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Mining data on Facebook with Python: 1- Setting up our app for mining data on Facebook
 
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In this tutorial we will set up our app to mine data from Facebook. We will be introduces to the Facebook API Graph and setting up user token access. Let's connect out app to communicate with the Graph API to get started mining data on this huge platform. ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 13633 Sukhvinder Singh
Extract Information from LinkedIn
 
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Many people asked “Is there any way to scrape data from public profiles on LinkedIn?"You may want to pull some info from LinkedIn, like people who followed your company, members information of a group. In this video, I’m going to share with you how to scrape data from LinkedIn public profiles. For more information please check out www.octoparse.com.
Views: 33665 Octoparse
Automate Social - Grab Social Data with Python - Part 1
 
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Coding with Python - Automate Social - Grab Social Data with Python - Part 1 Coding for Python is a series of videos designed to help you better understand how to use python. In this video we discover a API that will help us grab social data (twitter, facebook, linkedin) using just a person's email address. API - FullContact.com Django is awesome and very simple to get started. Step-by-step tutorials are to help you understand the workflow, get you started doing something real, then it is our goal to have you asking questions... "Why did I do X?" or "How would I do Y?" These are questions you wouldn't know to ask otherwise. Questions, after all, lead to answers. View all my videos: http://bit.ly/1a4Ienh Get Free Stuff with our Newsletter: http://eepurl.com/NmMcr The Coding For Entrepreneurs newsletter and get free deals on premium Django tutorial classes, coding for entrepreneurs courses, web hosting, marketing, and more. Oh yeah, it's free: A few ways to learn: Coding For Entrepreneurs: https://codingforentrepreneurs.com (includes free projects and free setup guides. All premium content is just $25/mo). Includes implementing Twitter Bootstrap 3, Stripe.com, django south, pip, django registration, virtual environments, deployment, basic jquery, ajax, and much more. On Udemy: Bestselling Udemy Coding for Entrepreneurs Course: https://www.udemy.com/coding-for-entrepreneurs/?couponCode=youtubecfe49 (reg $99, this link $49) MatchMaker and Geolocator Course: https://www.udemy.com/coding-for-entrepreneurs-matchmaker-geolocator/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Marketplace & Dail Deals Course: https://www.udemy.com/coding-for-entrepreneurs-marketplace-daily-deals/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Free Udemy Course (40k+ students): https://www.udemy.com/coding-for-entrepreneurs-basic/ Fun Fact! This Course was Funded on Kickstarter: http://www.kickstarter.com/projects/jmitchel3/coding-for-entrepreneurs
Views: 45656 CodingEntrepreneurs
Mining Twitter with Python : 2 - Collecting data from Twitter
 
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In order to interact with the Twitter APIs, we need a Python client that implements the different calls to the APIs itself. We will use Tweepy in these tutorials and see how to build our application in multiple parts to collects data from our own Twitter timeline and other users timeline. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 6538 Sukhvinder Singh
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 Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. 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: 46247 edureka!
Mining data on Facebook with Python: 4- Getting posts from a Facebook page and do some analysis
 
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We will get some basic information from a Page and then download all posts in a jsonl file to later do some visual analysis on the data collected. We will end this video tutorial series with a wordcloud analysis of the posts collected from a page. ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 3046 Sukhvinder Singh
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.continuum.io/downloads#wi... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3500+ employees from over 700 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f6wzS0 See what our past attendees are saying here: https://hubs.ly/H0f6wzY0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 388880 Data Science Dojo
How to Use LinkedIn Sales Navigator to Generate Leads?
 
