these concepts will also be covered during the course). Also, having a statistical background will be an added advantage. Why Learn Data Science? 'Data Science' is a term which came into popularity in past decade. Data Science is the process of extracting valuable insights from "data". It is the right time to learn Data science because: We are living in the Big Data Era, Data Science is becoming a very promising field to harness and process huge volumes of data generated from various sources. A data scientist has a dual role -- that of an "Analyst" as well as that of an "Artist"! Data scientists are very curious, who love large amount of data, and more than that, they love to play with such huge data to reach important inferences and spot trends. You could be one of them! As 'Data Science' is an emerging field, there is a plethora of opportunities available world across. Just browse through any of the job portals; you will be taken aback by the number of job openings available for Data scientists in different industries, whether it is IT or healthcare, Retail or Government offices or Academics, Life Sciences, Oceanography, etc. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 218199 edureka!
Hendrik Heuer - Data Science for Digital Humanities: Extracting meaning from Images and Text
 
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Description Analyzing millions of images and enormous text sources using machine learning and deep learning techniques is simple and straightforward in the Python ecosystem. Powerful machine learning algorithms and interactive visualization frameworks make it easy to conduct and communicate large scale experiments. Exploring this data can yield new insights for researchers, journalists, and businesses. Abstract The focus of this talk is extracting meaning from data and making powerful methods usable by everybody. With the advent of big data, new approaches and technologies are needed to tackle the increase in volume, variety, and velocity of data. This talk illustrates how analysts, journalists, and scientists can benefit from exploratory data analysis and data science. Imagine a journalist who wants to cross-reference the names on the guest list of a parliament with online information about lobbyists to identify which party meets which company. A business analyst might want to quantify what topics certain customers are discussing on Twitter or how their sentiment towards a particular product is. Exploratory data analysis and data science techniques enable researchers, journalists and businesses to ask bigger and more ambitious questions than anybody before them and to leverage the abundance of information that is available today. The Digital Humanities are located at the intersection of computing and the disciplines of the humanities. They can benefit from the massive-scale automated analysis of content like images and text. Researchers, analysts, and journalists can quantify the state of society from publicly available data like tweets. It is now possible to construct an almost complete map of our civilization just by looking at the tags and GPS coordinates of Flickr photos. A vast Python ecosystem is supporting this including machine learning frameworks like scikit-learn, dedicated deep learning frameworks like Keras, and topic modeling tools like gensim. All these tools are open source and can be integrated into powerful data science pipelines. Rather than training neural networks from scratch, pretrained features for text and images can be adapted for fast results. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 773 PyData
RapidMiner Tutorial - Importing Data into RapidMiner (Data Mining and Predictive Analytics System)
 
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A tutorial showing how to import data into RapidMiner. RapidMiner is an open source system for data mining, predictive analytics, machine learning, and artificial intelligence applications. For more information: http://rapid-i.com/ Brought to you by Rapid Progress Marketing and Modeling, LLC (RPM Squared) http://www.RPMSquared.com/
Views: 17714 Predictive Analytics
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
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** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 90653 edureka!
text mining and olap
 
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Views: 241 rajan10000
www.dbmcafe.nl: Text Mining met KNIME
 
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In dit derde deel van de reeks "Mijn eerste kleine Text Mining stapjes" gaan we weer de recensies van een Nokia GSM analyseren. Ditmaal met het open source pakket KNIME. Zie ook de overige afleveringen op: www.dbmcafe.nl
Views: 2285 dbmcafe
29 Predictive Analytics Training with Weka  (The data mining process)
 
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Data Mining and Predictive Analytics training course using the open source Weka tool. Videos producted by the University of Waikato, New Zealand. Posted by Rapid Progress Marketing and Modeling, LLC (RPM2) under CC BY 3.0 RPM2 is a full-service Predictive Analytics and Data Sciences Services company specializing in Model Development, Consulting, Direct Marketing Services, and Professional Training. Visit us at http://www.RPMSquared.com/
Views: 2213 Predictive Analytics