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Search results “Linkedin data mining uses”
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: 15160 Sukhvinder Singh
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: 46369 CodingEntrepreneurs
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: 36032 Octoparse
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] __ /// 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: http://bit.ly/2vqZCyK Get the proposal template you can use to sell 5 and 6 figure deals: http://bit.ly/2NqiPJw Free Sales Courses: https://experiment27.teachable.com/ __ /// WORK WITH ALEX More enterprise clients for your agency: http://experiment27.com Turn your book into a documentary: https://loreliapictures.com/ Work one-on-one with Alex: http://experiment27.com/consulting __ /// SHIRTS & HOODIES http://wohello.com __ /// MORE FROM ALEX Subscribe for more content like this: https://www.youtube.com/user/alxberman?sub_confirmation=1 The Alex Berman Podcast: iTunes: https://itunes.apple.com/us/podcast/the-alex-berman-podcast/id1200614219?mt=2 Spotify: https://open.spotify.com/show/6fnAZkjzRhtPYvsZkcMmjK?si=7gwE0NuPSqSMFpGM9MskGg __ /// BUSINESS INQUIRIES: For sponsorships you can reach us at: [email protected]
Views: 66149 Alex Berman
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: 54368 edureka!
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: 84 Michael Lomov
How Big Data Is Used In Amazon Recommendation Systems | Big Data Application & Example | Simplilearn
 
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This Big Data Video will help you understand how Amazon is using Big Data is ued in their recommendation syatems. You will understand the importance of Big Data using case study. Recommendation systems have impacted or even redefined our lives in many ways. One example of this impact is how our online shopping experience is being redefined. As we browse through products, the Recommendation system offer recommendations of products we might be interested in. Regardless of the perspectives, business or consumer, Recommendation systems have been immensely beneficial. And big data is the driving force behind Recommendation systems. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 26548 Simplilearn
Data Science in Practice: Importing and Visualizing Facebook Data Using Graphs!
 
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This meetup was held in Mountain View on 7th March 2018. Our speakers, Ray Bernard and Jen Webb walked through how their cloud-based marketing company, Suprfanz, uses Neo4j to drive attendance over social media platforms, email, and SMS. This talk will show you step-by-step how to use the Facebook Graph API to import data into Neo4j. The technologies discussed are Neo4j, Flask, Python, D3, and a cloud solutions stack on Digital Ocean. We encourage you to bring your laptops and follow along. All the materials will also be made available on Github. Speaker's Bios: Ray Bernard is the founder and Chief Architect at Suprfanz.com (http://suprfanz.com/). He is a former adjunct professor at Columbia University. Ray has worked for technology giants like Compaq, Dell, and EMC. As leader of the Cosmic Blues Band, he performs regularly at BB Kings in NYC! Jennifer Webb has over 12 years experience in web development, design, and print graphics. A graduate of the Design Essentials program at Emily Carr and BCIT in Vancouver, she has worked with many different kinds of businesses and people, and throughout them, all has brought a passion for solving problems and creating top quality user experiences.
Views: 338 H2O.ai
Create a LInkedin app
 
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In this video we show you how to create a linkedin app and configure the Social Media Autoposter settings to enable auto posting your wordpress content to your Linkedin account.
Views: 1280 martialartsite
Web Scraping with Excel and Chrome Extensions | Data Scraping and Mining From Websites
 
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Web Scraping with Excel and Chrome Extensions | Data Scraping and Mining From Websites After watch this video If you #WebScraping Services, Lead Generation Services, Data Entry Services with affordable rates, Please feel free to contact me. Web Scraper | Web Scraping using web scraper chrome extension | web scraper tutorial | Data Scraper https://www.youtube.com/watch?v=ZdOrH50WqEo How to Extract Emails from LinkedIn | How to Scrape Emails from Web | Extract millions email in min https://www.youtube.com/watch?v=O9kg76alKYo
Bay Area Girl Geek Dinner at LinkedIn, Oct. 2017
 
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Video from the Bay Area Girl Geek Dinner on Monday, October 2nd, 2017 in downtown San Francisco From the site: Come listen to LinkedIn girl geeks giving tech talks about a recommendation system for online learning, the importance of creating, insights from an economic graph, and scaling big data. There will be refreshments, drinks and fun schwag! See more about the event: http://girlgeek.io/linked-in-girl-geek-dinner-2017/ Speaker bios: Shivani Rao (Senior Applied Researcher – Machine Learning, LinkedIn) Shivani Rao is a Senior Applied Researcher working in the Learning Relevance group at LinkedIn. Shivani has accrued research experience in Industry and academia in areas of Machine Learning, Data Mining and Computer Vision. Outside of R&D work, Shivani loves engaging with the larger technical community, via writing and speaking and serving on the organizing committee of workshops and conferences. She has published and helped organize various academic and industry conferences including KDD and GHC. Shivani is also passionate about mentoring and supporting women in tech via outreach events with organizations like Hackbright Academy, Technovation. Follow her on Twitter at @shivanigrao. Omayeli Arenyeka (Software Engineer, LinkedIn) Omayeli Arenyeka is a creative technologist working at LinkedIn. She graduated from NYU with a degree she crafted, titled Art & Code or Creative Computation if you want to be fancy. She’s demoed her projects at the Razorfish Global Tech Summit, [email protected] Demodays, NY Tech Meetup and NYC Media Lab Summit. She’s an alum of CODE2040 and [email protected] – NYC’s largest student tech organization. She thinks a lot about the technology, art and social good and can tell you the plot of any Law and Order SVU episode. Follow her on Twitter at @yellzheard. Jacqueline Barrett (Lead Economic Graph Researcher, LinkedIn) Jacqueline Barrett is the Lead Researcher of the Economic Graph team at LinkedIn. In her role, she uses LinkedIn’s rich data set to uncover trends in local economies as well as understand complex labor market phenomena. Prior to working at LinkedIn, Jacqueline served as an Economist at Compass Lexecon where she focused on antitrust and competition. She has an MBA from the University of Chicago Booth School of Business and a BA in Economics from Northwestern University. Suja Viswesan (Senior Engineering Manager, LinkedIn) Suja Wiswesan leads engineering teams that are responsible for the Big Data Platform at LinkedIn. The team is responsible for the entire ecosystem including Data Management, Hadoop as a Service, Core Hadoop, Spark, etc. This platform is leveraged by LinkedIn to make key business decisions. This platform also powers products like ‘People You May Know’, Feed relevance to name a few. Some of the open source contributions from the team include, Gobblin, Azkaban, Dr.Elephant. Prior to joining LinkedIn, Suja led BigInsights Hadoop team, Database teams at IBM. Suja holds an MS in computer Engineering. Suja is very passionate about Women in tech and has been part of various talks/panels at Dataworks summit, Girls Who Code, LeanIn, Girls Scout and Universities. She is also mentors at WEST, TechWomen.
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: 14694 Growth Tribe
How to Automate Prospecting and Target List Building on LinkedIn With DMS Capture
 
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For more information - visit www.ringlead.com/contact-us RingLead’s end-to-end data management solution DMS features a key discovery and prospecting tool, DMS Capture, that automates target list building, enabling any sales rep, recruiter or marketer to instantly generate targeted lead lists complete with enriched fields, so your organization can manage their data... faster, and smarter. Sales reps understand that calling and nurturing leads these targeted lead list is indispensable to their account-based marketing strategy - it’s how they generate drive their organizations revenue. However, the average rep only spends 30 percent of their day selling - the rest of their time is often spent researching data on Google, LinkedIn and other social sites to build lead list and to find key information on their target leads and accounts. The all-too-common trade-off between selling and prospecting that damages most businesses does not need to exist at your organization. RingLead DMS Capture is used by thousands of sales reps, recruiters, and marketers to compliment existing tools like LinkedIn, Google Searches, and Company Webpages to automate prospecting. DMS Capture recognizes data patterns from countless origins, enabling reps to click Capture and instantly generate lead lists of 1,000+, all with enriched data fields like direct dials, validated email addresses, job titles, and more. Your rep then imports that list to Salesforce where DMS prevents any duplicates from entering your Salesforce, enables you to add leads to existing accounts, and standardizes your new data to ensure the quality of your database remains intact. Creating and maintaining a clean, dupe-free and enriched database has never been simpler.
Views: 367 TalkDataToMe
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 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/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: 424426 Data Science Dojo
▶ 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: 5405 BookBd
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: 523 The Audiopedia
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: 7267 Sukhvinder Singh
BIG Data and Hadoop Applications in Healthcare
 
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Register here for FREE ACCESS to our BIG Data & Hadoop Training Platform: http://promo.skillspeed.com/big-data-hadoop-developer-certification-course/ This is a short video presentation on Applications of Big Data & Hadoop in Healthcare. BIG Data & Hadoop in Healthcare is a key differentiator, especially in terms of creating personalized services. Healthcare industries use Big Data analytics for early disease detection, making personalized medicines, clinical trials and increasing profitability via detection of high cost patients. The agenda of the session is as follows ✓ Introduction to BIG Data & Hadoop ✓ BIG Data & Hadoop Use Cases in Healthcare ✓ Examples – Apple and IBM, Pfizer & Caroline healthcare System ✓ Deriving Insights from BIG Data ---------- What is BIG Data & Hadoop? Big Data refers to the vast amounts of unstructured data generated in today’s internet driven world which cannot be tapped, manipulated and utilized via traditional data harness tools. Apache Hadoop is an open-source JAVA based framework, which is used to harness & process BIG Data sets. It facilitates distributed parallel processing via cluster nodes to ensure a secure, scalabe & accurate data service solution. The framework consists of Hadoop Distributed File System (HDFS), Hive, Sqoop, Flume, Hbase, Pig, Yarn & ZooKeeper. This video will decipher Big Data in Healthcare & Hadoop in Healthcare. ---------- Examples of BIG Data & Hadoop in Healthcare IBM and Apple Apple & IBM are collaborating on a Big Data health platform that will allow iPhone and Apple Watch users to share data to IBM’s Watson Health, a cloud analytics service. The objective is to access data regarding real-time activity which can serve as a data-bank for the discovery of new medical and fitness insights. Users will also have the option to directly share this data with their doctors. Pfizer Pfizer developed Xalkori, a drug approved by the FDA in 2011 specifically for lung cancer patients via BIG Data analytics. They combed through data regarding genomic profiles, clinical trials and EMR reports to develop the drug. Pfizer is also exploring the possibility of developer drugs for smaller patient groups based on their health & lifestyle activities. Caroline Healthcare Systems They run more than 900 care centers which are visited by thousands of patients everyday. After mining through the patient database, they developed an algorithm to immediately identify high-risk patients so that doctors can provide them with priority healthcare. ---------- Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance. Email: [email protected] Website: https://www.skillspeed.com Number: +91-90660-20904 Facebook: https://www.facebook.com/SkillspeedOnline Linkedin: https://www.linkedin.com/company/skillspeed
Views: 2935 Skillspeed
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: 25372 Nour Galaby
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: 2065 Sukhvinder Singh
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: 1146 ZoonMan
How To Extract Tweets From Twitter | Web And Social Media Extraction | Data Science - ExcelR
 
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ExcelR : Learn Web and Social media extraction using R, Risk sensing - sentiment analysis,Twitter application management for extracting tweets. Things you will learn in this video 1.Web extraction and social media extraction. 2.How to extract tweets from twitter? 3.What is risk sensing? To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here " https://goo.gl/JTkWXo" SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For Introduction to data science demo click here https://goo.gl/2vkFjq #ExcelRSolutions #DataScience #BusinessAnalytics #DatasciencewithR#DataScienceWithPython #DataScienceTutorialForBeginners #DataScienceTraining #DataScienceCertification #DataSciencetutorial ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 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/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
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: 369 pooja bhojwani
Linkedin Data Scraper
 
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http://linkedinscraper.com/
Views: 111 Yasir Ali
linkedin data scraper
 
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http://linkedinscraper.com/
Views: 87 Yasir Ali
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: 434 Vytautas Bielinskas
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: 15 Go News 24x7 India
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: 41961 DeepLearning.TV
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: 129350 CBC News
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/
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: 27096 edureka!
How NLP text mining works: find knowledge hidden in unstructured data
 
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Connect with us: http://www.linguamatics.com/contact What use is big data if you can't find what you're looking for? Follow: @Linguamatics https://twitter.com/Linguamatics https://www.linkedin.com/company/linguamatics https://www.facebook.com/Linguamatics https://plus.google.com/+Linguamatics https://www.youtube.com/user/Linguamatics/videos In knowledge driven industries such as the life sciences and healthcare, finding the right information quickly from huge volumes of text is crucial in supporting the best business decisions. However, around 80% of available information exists as unstructured text, and conventional keyword searches only retrieve documents, which still have to be read. This is very time consuming, unreliable, and, when important decisions rest on it, costly. Linguamatics’ text mining solution, I2E, uses Natural Language Processing to identify and extract relevant knowledge at least 10 times faster than conventional search, often uncovering insights that would otherwise remain unknown. I2E analyses the meaning of the text using powerful linguistic algorithms, enabling you to ask open questions, find the relevant facts and identify valuable connections. Going beyond simple keywords, I2E can recognise concepts and the different ways the same thing can be expressed, increasing the recall of relevant information. I2E then presents high quality results as structured, actionable knowledge, enabling fast review and analysis, and providing dramatically improved speed to insight. Our market leading software is supported by highly qualified domain experts who work with our customers to ensure successful project outcomes. Text mining for beginners: https://www.youtube.com/watch?v=40QIW9Sr6Io
Views: 15186 Linguamatics
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: 364403 Facebook Developers
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
🇫🇷 Data mining for Fil Rouge project at 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: 406 Amine M. Boulouma
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: 1428 Sukhvinder Singh
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 882323 David Langer
Why Machine Learning is The Future? | Sundar Pichai Talks About Machine Learning
 
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Why Machine Learning is The Future? | Sundar Pichai Talks About Machine Learning https://acadgild.com/big-data/deep-learning-course-training-certification?aff_id=6003&source=youtube&account=5cFUZ03Sbhc&campaign=youtube_channel&utm_source=youtube&utm_medium=sundarpichai&utm_campaign=youtube_channel It is 2017 and one technology which is expected to bring in a sea of innovation is Machine Learning. Be it the day to day life or high-end sophisticated innovation, the world is slowly but surely moving forward to become more Machine Learning reliant. Products of the Internet giant like Google or Facebook are heavily embedded around Machine Learning. "We are making a big bet on machine learning and artificial intelligence. Advancement in machine learning will make a big difference in many many fields.", the Google CEO, Sundar Pichai said at IIT Kharagpur pointing out how effectively computers recognize image, voice or speech. 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: 705324 ACADGILD
Machine Learning - Supervised VS Unsupervised Learning
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
Views: 67632 Cognitive Class
How Artificial Neural Network (ANN) Algorithm Work | Data Mining | Introduction to Neural Network
 
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#ArtificialNeuralNetwork | Beginners guide to how artificial neural network model works. Learn how neural network approaches the problem, why and how the process works in ANN, various ways errors can be used in creating machine learning models and ways to optimise the learning process. - Watch our new free Python for Data Science Beginners tutorial: https://greatlearningforlife.com/python - Visit https://greatlearningforlife.com our learning portal for 100s of hours of similar free high-quality tutorial videos on Python, R, Machine Learning, AI and other similar topics Know More about Great Lakes Analytics Programs: PG Program in Business Analytics (PGP-BABI): http://bit.ly/2f4ptdi PG Program in Big Data Analytics (PGP-BDA): http://bit.ly/2eT1Hgo Business Analytics Certificate Program: http://bit.ly/2wX42PD #ANN #MachineLearning #DataMining #NeuralNetwork About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/
Views: 66355 Great Learning
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: 4493 edureka!
Intelligence Software LinkedIn Scraper
 
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Shane McCusker demonstrates how Intelligence can import profile information straight from LinkedIn.
Views: 3388 Shane McCusker
Extract Linkedin profiles on People You May Know - Handy Linkedin Automation
 
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Just 2 clicks to scrape Linkedin profiles on the page of People You May Know
Views: 93 Handy Tools
Machine Learning Interview Questions And Answers | Data Science Interview Questions | Simplilearn
 
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This Machine Learning Interview Questions And Answers video will help you prepare for Data Science and Machine learning interviews. This video is ideal for both beginners as well as professionals who are appearing for Machine Learning or Data Science interviews. Learn what are the most important Machine Learning interview questions and answers and know what will set you apart in the interview process. Some of the important Machine Learning Interview Questions are listed below: 1. What are the different types of Machine Learning? 2. What is overfitting? And how can you avoid it? 3. What is false positive and false negative and how are they significant? 4. What are the three stages to build a model in Machine Learning? 5. What is Deep Learning? 6. What are the differences between Machine Learning and Deep Learning? 7. What are the applications of supervised Machine Learning in modern businesses? 8. What is semi-supervised Machine Learning? 9. What are the unsupervised Machine Learning techniques? 10. What is the difference between supervised and unsupervised Machine Learning? 11. What is the difference between inductive Machine Learning and deductive Machine Learning? 12. What is 'naive' in the Naive Bayes classifier? 13. What are Support Vector Machines? 14. How is Amazon able to recommend other things to buy? How does it work? 15. When will you use classification over regression? 16. How will you design an email spam filter? 17. What is Random Forest? 18. What is bias and variance in a Machine Learning model? 19. What’s the trade-off between bias and variance? 20. What is pruning in decision trees and how is it done? Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Machine-Learning-interview-Questions-and-answers-hB1CTizqGFk&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-interview-Questions-and-answers-hB1CTizqGFk&utm_medium=Tutorials&utm_source=youtube You can also go through the Slides here: https://goo.gl/rmzjaQ #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 29241 Simplilearn
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning 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. 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. "
Views: 58410 edureka!
How to use WEKA software for data mining tasks
 
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In this video, I'll guide you how to use WEKA software for preprocessing, classifying, clustering, association. WEKA is a collection of machine learning algorithms for performing data mining tasks. #RanjiRaj #WEKA #DataMining Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj Get WEKA from here : http://www.cs.waikato.ac.nz/ml/weka/
Views: 15668 Ranji Raj
Selenium - LinkedIn Test
 
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Uses Selenium and Python to test functionality of LinkedIn.com
Views: 34 Shoaib Faizi

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