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At the top of my list for best sales tools are Yesware and LinkedIn Sales Navigator. LinkedIn Sales Navigator is the premium version of LinkedIn. In this video I go into is it worth having and how can you use it to generate leads. Sales Navigator is one of the easiest tools that pay for themselves and in this video I want to show you how we use Sales Navigator and how you can use it to generate leads and how you can speed up all your processes. It’s $64.99 a month The thing to look at with LinkedIn Sales Navigator is Lead Builder. [0:54] You take your ideal client profile and you can put them in here and find the types of leads you’re looking for. Video on ideal client discovery - https://www.youtube.com/watch?v=mwSm-MEb9m0 So for Experiment 27 we’re always looking for founders at digital agencies so we need. Geography: United States. Title: CEO founder managing director. 51 to 200 people and also 200-500. Digital agency is the keyword. And it looks like we have 6,409 results. Before you send any emails just be sure to check all of these, you don’t want to blast your email out to everyone. All the info you need is their name and website. And then use the add ons we talked about previously to split the names and get the email addresses. You use split names to get this split and use the email hunter add on to find the email address. And that's how easy it is. You can have your VAs or yourself go through any of these lead lists and the only things worth tweaking is coming up with the right searches to find the highest quality leads. Sales Navigator's pretty good but you still have to make sure you check everything to be sure that it’s solid. I recommend Sales Navigator. Let me know if you have any feedback or you want me to run through your lead criteria. Just post it down in the comments and I’ll take a look. ------------Support the channel: Support this channel: https://www.patreon.com/alexberman SUBSCRIBE for more videos like this: http://youtube.com/alxberman?sub_confirmation=1 Check out our Digital Agency Marketing Podcast: https://itunes.apple.com/hr/podcast/digital-agency-marketing-1/id1200614219?mt=2&ls=1 Need lead generation or marketing support for your agency? Check out http://experiment27.co . /// R E S O U R C E S Get the sales and service agreement we use to close business (free client contract template) [$1,000 value]: http://bit.ly/2mpyFLs Get the actual questions we use to qualify clients on the first call: https://experiment27.lpages.co/discovery-call-structure-and-questions/ Free Sales Courses: https://experiment27.teachable.com/ __ /// MORE FROM ALEX Subscribe for more videos: http://youtube.com/alxberman The Alex Berman Podcast: https://itunes.apple.com/hr/podcast/digital-agency-marketing-1/id1200614219 __ /// WORK WITH ALEX More enterprise clients for your agency: http://experiment27.com Turn your book into a documentary: https://loreliapictures.com/ Book a one on one with Alex: http://experiment27.com/consult __ /// BUSINESS INQUIRIES: For sponsorships you can reach me at: [email protected]
Views: 57659 Alex Berman
Episode 3: How to Visualize Your Linkedin Contact Data
 
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Ever wonder how you might create map of all your LinkedIn contacts? Check out episode 3 of our new TechChange video series--Data Day-- in which Nick and Samhir piece through Nick's LinkedIn network data using a powerful data visualization tool called kumu to reveal key relationships. https://kumu.io/ Stay tuned for future episodes and don't forget to subscribe! Follow TechChange on Twitter at @TechChange and see our latest course listings here: https://www.techchange.org/online-courses/
Views: 1335 TechChange
Linked-Deed(Job Recommendation System)
 
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Final project for Data Mining Course 2017, Computer Science, University of Victoria Uses Data Extraction, pre-processing and mining to find the best Indeed jobs on the basis of a linkedin profile Note: Shows best jobs on the basis of linkedin profile skills for now, but it can be extended in future Contributors: Aigerim Mashkanova, Disha Garg, Pooja Bhojwani Professor: Alex Thomo
Views: 310 pooja bhojwani
Lesson-01 :Introduction & Understanding Graph API - Facebook Data Analysis with Python
 
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UPDATE** Since facebook updated their policies and made it more limited, some of the requests shown in the video may not work anymore. please refer to the facebook Graph API Official documentation for more info In this video I will introduce you to the GRAPH API, I will use the GRAPH API Explorer and show you some example requests. contact me on: https://www.linkedin.com/in/nourgalaby/ https://www.upwork.com/fl/ngalaby
Views: 22925 Nour Galaby
How to Create LinkedIn App, Client ID, and Client Secret
 
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Read tutorial from CodexWorld.com - https://www.codexworld.com/create-linkedin-app-client-id-secret/ LinkedIn App Creation - Step-by-step guide to creating a LinkedIn app, client id, client secret, API key, app id. Create a LinkedIn app and connect with LinkedIn API. Subscribe for more tutorials: https://www.youtube.com/codexworld Stay Connected With Us: Website: http://www.codexworld.com Google+: https://plus.google.com/+codexworld Facebook: https://www.facebook.com/codexworld Twitter: https://twitter.com/codexworldblog
Views: 3755 CodexWorld
George Webb - LinkedIn Resume
 
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George Webb's resume from LinkedIn. The resume of George Webb (aka George Webb Sweigert).includes George Webb's past work experience. George appears to have been an outstanding sales and marketing executive. Source information below. For discussion. Account Executive - Digital Marketing, Big Data, Networking, Saas, and Database technologies Specialties: Strategic selling, account development, account management, prospecting, qualifying, secure marquee accounts.Specializing in developing call plans to land new accounts, crafting messaging for effective prospecting, and closing with unique customer benefit presentations. Saas, Web analytics, behavioral analytics, data mining, large scale database, Java middleware, network management, network troubleshooting, application availability and performance are areas of technical experience. SOURCE: https://www.linkedin.com/in/gwebb/ .
Views: 193 Conservative Eagle
LinkedIn Full Automation Software You Have Ever Dreamt Of
 
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This software completely automates all processes of LinkedIn lead generation and sales funnel management, including multi-criteria search, data mining, sending personalized connection requests, Sales Navigator support, bulk messaging and 3rd party integration. Key features: # multi-criteria search # data export/import # Sales Navigator on-board # automatic sending of personalized connection requests (with the option to send a custom note) # automatic sending of personalized messages # runs by schedule # works in the cloud # multiple accounts support # proxies support # email notifications # integration with Zapier, MailChimp, Infusionsoft, Salesforce (over Zapier) and others # integration with 3rd party APIs # can work 24/7 # accounts are not get banned # and etc, etc... This is an exclusive custom-build solution, the tool is quite flexible and can be easily modified according to your specific needs. Moreover, any additional features you'd like to see there will be developed on demand and added to the core application in the shortest possible time. Is it something you are interested in? Please, contact [email protected] to learn more.
Views: 67 Michael Lomov
LinkedIn API Demo
 
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Demo of https://github.com/zoonman/linkedin-api-php-client library. Unfortunately there is no sound. Sorry.
Views: 900 ZoonMan
Facebook Data Mining Not Unusual
 
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Data leak stormbrewer Aleksandr Kogan denies any illegality through his App, says tech firms assume users know of information selling
Views: 14 Go News 24x7 India
Big Data Use Cases | Banking Data Analysis Using Hadoop | Big Data Case Study Part 1
 
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Big Data Use Cases: Banking Data Analysis Using Hadoop | Hadoop Tutorial Part 1 A leading banking and credit card services provider is trying to use Hadoop technologies to handle an analyse large amounts of data. Currently the organization has data in the RDBMS but wants to use the Hadoop ecosystem for storage, archival and analysis of large amounts of data. let’s get into the tutorial, Welcome to online Big Data training video conducted by Acadgild. This is the series of tutorial consists of real world Big Data use cases. In this project, you will be able to learn, • Understand the Project Requirement • What exactly the project is talking about • From where the data is coming • How the data is getting loaded into Hadoop, and • The different analysis that is performed with the Data Go through the entire video to understand the Big Data problems with finance departments and how to track the data. Enroll for big data and Hadoop developer training and certification to become successful Developer, https://acadgild.com/big-data/big-data-development-training-certification?utm_campaign=enrol-bigdata-usecase-part1-iQrao1C7juk_medium=VM&utm_source=youtube For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 20671 ACADGILD
Python Web Scraping Tutorial 15 – How APIs Work
 
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In this tutorial and the following videos we'll offer a general overview of APIs and how they work, look at a few popular APIs available today and look at how you might use an API in your own web scrapers. This video is an intro to How APIs Work and we will talk about common conventions and API methods. ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebook Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 1351 Sukhvinder Singh
Import data from Facebook API in Power BI (Part 2)
 
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On Part 1 we showed you how to use the Power BI connector. In this video we skip the connector altogether and show you how to import data from the Facebook API into Power BI. We also show you how to get all comments from posts into Power BI. Enjoy! Looking for a download file? Go to our Download Center: https://curbal.com/donwload-center SUBSCRIBE to learn more about Power and Excel BI! https://www.youtube.com/channel/UCJ7UhloHSA4wAqPzyi6TOkw?sub_confirmation=1 Our PLAYLISTS: - Join our DAX Fridays! Series: https://goo.gl/FtUWUX - Power BI dashboards for beginners: https://goo.gl/9YzyDP - Power BI Tips & Tricks: https://goo.gl/H6kUbP - Power Bi and Google Analytics: https://goo.gl/ZNsY8l ABOUT CURBAL: Website: http://www.curbal.com Contact us: http://www.curbal.com/contact ▼▼▼▼▼▼▼▼▼▼ If you feel that any of the videos, downloads, blog posts that I have created have been useful to you and you want to help me keep on going, here you can do a small donation to support my work and keep the channel running: https://curbal.com/product/sponsor-me Many thanks in advance! ▲▲▲▲▲▲▲▲▲▲ QUESTIONS? COMMENTS? SUGGESTIONS? You’ll find me here: ► Twitter: @curbalen, @ruthpozuelo ► Google +: https://goo.gl/rvIBDP ► Facebook: https://goo.gl/bME2sB ► Linkedin: https://goo.gl/3VW6Ky
Views: 8959 Curbal
linkedin data scraper
 
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http://linkedinscraper.com/
Views: 82 Yasir Ali
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning
 
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What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning - SOCIAL MEDIA MINING definition - SOCIAL MEDIA MINING 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 Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services). Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens. As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.
Views: 232 The Audiopedia
Lesson-02: Download & Save Facebook data - Facebook Data Analysis with Python
 
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Download and Save and scrap all posts and comments count and likes for a certain page or group using Python and Graph API contact me on: https://www.linkedin.com/in/nourgalaby/ https://www.upwork.com/fl/ngalaby
Views: 15145 Nour Galaby
Facebook CEO Mark Zuckerberg testifies before Congress on data scandal
 
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Facebook CEO Mark Zuckerberg will testify today before a U.S. congressional hearing about the use of Facebook data to target voters in the 2016 election. Zuckerberg is expected to offer a public apology after revelations that Cambridge Analytica, a data-mining firm affiliated with Donald Trump's presidential campaign, gathered personal information about 87 million users to try to influence elections. »»» Subscribe to CBC News to watch more videos: http://bit.ly/1RreYWS Connect with CBC News Online: For breaking news, video, audio and in-depth coverage: http://bit.ly/1Z0m6iX Find CBC News on Facebook: http://bit.ly/1WjG36m Follow CBC News on Twitter: http://bit.ly/1sA5P9H For breaking news on Twitter: http://bit.ly/1WjDyks Follow CBC News on Instagram: http://bit.ly/1Z0iE7O Download the CBC News app for iOS: http://apple.co/25mpsUz Download the CBC News app for Android: http://bit.ly/1XxuozZ »»»»»»»»»»»»»»»»»» For more than 75 years, CBC News has been the source Canadians turn to, to keep them informed about their communities, their country and their world. Through regional and national programming on multiple platforms, including CBC Television, CBC News Network, CBC Radio, CBCNews.ca, mobile and on-demand, CBC News and its internationally recognized team of award-winning journalists deliver the breaking stories, the issues, the analyses and the personalities that matter to Canadians.
Views: 126942 CBC News
Social Media Social Data and Python: 4 - Social Media Mining Techniques
 
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In this video we will briefly discuss the overall process for building a social media mining application, before digging into the details. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 1794 Sukhvinder Singh
Mining of Road Accident Data Using K Means Clustering and Apriori Algorithm
 
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Introduction Road and accidents are uncertain and unsure incidents. In today’s world, traffic is increasing at a huge rate which leads to a large numbers of road accidents. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this project, Apriori algorithm clubbed with Kmeans Clustering is used to analyse the road accidents factors Kmeans Algorithm The algorithm is composed of the following steps: It randomly chooses K points from the data set. Then it assigns each point to the group with closest centroid. It again recalculates the centroids. Assign each point to closest centroid. The process repeats until there is no change in the position of centroids. Apriori Algorithm Apriori involves frequent item-sets, which is a set of items appearing together in the given number of database records meeting the user-specified threshold. Apriori uses a bottom-up search method that creates every single frequent item-set. This means that to produce a frequent item-set of length; it must produce all of its subsets as need to be frequent. Follow Us: Facebook : https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/ Twitter: https://twitter.com/e2matrix_lab/ LinkedIn: https://www.linkedin.com/in/e2matrix-thesis-jalandhar/ Instagram: https://www.instagram.com/e2matrixresearch/
Mining data on Google+ with Python: 1- Setting up Google+ API with Python
 
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We will set up our Google+ APi with creating an API key and putting it to test to do some initial testing to see if it works. ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 341 Sukhvinder Singh
Linkedin Data Scraper
 
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http://linkedinscraper.com/
Views: 106 Yasir Ali
Mining Twitter with Python : 1 - Hashtags, Topics, and Time Series
 
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Twitter is one of the most well-known online social networks that enjoy extreme popularity in the recent years. We will start looking at data mining on Twitter and how to interact with Twitter API. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 3370 Sukhvinder Singh
AI for Marketing & Growth #1 - Predictive Analytics in Marketing
 
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AI for Marketing & Growth #1 - Predictive Analytics in Marketing Download our list of the world's best AI Newsletters 👉https://hubs.ly/H0dL7N60 Welcome to our brand new AI for Marketing & Growth series in which we’ll get you up to speed on Predictive Analytics in Marketing! This series you-must-watch-this-every-two-weeks sort of series or you’re gonna get left behind.. Predictive analytics in marketing is a form of data mining that uses machine learning and statistical modeling to predict the future. Based on historical data. Applications in action are all around us already. For example, If your bank notifies you of suspicious activity on your bank card, it is likely that a statistical model was used to predict your future behavior based on your past transactions. Serious deviations from this pattern are flagged as suspicious. And that’s when you get the notification. So why should marketers care? Marketers can use it to help optimise conversions for their funnels by forecasting the best way to move leads down the different stages, turning them into qualified prospects and eventually converting them into paying customers. Now, if you can predict your customers’ behavior along the funnel, you can also think of messages to best influence that behavior and reach your customer’s highest potential value. This is super-intelligence for marketers! Imagine if you could not only determine whether a lead is a good fit for your product but also which are most promising. This’ll allow you to focus your team’s efforts on leads with the highest ROI. Which will also imply a shift in mindset. Going from quantity metrics, or how many leads you can attract, to quality metrics, or how many good leads you can engage. You can now easily predict your OMTM or KPIs in real-time and finally push vanity metrics aside. For example, based on my location, age, past purchases, and gender, how likely are you to buy eggs I if you just added milk to your basket? A supermarket can use this information to automatically recommend products to you A financial services provider can use thousands of data points created by your online behaviour to decide which credit card to offer you, and when. A fashion retailer can use your data to decide which shoes to recommend as your next purchase, based on the jacket you just bought. Sure, businesses can improve their conversion rates, but the implications are much bigger than that. Predictive analytics allows companies to set pricing strategies based on consumer expectations and competitor benchmarks. Retailers can predict demand, and therefore make sure they have the right level of stock for each of their products. The evidence of this revolution is already around us. Every time we type a search query into Google, Facebook or Amazon we’re feeding data into the machine. The machine thrives on data, growing ever more intelligent. To leverage the potential of artificial intelligence and predictive analytics, there are four elements that organizations need to put into place. 1. The right questions 2. The right data 3. The right technology 4. The right people Ok.. let’s look at some use cases of businesses that are already leveraging predictive analytics. Other topics discussed: Ai analytics case study artificial intelligence big data deep learning demand forecasting forecasting sales machine learning predictive analytics in marketing data mining statistical modelling predict the future historical data AI Marketing machine learning marketing machine learning in marketing artificial intelligence in marketing artificial intelligence AI Machine learning ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/company/growth-tribe Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Snapchat: growthtribe Video URL: https://youtu.be/uk82DHcU7z8
Views: 12471 Growth Tribe
Mining Twitter with Python : 4 - Using the Twitter Streaming API
 
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The Streaming API is one of the favorite ways of getting a massive amount of data without exceeding the rate limits. Lets see how this differs from REST API and the Search API. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 1311 Sukhvinder Singh
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
 
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Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 40190 DeepLearning.TV
▶ 5 Most Used Data Mining Software || Data Mining Tools -- Famous Data Mining Tools
 
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»See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on Data Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner Here We're Going to Learn Which Software is best to use in Data Mining Field R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science. আধুনিক প্রযুক্তির ব্যবহার বৃদ্ধির সাথে অতি দ্রুত ডেটা উৎপন্ন হচ্ছে। টেক জায়ান্ট আইবিএম জানায় ইন্টারনেটে যত ডেটা আছে তার ৯০ ভাগ উৎপন্ন হয়েছে গত তিন বছরে। এ ডেটা উৎপন্নের হার দিনকে দিন বেড়েই চলছে। বিশেষজ্ঞদের ধারনা ২০২০ সাল নাগাদ প্রায় ৪০ জেটাবাইট ডেটা জেনারেট হবে। যা ২০১১ তুলনায় প্রায় ৫০ গুন বেশি। বিশাল পরিমাণ এই ডেটা প্রক্রিয়াজাতের মাধ্যমে বিজ্ঞান, গবেষণা, চিকিৎসা, শিক্ষা ও ব্যবসায় ব্যপক ভুমিকা রাখা যেতে পারে। তাই বলা হচ্ছে “ বিগ ডেটা ইজ বিগ ইমপ্যাক্ট।” Data Mining,big data,data analysis,data mining tutorial,book , Bangla tutorials,data mining software,Data Mining,What is data mining, bookbd, data analysis,data mining tutorial,data science,big data,business tutorial,data mining Bangla tutorial,how to,how to mine data,knowledge discovery,Artificial Intelligence,Deep learning,machine learning,Python tutorials,
Views: 4559 BookBd
Business Applications of Predicitive Modeling at Scale (Part 1)
 
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Authors: Yan Liu, LinkedIn Corporation Paul Ogilvie, LinkedIn Corporation Songtao Guo, LinkedIn Corporation Qiang Zhu, LinkedIn Corporation Abstract: Predictive modeling is the art of building statistical models that forecast probabilities and trends of future events. It has broad applications in industry across different domains. Some popular examples include user intention predictions, lead scoring, churn analysis, etc. In this tutorial, we will focus on the best practice of predictive modeling in the big data era and its applications in industry, with motivating examples across a range of business tasks and relevance products. We will start with an overview of how predictive modeling helps power and drive various key business use cases. We will introduce the essential concepts and state of the art in building end-to-end predictive modeling solutions, and discuss the challenges, key technologies, and lessons learned from our practice, including case studies of LinkedIn feed relevance and a platform for email response prediction. Moreover, we will discuss some practical solutions of building predictive modeling platform to scale the modeling efforts for data scientists and analysts, along with an overview of popular tools and platforms used across the industry. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 172 KDD2016 video
Getting Started with the Graph API
 
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An introduction to Facebook's Graph API which is the primary way to programmatically integrate with Facebook - publishing Open Graph stories, reading data about the current user - their details, likes and interests and friends. Read the full Getting Started guide at https://developers.facebook.com/docs/getting-started/graphapi/ We cover: - the difference between Objects (also known as Edges) and Connections - Using the Graph API Explorer we see how to build up deep graph queries which dive several layers deep into the Graph - How we need to request additional permissions to access more data about the user, or to have permission to publish on behalf of the user.
Views: 355117 Facebook Developers
4/7 Live Webinar : Setting Yourself Up for Success in Data Science
 
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DEAS & Data Application Lab co-host this live webinar: Guest Speakers: 1.Eric Weber Senior Data Scientist at LinkedIn Eric focus includes sales, marketing, product, data mining, and machine learning, among others. He also focuses specifically on sales intelligence for our LinkedIn Learning product. As a senior data scientist, he also is expected to take leadership roles in specific projects. 2. Sarah Nooravi Senior Data Scientist at Operam Sarah is currently working as the Senior Data Scientist at Operam. She is one of the most popular and helpful personalities on LinkedIn. Sarah regularly posts content aimed at helping aspiring data scientists break into the field and is always available for helpful and insightful advice. Topics: How to become a Data Scientist? In this discussion/webinar, Sarah and Eric will be focusing on their tips for starting your career in data science. About Data Application Lab: https://www.dataapplab.com/about-us/ About IDEAS; https://www.ideassn.org/
Views: 30 IDEAS
Web API Kit: OAuth - Connecting to LinkedIn - Part 1
 
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Web API Kit: OAuth - Connecting to LinkedIn - Part 1
Views: 1572 haptixgames
Jatin Madhra speaks about Social Media Data Mining on Newsx | DIGIASAP
 
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Here Mr. Jatin Madhra,CEO of DIGIASAP talking about the serious concern i.e. social media data mining over social platform on NewsX Channel. He told us that how there are such numbers of company running in the market to mining the data and sales to potenial user. Data mining is legal but when somebody uses this data unethically for your own sake. Jatin Madhra also said that there are so many tools for data mining tools, analysis.It is quite common nowadays. Video Credit- NewsX Digiasap is best digital marketing company in Delhi/NCR which always gives you the best ROI on your investments. We provide services like SEO, SMO, SMM, PPC, Email Marketing, Content Writing.We are also providing the services for startup and make it successful through digital markeitng. Contact Us Website- https://www.digiasap.com/ Facebook- https://www.facebook.com/digiasap/ Twitter- https://twitter.com/digiasap Linkedin- https://www.linkedin.com/company/digiasap Mobile- 096439 11991
Introduction to Data Mining: Feature Subset Selection
 
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In part six of data preprocessing, we discuss another way of dimensionality reduction, feature subset selection. -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 3500+ employees from over 700 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8Lrw0 See what our past attendees are saying here: https://hubs.ly/H0f8M7M0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 4429 Data Science Dojo
Python for Data Science | Python Data Science Tutorial | Data Science Certification | Edureka
 
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( Python Data Science Training : https://www.edureka.co/python ) This Edureka video on "Python For Data Science" explains the fundamental concepts of data science using python. It will also help you to analyze, manipulate and implement machine learning using various python libraries such as NumPy, Pandas and Scikit-learn. This video helps you to learn the below topics: 1. Need of Data Science 2. What is Data Science? 3. How Python is used for Data Science? 4. Data Manipulation in Python 5. Implement Machine Learning using Python 6. Demo Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Training Playlist: https://goo.gl/Na1p9G #Python #PythonForDataScience #PythonTutorial #PythonForBeginners #PythonOnlineTraining 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 Data Science 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 Data Science 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] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 22468 edureka!
Intro to Azure ML: Data Exploration
 
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Now that Azure Machine Learning Studio is setup, let’s begin an end-to-end data science project in Azure Machine Learning. We’ll choose the flight delay data, and use it to predict whether not a flight will be late on arrival based upon the flight’s circumstances. In this video we will begin our preliminary exploration into the dataset using Azure Machine Learning’s dataset module. In Part 4 we will cover: - introduction to projects - Exploring a data set using Azure ML - Building a data mining strategy -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3500+ employees from over 700 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8p250 See what our past attendees are saying here: https://hubs.ly/H0f8p2l0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 5079 Data Science Dojo
Big Data Applications | Big Data Analytics Use-Cases | Big Data Tutorial for Beginners | Edureka
 
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( ** Hadoop Training: https://www.edureka.co/hadoop ** ) This Edureka tutorial video on "Big Data Applications" will explain various how Big Data analytics can be used in various domains. Following are the topics included in this video: 1. Why do we need Big data Analytics. 2. Big Data Applications in Health Care. 3. Big Data in Real World Clinical Analytics. 4. Big Data Analytics in Education Sector. 5. IBM Case Study in Education Section. 6. Big data applications and use cases in E-Commerce. 7. How Government uses Big Data analytics? 8. How Big data is helpful in E-Government Portal? 9. Big Data in IOT. 10. Smart city concept. 11. Big Data analytics in Media and Entertainment 12. Netflix example in Big data 13. Future Scope of Big data. Check our complete Hadoop playlist here: https://goo.gl/hzUO0m Subscribe to our channel to get video updates. Hit the subscribe button above. ----------------------------------------------------------------------------------------- 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 ----------------------------------------------------------------------------------------- How does it work? 1. This is a 5 Week Instructor-led Online Course, 40 hours of assignment and 30 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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! -------------------------------------------------------------------- About The Course Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you: 1. Master the concepts of HDFS and MapReduce framework 2. Understand Hadoop 2.x Architecture 3. Setup Hadoop Cluster and write Complex MapReduce programs 4. Learn data loading techniques using Sqoop and Flume 5. Perform data analytics using Pig, Hive and YARN 6. Implement HBase and MapReduce integration 7. Implement Advanced Usage and Indexing 8. Schedule jobs using Oozie 9. Implement best practices for Hadoop development 10. Work on a real life Project on Big Data Analytics 11. Understand Spark and its Ecosystem 12. Learn how to work in RDD in Spark ---------------------------------------------------------------------- Who should go for this course? If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career: 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building a successful career in Big Data --------------------------------------------------------------------- Why Learn Hadoop? Big Data! A Worldwide Problem? According to Wikipedia, "Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. --------------------------------------------------------------------- Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! For more information, please write back to us at [email protected] Call us at US: 1844 230 6362 (toll free) or India: +91-90660 20867
Views: 3572 edureka!
Crowdsourcing: Achieving Data Quality with Imperfect Humans
 
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http://data.linkedin.com Crowdsourcing is a great tool to collect data and support machine learning -- it is the ultimate form of outsourcing. But crowdsourcing introduces budget and quality challenges that must be addressed to realize its benefits. In this talk, I will discuss the use of crowdsourcing for building robust machine learning models quickly and under budget constraints. I'll operate under the realistic assumption that we are processing imperfect labels that reflect random and systematic error on the part of human workers. I will also describe our "beat the machine" system engages humans to improve a machine learning system by discovering cases where the machine fails and fails while confident on being correct. I'll use classification problems that arise in online advertising. Finally, I'll discuss our latest results showing that mice and Mechanical Turk workers are not that different after all. About the Speaker: Panos Ipeirotis is an Associate Professor and George A. Kellner Faculty Fellow at the Department of Information, Operations, and Management Sciences at Leonard N. Stern School of Business of New York University. His recent research interests focus on crowdsourcing and on mining user-generated content on the Internet. He received his Ph.D. degree in Computer Science from Columbia University in 2004, with distinction. He has received three "Best Paper" awards (IEEE ICDE 2005, ACM SIGMOD 2006, WWW 2011), two "Best Paper Runner Up" awards (JCDL 2002, ACM KDD 2008), and is also a recipient of a CAREER award from the National Science Foundation.
Views: 3909 LinkedInTechTalks
Big Data Use Cases | E-Commerce Data Analysis | Big Data Case Study Part 7
 
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Big Data Use Cases | Ecommerce Data Analysis | Big Data Case Study Part 7 Welcome back to the online tutorial, Hadoop Case Studies. i.e. ecommerce data analysis. This is in continuation to the previous training session. If you have missed the previous Session, click the following link: https://www.youtube.com/watch?v=7nYjJoc9FQ0 Click the following link for queries, source code etc. https://acadgild.com/blog/big-data-training-with-real-projects/?utm_campaign=enrol-bigdata-usecase-part7-jyOg2WkLDPA_medium=VM&utm_source=youtube In the last session of Hadoop case study, you understood, various rule checking and validation processes. In this big data case study, let’s understand, how to perform analysis on the validated Data. Data Analysis: Following are desired to be known to the management team: Purchase Pattern Detection: • What is the most purchased category for every user? Identify the user with a maximum amount of valid purchase • Which products are generating the maximum profit? • Which resellers are generating the maximum profit? • Which is the most sought after category corresponding to the very occupation Go through the entire video to learn more. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 1428 ACADGILD
Mining data on Google+ with Python: 2- How to create a simple search app with Flask and Google+ API
 
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Let's create a search app with the framework Flask and use Google+ API. Python and web development goes hand in hand and Flask is a great framework to create prototypes fast. Let's have some fun, guys! ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 137 Sukhvinder Singh
Twitter Streaming API in Python. Data mining Demonstration
 
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I have made some web scrappers before. You can check it out here: - https://www.youtube.com/watch?v=-vYiAfLDEVw - https://www.youtube.com/watch?v=oQkIp1Bk_vg - https://www.youtube.com/watch?v=pqAdxZWFkTM and more. It was so easy and fun work by using Beautiful Soup and Selenium. This time I going to build a powerful Twitter scanner. Twitter provide three main APIs: the REST API, Streaming API, and the Ads API. I will use Twitter Streaming API for gathering all related by a keyword tweets in real time. Yes - in real time! First of all before all the work you must to create an application in your Twitter account. Then you will generate your Consumer ID, Secret keys, Token key and Token secret keys. You have to obtain credentials to be able to collect data from Twitter. Before coding I recommend install requests-oauthlib 0.8.0 module from here: https://pypi.python.org/pypi/requests-oauthlib to your default Python directory. Good is that my code will write the real time tweets to output csv file after each tweet is come to the pocket. Next step: implement sentimental analysis for tweets. Still searching for solutions. Vytautas Bielinskas LinkedIn: https://www.linkedin.com/in/bielinskas
Views: 337 Vytautas Bielinskas
Scraping data from Facebook - PART 2
 
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Use the Visual Web Ripper web scraping software to extract data from Facebook. This is an advanced demonstration video and is intended for expert web scrapers.
Views: 3446 sequentum
AT Internet’s Analytics Suite: Data Query application
 
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AT Internet’s Data Query application makes data mining as easy as dragging and dropping. Cross-calculate, compare, correlate, filter and sort analytics data in just seconds with Data Query. More about Data Query: http://www.atinternet.com/en/product/data-query/ Want to know more about AT Internet? Visit http://www.atinternet.com/en Be sure to check out our blog: http://blog.atinternet.com/en Follow us to stay updated on the latest trends in digital analytics: -Facebook : https://www.facebook.com/atinternet.analytics/ -Twitter : https://twitter.com/AT_Internet -LinkedIn : https://www.linkedin.com/company/at-internet -SlideShare : http://fr.slideshare.net/AT-Internet -Xing : https://www.xing.com/companies/atinternetgmbh -Google + : https://plus.google.com/+ATinternet
Views: 1284 AT Internet
🇫🇷 Data mining for Fil Rouge project in INSA de Lyon
 
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Hackathon organisé par nous, membre du projet Opinionytics Fil Rouge INSA 2018 Opinionytics: Outil permettant l'analyse de n'importe quel texte entré https://opinionytics.github.io/opinionytics/ Technologies utilisées: JS / AJAX Django Python 3 APIs : Watson, Google Trends et Aylien APIs Liens du projet: Github: https://github.com/Opinionytics/opinionytics Gitlab: https://gitlab.com/Opinionytics/ Facebook: https://www.facebook.com/Opinionytics/ Twitter: https://twitter.com/opinionytics LinkedIn: https://www.linkedin.com/company/opinionytics Medium: https://medium.com/@Opinionytics Reddit: https://www.reddit.com/user/opinionytics ------------------- { * LIENS* } ------------------- Portefolio : http://amine.boulouma.com Facebook : https://www.facebook.com/aminemboulouma LinkedIn : https://www.linkedin.com/in/aminemboulouma Twitter : https://twitter.com/aminemboulouma Instagram : https://www.instagram.com/aminemboulouma Snapchat : https://www.snapchat.com/add/flambok Blog : https://hightechbrains.com
Views: 379 Amine M. Boulouma
"Data Science" What Is Text Mining ? | Applications Of Text Mining And Clustering | Training -ExcelR
 
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What Is Text Mining ? | Applications Of Text Mining And Clustering #DataScience #TextMining #Clustering #TextMining In Datascience #Applications of Textmining #training #2018 Text Mining, Hmm before we dive into the water we need to make sure that we are ready. So in the same way before going into deep we should know what is text-mining and it`s applications? Daily a new data is being generated and mostly majority of data is unstructured. To convert/transform the unstructured data (unstructured text data) into structured, then the unstructured text data is to set for analysis using text-mining to get new generated information. Like we get daily 1. Call transcripts, 2. Emails that we sent to customer service 3. Social Media outreach (Facebook, twitter, Instagram and many more) 4. Speech transcripts 5. Filed agents, sales people 6. Interviews and survey`s The process of getting high quality of information deriving from the text data is text –mining. To examine the large amount of text data/Written data sources to generate new information. This quality information I typically derived through devising of patters and trends such as statistical pattern learning. Clustering in data mining is gathering set of abstract data and aggregating them based on their similarities. Here are some of the applications of text mining and clustering are: 1. Text categorization into particular domains 2. Organizing a set of documents automatically by text Clustering. 3. Identifying and extracting subject information in documents. In other words-sentiment analysis. 4. Extracting entity/concepts which can identify people, places, organisations and other entities. 5. Learning relations between named entities. In this video you will learn about 1. Text Mining and use of Clustering 2. Applications of Text Mining 3. What is Word Cloud? SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/excelr-solutions/ Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions