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Web Usage Mining
 
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Clustering of the web users based on the user navigation patterns....
Views: 7311 GRIETCSEPROJECTS
Introduction to WebMining - Part 1
 
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Introduction to Web Mining and its usage in E-Commerce Websites. This is part 1. This will contain introduction of the field and in part two we will discuss its usage in E-Commerce website. Please don't forget to give your feedback... :)
Views: 5076 zdev log
SVM-based Web Content Mining with Leaf Classification Unit from DOM-tree
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 105 Clickmyproject
Content Mining of the bioscience literature
 
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Published on May 14, 2015 by BioCADDIE Project https://www.youtube.com/watch?v=G9LePsd9R9A Abstract: The ContentMine (http://contentmine.org/) has developed Open tools for mining the scientific and medical literature (full text, figures, images and supplemental data). We have developed a pipeline to cover the whole process of Crawling, Scraping, Normalising and Mining articles and storing/republishing the results. We are now doing this on a daily basis. The ContentMine is funded by a Fellowship to PMR from the Shuttleworth Foundation. The aims include the creation of subcommunities, and unrestricted dissemination of all materials, code and results (Apache 2, CC-BY and CC0 as appropriate). We intend to generate publish 100 million facts per year available for use and re-use. The system is designed to allow anyone to create pluggable resources (code, vocabularies) and to make ContentMining easy and available to anyone. Much of our work is through interactive workshops and we hope to show participants how to start ContentMining. Two of our approaches include downloadable virtual machines and a web service. Bio: Dr. Peter Murray-Rust is a chemist currently working at the University of Cambridge. As well as his work in chemistry, Dr. Murray-Rust is also known for his support of open access and open data. He leads the team at the ContentMine project which uses machines to liberate 100,000,000 facts from the scientific literature. After obtaining a Ph.D., he became lecturer in chemistry at the (new) University of Stirling and was first warden of Andrew Stewart Hall of Residence. In 1982 he moved to Glaxo Group Research at Greenford to head Molecular Graphics, Computational Chemistry and later protein structure determination. He was Professor of Pharmacy in the University of Nottingham from 1996-2000, setting up the Virtual School of Molecular Sciences. He is now Reader in Molecular Informatics at the University of Cambridge and Senior Research Fellow of Churchill College, Cambridge. Dr. Murray-Rust's research interests have involved the automated analysis of data in scientific publications, creation of virtual communities (e.g., The Virtual School of Natural Sciences in the Globewide Network Academy and the Semantic Web). With Henry Rzepa he has extended this to chemistry through the development of markup languages, especially Chemical Markup Language. He campaigns for open data, particularly in science, and is on the advisory board of the Open Knowledge Foundation and a co-author of the Panton Principles for Open scientific data. Together with a few other chemists he was a founder member of the Blue Obelisk movement in 2005.
Views: 247 ContentMine
Content Mining of the bioscience literature
 
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Abstract: The ContentMine (http://contentmine.org/) has developed Open tools for mining the scientific and medical literature (full text, figures, images and supplemental data). We have developed a pipeline to cover the whole process of Crawling, Scraping, Normalising and Mining articles and storing/republishing the results. We are now doing this on a daily basis. The ContentMine is funded by a Fellowship to PMR from the Shuttleworth Foundation. The aims include the creation of subcommunities, and unrestricted dissemination of all materials, code and results (Apache 2, CC-BY and CC0 as appropriate). We intend to generate publish 100 million facts per year available for use and re-use. The system is designed to allow anyone to create pluggable resources (code, vocabularies) and to make ContentMining easy and available to anyone. Much of our work is through interactive workshops and we hope to show participants how to start ContentMining. Two of our approaches include downloadable virtual machines and a web service. Bio: Dr. Peter Murray-Rust is a chemist currently working at the University of Cambridge. As well as his work in chemistry, Dr. Murray-Rust is also known for his support of open access and open data. He leads the team at the ContentMine project which uses machines to liberate 100,000,000 facts from the scientific literature. After obtaining a Ph.D., he became lecturer in chemistry at the (new) University of Stirling and was first warden of Andrew Stewart Hall of Residence. In 1982 he moved to Glaxo Group Research at Greenford to head Molecular Graphics, Computational Chemistry and later protein structure determination. He was Professor of Pharmacy in the University of Nottingham from 1996-2000, setting up the Virtual School of Molecular Sciences. He is now Reader in Molecular Informatics at the University of Cambridge and Senior Research Fellow of Churchill College, Cambridge. Dr. Murray-Rust's research interests have involved the automated analysis of data in scientific publications, creation of virtual communities (e.g., The Virtual School of Natural Sciences in the Globewide Network Academy and the Semantic Web). With Henry Rzepa he has extended this to chemistry through the development of markup languages, especially Chemical Markup Language. He campaigns for open data, particularly in science, and is on the advisory board of the Open Knowledge Foundation and a co-author of the Panton Principles for Open scientific data. Together with a few other chemists he was a founder member of the Blue Obelisk movement in 2005.
Views: 82 bioCADDIE Project
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 111293 LearnEveryone
What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning
 
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✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning - INFORMATION RETRIEVAL definition - INFORMATION RETRIEVAL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on full-text or other content-based indexing. Automated information retrieval systems are used to reduce what has been called "information overload". Many universities and public libraries use IR systems to provide access to books, journals and other documents. Web search engines are the most visible IR applications. An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy. An object is an entity that is represented by information in a content collection or database. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching. Depending on the application the data objects may be, for example, text documents, images, audio, mind maps or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata. Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.
Views: 13747 The Audiopedia
Is web scraping a career? How can it make money?
 
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This video is a reply to an important comment that I received on the channel. The viewer was asking whether web scraping can be regarded as a job or a career. Searching on indeed.com, the viewer found only three jobs when using 'web scraping' as a keyword. In this video I tell you what you can do with web scraping to make it a full time job instead of just a hobby.
Views: 16368 Ahmad Al Fakharany
Web Mining
 
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Tecnología de la información II-- Created using PowToon -- Free sign up at http://www.powtoon.com/ . Make your own animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 731 Cristian angulo
WDM 112: How a Web Crawler Works
 
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What is crawling For Full Course Experience Please Go To http://mentorsnet.org/course_preview?course_id=1 Full Course Experience Includes 1. Access to course videos and exercises 2. View & manage your progress/pace 3. In-class projects and code reviews 4. Personal guidance from your Mentors
Views: 27724 Oresoft LWC
What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning, definition & explanation
 
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What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning - TEXT MINING definition - TEXT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining" in 2004 to describe "text analytics." The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s, notably life-sciences research and government intelligence. The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80 percent of business-relevant information originates in unstructured form, primarily text. These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing.
Views: 2377 The Audiopedia
web scraping using python for beginners
 
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Learn Python here: https://courses.learncodeonline.in/learn/Python3-course In this video, we will talk about basics of web scraping using python. This is a video for total beginners, please comment if you want more videos on web scraping fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com Download LearnCodeOnline.in app from Google play store and Apple App store
Views: 166915 Hitesh Choudhary
What is Web Mining
 
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Views: 13878 TechGig
Web Crawler - CS101 - Udacity
 
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Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/f16/ Sergey Brin, co-founder of Google, introduces the class. What is a web-crawler and why do you need one? All units in this course below: Unit 1: http://www.youtube.com/playlist?list=PLF6D042E98ED5C691 Unit 2: http://www.youtube.com/playlist?list=PL6A1005157875332F Unit 3: http://www.youtube.com/playlist?list=PL62AE4EA617CF97D7 Unit 4: http://www.youtube.com/playlist?list=PL886F98D98288A232& Unit 5: http://www.youtube.com/playlist?list=PLBA8DEB5640ECBBDD Unit 6: http://www.youtube.com/playlist?list=PL6B5C5EC17F3404D6 Unit 7: http://www.youtube.com/playlist?list=PL6511E7098EC577BE OfficeHours 1: http://www.youtube.com/playlist?list=PLDA5F9F71AFF4B69E Join the class at http://www.udacity.com to gain access to interactive quizzes, homework, programming assignments and a helpful community.
Views: 127814 Udacity
Semi-unsupervised learning of taxonomic and non-taxonomic relationships from the web
 
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Due to the size of the World Wide Web, it is necessary to develop tools for automatic or semi-automatic analyses of web data, such as finding patterns and implicit information in the web, a task usually known as Web Mining. In particular, web content mining consists of automatically mining data from textual web documents that can be represented with machine-readable semantic formalisms. While more traditional approaches to Information Extraction from text, such as those applied to the Message Understanding Conferences during the nineties, relied on small collections of documents with many semantic annotations, the characteristics of the web (its size, redundancy and the lack of semantic annotations in most texts) favor efficient algorithms able to learn from unannotated data. Furthermore, new types of web content such as web forums, blogs and wikis, are also a source of textual information that contain an underlying structure from which specialist systems can benefit. This talk will describe an ongoing project for automatically acquiring ontological knowledge (both taxonomic and non-taxonomic relationships) from the web in a partially unsupervised way. The proposed approach combines distributional semantics techniques with rote extractors. A particular focus will be set on an automatic addition of semantic tags to the Wikipedia with the aim of transforming it, with small effort, into a Semantic Wikipedia.
Views: 22 Microsoft Research
SVM-based Web Content Mining with Leaf Classification Unit from DOM-tree
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ https://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 25 myproject bazaar
#FixCopyright:  Copyright & Research - Text & Data Mining (TDM) Explained
 
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Read our blog post analysing the European Commission's (EC) text and data mining (TDM) exception and providing recommendations on how to improve it: http://bit.ly/2cE60sp Copy (short for Copyright) explains what text and data mining (TDM) is all about, and what hurdles researchers are currently facing. We also have a blog post on the TDM bits in the EC's Impact Assessment accompanying the proposal: http://bit.ly/2du9sYe Read more about the EC's copyright reform proposals in general: http://bit.ly/2cvAh0a
Views: 3324 FixCopyright
Machine Learning and Data Science
 
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link: https://courses.learncodeonline.in/learn/Machine-Learning-Bootcamp Machine Learning and Data Science Companies like Facebook, Google and Amazon have got a lot of data about us. Even the small companies have got a lot of data like signup information, number of logins, Product purchase, products that we are looking for. All this data can be processed and can give any company a boost in productivity and increase in sale. That is why machine learning is growing so fast. Companies can offer amazing features like quick replies that are context based in Gmail, Uber driver arrival time or time to reach at the destination via Google maps, self-driving cars etc. This is just a start of machine learning and power of data science. Welcome to data science and machine learning course! One of the best online resource to understand and implement Machine learning and data science concepts. Usually people thinks that data science can only be learn by Ph.D but that not true, anyone can learn data science and machine learning. Desktop: https://amzn.to/2GZ0C46 Laptop that I use: https://amzn.to/2Goui9Q Wallpaper: https://imgur.com/a/FYHfk Facebook: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com Download LearnCodeOnline.in app from Google play store and Apple App store
Views: 91072 Hitesh Choudhary
Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 12790 Stat Pharm
Data Mining | Web Scraping
 
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http://www.datatudetechnologies.com/ - We provide data mining, eBay templates, web scraping, data extraction, web automation, Amazon automation, eBay automation services to our clients around the globe.
Data mining  harvesting and analytics. ( All you  need to know)
 
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There is a whirlwind of videos and info on this but none that explained it properly to me. I went online and found out everything I needed to know about the data breaches and the implications of those breaches! I provided some links below in case you wish to educate yourself on whats happening with YOUR data! https://www.quora.com/What-is-the-difference-between-data-analytics-and-data-mining-1 https://www.connotate.com/are-you-screen-scraping-or-data-mining/ http://searchdatamanagement.techtarget.com/definition/data-scrubbing What is the difference between data warehousing and data mining? The main difference between data warehousing and data mining is that data warehousing is the process of compiling and organizing data into one common database, whereas data mining is the process of extracting meaningful data from that database.
Views: 50 Elle's place
Introduction to Text Analytics with R: Overview
 
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The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and initial data cleaning – Some initial data analysis, feature engineering, and data visualization About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- 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
Views: 68966 Data Science Dojo
Data- What is the Importance of DATA in Tamil?
 
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Data is collection of information . Data store and Data process Play List : https://www.youtube.com/playlist?list=PLLa_h7BriLH2U05m3eN43779AnrmieYHz YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English
Views: 18242 atoz knowledge
PHD RESEARCH TOPIC IN TEXT MINING
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-contextaware-computing/
Views: 691 PHD Projects
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1594 Kakoli Bandyopadhyay
Data Collection and Preprocessing | Lecture 6
 
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Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07 Highlights: Garbage-in, Garbage-out Dataset Bias Data Collection Web Mining Subjective Studies Data Imputation Feature Scaling Data Imbalance #deeplearning #machinelearning
Views: 1545 Leo Isikdogan
What are the current challenges in Knowledge Discovery? - Ron Daniel
 
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Video recorded at the Workshop On mining Scientific Publications, 19th-23rd June at The University of Toronto, as a part of JCDL 2017 (Joint Conference on Digital Libraries).
Views: 51 OpenMinTeD
What is the world wide web? - Twila Camp
 
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View full lesson: http://ed.ted.com/lessons/what-is-the-world-wide-web-twila-camp The world wide web is used every day by millions of people for everything from checking the weather to sharing cat videos. But what is it exactly? Twila Camp describes this interconnected information system as a virtual city that everyone owns and explains how it's organized in a way that mimics our brain's natural way of thinking. Lesson by Twila Camp, animation by Flaming Medusa Studios Inc.
Views: 500833 TED-Ed
How to do web scraping? What is web-scraping? How to earn money with webscraping?
 
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Full course: http://bit.ly/scraping2019 You will learn everything you need to upgrade yourself from a complete newcomer to skillful websraper specialist. Easy to understand video tutorials for required soft installation, Python programming basics, and web-scraping mechanics. You will get ready to use scripts for every typical situation. You will learn: Python programming basics Static web-pages scrapping using CSS-selectors, proper web-requests, and BeutifulSoup Python library Dynamic (JavaScript enabled) scrapping and web-page manipulation automation with PhantomJS and Selenium Extracting data via API (open data sources, Twitter, Facebook, Instagram, YouTube) Working with JSON data format Regular expressions for text parsing "Hacking" servers for undocumented API data extraction This course will be useful for: Beginner Python developers Data scientists who need to extract web-data Marketing specialists who need lead generation from the web Anyone interested in web scrapping, parsing and data API "hacking" how to web scraping python? how to web scraping using python? how to do web scraping in python? how to do web scraping in r? how to do web scraping using python? how does web scraping work? how web scraping make money? is web scraping legal? what is web scraping used for? web scraping across multiple pages, web scraping after login, web scraping behind login, web scraping by python, web scraping for profit, web scraping for money, web scraping for beginners, web scraping for dummies, web scraping for data science with python, web scraping for keywords, web scraping for dropshipping, web scraping from scratch, web scraping from linkedin, web scraping from yahoo finance, web scraping from facebook, web scraping from wikipedia, scraping web data into excel, web scraping through python, web scraping to csv, web scraping to make money, web scraping to json, web scraping vs crawling, web scraping and parsing with beautifulsoup and python introduction, a web scraping methodology for bypassing twitter api restrictions, web scraping basics, web scraping chrome extension, web scraping craigslist, web scraping dynamic content python, web scraping data mining, web scraping definition, web scraping free tools, web scraping financial data, web scraping google search results, web scraping hacking, phantomjs install windows, phantomjs tutorial, phantomjs button click, phantomjs bot, phantomjs examples, phantomjs extract html, phantomjs execute javascript, phantomjs full page screenshot, phantomjs tutorial for beginners, phantomjs get page source, phantomjs login example, selenium webdriver, selenium automation, selenium automation framework, selenium beginner, what is css selector in selenium? difference between css selector and xpath, beautifulsoup css selector example, find css selector chrome, selectorgadget, how regular expressions work? what are regular expressions? what are regular expressions in programming? regular expressions in python, regular expressions examples, upwork easy work, upwork earning, upwork easy jobs, upwork freelance, upwork get job, upwork python, how to do upwork? is upwork good? upwork for beginners, upwork per hour, upwork fixed price, earn money through upwork, earning through upwork, upwork with no skills, upwork with no experience, how to do freelance work? how to do freelance work online? how to do freelance programming? how freelancer get paid? how freelancer earn money? freelance jobs, freelance work, freelance developer, freelance 2k19, freelance coding, freelance coder, freelance data analyst, freelance easy jobs, freelance it, freelance income, freelance upwork, instagram scraper, facebook scraper, youtube scraper
Semantic Web Mining
 
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Semantic Web Mining by Dr. S Yasodha
Views: 439 Krish eClasses
What is VIDEO MOTION ANALYSIS? What does VIDEO MOTION ANALYSIS mean? VIDEO MOTION ANALYSIS meaning
 
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What is VIDEO MOTION ANALYSIS? What does VIDEO MOTION ANALYSIS mean? VIDEO MOTION ANALYSIS meaning - VIDEO MOTION ANALYSIS definition - VIDEO MOTION ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Video motion analysis is a technique used to get information about moving objects from video. Examples of this include gait analysis, sport replays, speed and acceleration calculations and, in the case of team or individual sports, task performance analysis. The motions analysis technique usually involves a high-speed camera and a computer that has software allowing frame-by-frame playback of the video. Traditionally, video motion analysis has been used in scientific circles for calculation of speeds of projectiles, or in sport for improving play of athletes. Recently, computer technology has allowed other applications of video motion analysis to surface including things like teaching fundamental laws of physics to school students, or general educational projects in sport and science. In sport, systems have been developed to provide a high level of task, performance and physiological data to coaches, teams and players. The objective is to improve individual and team performance and/or analyse opposition patterns of play to give tactical advantage. The repetitive and patterned nature of sports games lends itself to video analysis in that over a period of time real patterns, trends or habits can be discerned. Police and forensic scientists analyse CCTV video when investigating criminal activity. Police use software which performs video motion analysis to search for key events in video and find suspects. A digital video camera is mounted on a tripod. The moving object of interest is filmed doing a motion with a scale in clear view on the camera. Using video motion analysis software, the image on screen can be calibrated to the size of the scale enabling measurement of real world values. The software also takes note of the time between frames to give a movement versus time data set. This is useful in calculating gravity for instance from a dropping ball. Sophisticated sport analysis systems such as those by Verusco Technologies in New Zealand use other methods such as direct feeds from satellite television to provide real-time analysis to coaches over the Internet and more detailed post game analysis after the game has ended. There are many commercial packages that enable frame by frame or real-time video motion analysis. There are also free packages available that provide the necessary software functions. These free packages include the relatively old but still functional Physvis, and a relatively new program called PhysMo which runs on Macintosh and Windows. Upmygame is a free online application. VideoStrobe is free software that creates a strobographic image from a video; motion analysis can then be carried out with dynamic geometry software such as GeoGebra. The objective for video motion analysis will determine the type of software used. Prozone and Amisco are expensive stadium-based camera installations focusing on player movement and patterns. Both of these provide a service to "tag" or "code" the video with the players' actions, and deliver the results after the match. In each of these services, the data is tagged according to the company's standards for defining actions. Verusco Technologies are oriented more on task and performance and therefore can analyse games from any ground. Focus X2 and Sportscode systems rely on the team performing the analysis in house, allowing the results to be available immediately, and to the team's own coding standards. MatchMatix takes the data output of video analysis software and analyses sequences of events. Live HTML reports are generated and shared across a LAN, giving updates to the manager on the touchline while the game is in progress.
Views: 215 The Audiopedia
Intro into Text Mining and Analytics - Chapter 1
 
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Text Mining and Analytics Intro into Text Mining and Analytics - Chapter 1 This video tutorials cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications. analytics | analytics tools | analytics software | data analysis programs | data mining tools | data mining | text analytics | strucutred data | unstructured data |text mining | what is text mining | text mining techniques More Articles, Scripts and How-To Papers on http://www.aodba.com
Views: 374 AO DBA
link mining
 
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Subscribe today and give the gift of knowledge to yourself or a friend link mining Link Mining . Lise Getoor Department of Computer Science University of Maryland, College Park. Link Mining. Traditional machine learning and data mining approaches assume: A random sample of homogeneous objects from single relation Real world data sets: Slideshow 2979172 by zaina show1 : Link mining show2 : Link mining1 show3 : Outline show4 : Linked data show5 : Sample domains show6 : Example linked bibliographic data show7 : Link mining tasks show8 : Link based object classification show9 : Link type show10 : Predicting link existence show11 : Link cardinality estimation i show12 : Link cardinality estimation ii show13 : Object identity show14 : Link mining challenges show15 : Logical vs statistical dependence show16 : Model search show17 : Feature construction show18 : Aggregation show19 : Selection show20 : Individuals vs classes show21 : Instance based dependencies show22 : Class based dependencies show23 : Collective classification show24 : Model selection estimation show25 : Collective classification algorithm show26 : Collective classification algorithm1 show27 : Labeled unlabeled data show28 : Link mining show29 : Link prior probability show30 : Summary show31 : References
Views: 136 Magalyn Melgarejo
WEB MINING PROJECTS IN TAMILNADU
 
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DOTNET PROJECTS,2013 DOTNET PROJECTS,IEEE 2013 PROJECTS,2013 IEEE PROJECTS,IT PROJECTS,ACADEMIC PROJECTS,ENGINEERING PROJECTS,CS PROJECTS,JAVA PROJECTS,APPLICATION PROJECTS,PROJECTS IN MADURAI,M.E PROJECTS,M.TECH PROJECTS,MCA PROJECTS,B.E PROJECTS,IEEE PROJECTS AT MADURAI,IEEE PROJECTS AT CHENNAI,IEEE PROJECTS AT COIMBATORE,PROJECT CENTER AT MADURAI,PROJECT CENTER AT CHENNAI,PROJECT CENTER AT COIMBATORE,BULK IEEE PROJECTS,REAL TIME PROJECTS,RESEARCH AND DEVELOPMENT,INPLANT TRAINING PROJECTS,STIPEND PROJECTS,INDUSTRIAL PROJECTS,MATLAB PROJECTS,JAVA PROJECTS,NS2 PROJECTS, Ph.D WORK,JOURNAL PUBLICATION, M.Phil PROJECTS,THESIS WORK,THESIS WORK FOR CS
Views: 74 Ranjith Kumar
Multimedia in Hindi
 
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Multimedia
Views: 92834 All in one
Web Scraping for Beginners
 
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Learn how to get started with retrieving content automatically from the web with these techniques and tools of the trade. Note that this video is not meant to be a complete guide to web scraping but an aid to get started.
Views: 1903 cubiclesoft
SAXually Explicit Images: Data Mining Large Shape Databases
 
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Google TechTalks May 12, 2006 Eamonn Keogh ABSTRACT The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining. Motif Discovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images. Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image. As we will show, both these problems have applications in fields as diverse as anthropology, crime...
Views: 4698 Google
Mining articles for practical insight for content creation - Łukasz Dziekan, Michał Stolarczyk
 
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Description As a support to our marketing team we have created a tool which analyzes article headlines and contents. It gives insights how to create headlines and models potential "virality" of the content piece, This was particularly challenging because of limited support for NLP in polish language. And it is actually used by our marketing team. Abstract Using Facebook API we have collected data from fanpages of Polish portals publishing articles in the internet. Based on number of shares, comments, likes and other reactions we defined the virality coefficient, which allows us to measure how much potential each article has to become viral, and therefore being particularly interesting in terms of marketing potential. Given this dataset, we wanted to classify the most catchy phrases occurring in article titles and to check if the content actually matters. We examined how these best phrases change over time, did clustering based on their meaning. Moreover, we automated the process of distinguishing between phrases being one-time events (27-1) and those occurring regularly. We also consider impact of other features of the headline on the virality of the article. Additionally we examine the formatting features based on article content and formatting. Higher level virality analysis concerns linking articles covering the same topic, which requires inclusion of our dataset HTML code of article and text (body) extraction out of it. During our speech we will cover the following areas: Data collection: facebook API (headline, article link, reactions) downloading HTML code article text extraction Data preprocessing: stemming tokenization Analysis: token, bigram, trigram, starting and ending phrases frequencies and scores variance and entropy – automatic detection of one-off, regular and seasonal headlines/topics x-validation on different time intervals and using different news-sources virality score vs headline length Analyses : all of the above analyses for article text and HTML code topic analysis (LDA) Modeling: ensemble modeling to for regression algorithms/classification algorithms to predict virality 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: 1194 PyData
Sentiment Analysis in 4 Minutes
 
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Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&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: 100601 Siraj Raval
How does a blockchain work - Simply Explained
 
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What is a blockchain and how do they work? I'll explain why blockchains are so special in simple and plain English! 💰 Want to buy Bitcoin or Ethereum? Buy for $100 and get $10 free (through my affiliate link): https://www.coinbase.com/join/59284524822a3d0b19e11134 📚 Sources can be found on my website: https://www.savjee.be/videos/simply-explained/how-does-a-blockchain-work/ 🐦 Follow me on Twitter: https://twitter.com/savjee ✏️ Check out my blog: https://www.savjee.be ✉️ Subscribe to newsletter: https://goo.gl/nueDfz 👍🏻 Like my Facebook page: https://www.facebook.com/savjee
Views: 2755678 Simply Explained - Savjee
Predicting the Winning Team with Machine Learning
 
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Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ 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: 94096 Siraj Raval
What is Data Entry in Hindi
 
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here subject is what is data entry in hindi. A data entry work is similar job to a typist in which data entry staff employed to enter or update data into a computer system database, often from paper documents using a keyboard, optical scanner, or data recorder.The keyboards used can often have specialist keys and multiple colors to help in the task and speed up the work. While requisite skills can vary depending on the nature of the data being entered, few specialized skills are usually required, aside from touch typing proficiency with adequate speed and accuracy. The ability to focus for lengthy periods is necessary to eliminate or at least reduce errors. When dealing with sensitive or private information such as medical, financial or military records, a person's character and discretion becomes very relevant as well. Beyond these traits, no technical knowledge is generally required and these jobs can even be worked from home. The invention of punch card data processing in the 1890's created a demand for many workers, typically women, to run key-punch machines. It was common practice to ensure accuracy by entering data twice, the second time on a verifier, a separate, keyboard-equipped machine, such the IBM 056. In the 1970's, punch card data entry was gradually replaced by the use of video display terminals. Reference:-https://en.wikipedia.org/wiki/Data_entry_clerk Reference:-https://www.upwork.com/ Subscribe:- goo.gl/9TVZ3I Watch How To Type Fast in Just 3 Weeks :- https://youtu.be/HE-3bpYvGc4 Check my Google plus :- https://plus.google.com/+Introtuts
Views: 598138 Introtuts
The Bird Poop That Changed The World
 
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Thanks to my grandmother for inspiring this story, and to my mother for helping make it. If you like our videos, please consider supporting MinuteEarth on Patreon! - Alex Bird poop was the gateway fertilizer that turned humanity onto the imported-chemical-based farming system of modern agriculture. Thanks to our Patreon patrons https://www.patreon.com/MinuteEarth and our YouTube members. ___________________________________________ To learn more, start your googling with these keywords: Guano: seabird (or bat) poop. From the indigenous Peruvian word “wanu”, meaning “manure that’s good for fertilizer" Manure: animal poop used as fertilizer (typically cow or pig poop) Fertilizer: a chemical-containing substance added to soil to provide nutrients to plants Nitrate mining: digging up the naturally occurring solid form of the element nitrogen (sodium nitrate) Phosphate mining: digging up the naturally occurring solid form of the element phosphorus Haber-Bosch process: the major industrial method to take nitrogen gas out of the air and convert it to ammonia ___________________________________________ If you liked this week’s video, you might also like: Our fertilizer is killing us. Here's a fix: https://grist.org/article/billionaires-and-bacteria-are-racing-to-save-us-from-death-by-fertilizer/ Why bird poop is white: https://www.audubon.org/news/what-makes-bird-poop-white In 1856 US Congress enabled US citizens to take over unclaimed islands with guano on them: http://americanhistory.si.edu/norie-atlas/guano-islands-act Guano is in demand again today: https://www.nytimes.com/2008/05/30/world/americas/30peru.html _________________________________________ Subscribe to MinuteEarth on YouTube: http://goo.gl/EpIDGd Support us on Patreon: https://goo.gl/ZVgLQZ And visit our website: https://www.minuteearth.com/ Say hello on Facebook: http://goo.gl/FpAvo6 And Twitter: http://goo.gl/Y1aWVC And download our videos on itunes: https://goo.gl/sfwS6n ___________________________________________ Credits (and Twitter handles): Script Writer, Video Director, and Narrator: Alex Reich (@alexhreich) Video Illustrator: Jesse Agar (@JesseAgarYT) With Contributions From: Henry Reich, Ever Salazar, Peter Reich, David Goldenberg Music by: Nathaniel Schroeder: http://www.soundcloud.com/drschroeder Image Credits: Farquhar, W.H. 1884. The Annals of Sandy Spring, Vol. I, Pg. xxix-xxx. Baltimore: Cushings & Bailey. http://bit.ly/2QOWGKr ___________________________________________ References: Canby, T.Y. 2002. The Annals of Sandy Spring, Vol. VI. Introduction: Pg. 26-27. Sandy Spring Museum. Cushman, G.T. 2013. Guano and the opening of the Pacific World: A global ecological history. Cambridge University Press. Cushman, G.T., personal communication, October 2018. Farquhar, W.H. 1884. The Annals of Sandy Spring, Vol. I, Pg. xxix-xxx. Baltimore: Cushings & Bailey. http://bit.ly/2QOWGKr Lorimor, J., Powers, W., Sutton, A. 2004. Manure Characteristics. MWPS-18, Section 1. Second Edition. Table 6. Iowa State University, Ames, Iowa. http://msue.anr.msu.edu/uploads/files/ManureCharacteristicsMWPS-18_1.pdf Robinson, M.B. April 26, 2007. In Once-Rural Montgomery, a Rich History. The Washington Post. http://www.washingtonpost.com/wp-dyn/content/article/2007/04/25/AR2007042501342.html S. Sands & Son. 1875. The American Farmer: Devoted to Agriculture, Horticulture and Rural Life. Vol. 4, Issue 12, pg. 417-418. Baltimore. https://play.google.com/books/reader?id=ul1TAAAAYAAJ&hl=en&pg=GBS.PA417 Stabler, H.O. 1950. The Annals of Sandy Spring, Vol. V, Pg. 43. American Publishing Company. Szpak, P., et al. 2012. Stable isotope biogeochemistry of seabird guano fertilization: results from growth chamber studies with Maize (Zea mays). PloS one, 7(3), e33741. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0033741 Thanks also to the Sandy Spring Museum: https://www.sandyspringmuseum.org/
Views: 357209 MinuteEarth
Ontologies
 
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Dr. Michel Dumontier from Stanford University presents a lecture on "Ontologies." Lecture Description Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery. View slides from this lecture: https://drive.google.com/open?id=0B4IAKVDZz_JUVjZuRVpMVDMwR0E About the Speaker Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars