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Mozenda - Data Mining - Real Estate Leads - PT1
 
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http://www.twitter.com/jbmcclelland Justin McClelland (http://www.justinmcclelland.com), provides various how-to demonstrations and example applications of the Mozenda software (http://www.getmozenda.com ). The Mozenda, Software as a Service (SaaS), platform is ideal for performing comprehensive web data gathering (a.k.a web data extraction, screen scraping, web crawling, web harvesting, etc.)
Views: 2965 Justin McClelland
Real Estate Data Mining Video
 
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Real Estate Data Mining Video
Mozenda - Data Mining - Real Estate Leads - PT2
 
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http://www.twitter.com/jbmcclelland Justin McClelland (http://www.justinmcclelland.com), provides various how-to demonstrations and example applications of the Mozenda software (http://www.getmozenda.com ). The Mozenda, Software as a Service (SaaS), platform is ideal for performing comprehensive web data gathering (a.k.a web data extraction, screen scraping, web crawling, web harvesting, etc.)
Views: 827 Justin McClelland
GOTO 2013 • Elasticsearch - Beyond Full-text Search • Alex Reelsen
 
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This presentation was recorded at GOTO Aarhus 2013 http://gotocon.com Alex Reelsen - Software Engineer at Elasticsearch ABSTRACT Elasticsearch is the leading real-time, distributed, open source search and analytics engine. In addition to providing a highly scalable full-text search engine, users choose Elasticsearch to build sophisticated real-time analytics applications. Recently recommended by Thoughtworks as the #1 technology platform to adopt, Elasticsearch users include SoundCloud, Github, Foursquare and StackOverflow. We'll show some of the real world examples of the cool stuff they are doing using Elasticsearch. https://twitter.com/gotocon https://www.facebook.com/GOTOConference http://gotocon.com
Views: 18222 GOTO Conferences
Lingo4G large-scale text clustering engine, workflow overview
 
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Carrot Search Lingo4G is a next-generation text clustering engine capable of processing tens of gigabytes of text and millions of documents. This video is a more in-depth overview of Lingo4G workflow. We index and analyze 240k questions and answers posted to the computer enthusiasts Q&A site, superuser.com. Lingo4G documentation: http://get.carrotsearch.com/lingo4g/l... Lingo4G trial and more information: https://carrotsearch.com/lingo4g
Views: 497 Carrot Search
Data Mining Tutorial for Beginners FREE Training 02
 
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By: MyTeacher Published on Aug 2, 2014 2 intro import IO next tutorial here: http://youtu.be/Yi9GkFdL11A please comment below if you have any questions. Tq Category Education License Standard YouTube License
SEO - Keyword discovery tool - Mozenda Data Mining - analyticip.com
 
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Views: 77 Data Analytics
Data Mining
 
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Learn how to properly data mine using Google search engine.
MBA 738 Data Mining for Business Intelligence | George Mason University MBA
 
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Dr. Pallab Sanyal gives an overview MBA 738 Data Mining for Business Intelligence, an elective course in George Mason University's MBA program. George Mason MBA students have 15 credits of electives to choose courses that fit their interests and career goals. The Mason MBA currently offers the following areas of emphasis: •Accounting •Entrepreneurship •Financial Management •Information Systems Management •International Business •Leadership •Marketing •Project Management •Real Estate Learn more about areas of specialization at http://business.gmu.edu/mba-programs/curriculum/areas-emphasis/. Review all available courses and descriptions at http://business.gmu.edu/mba-programs/curriculum/courses-syllabi/.
Views: 1716 GeorgeMasonBusiness
Building dataset - p.4 Data Analysis with Python and Pandas Tutorial
 
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In this part of Data Analysis with Python and Pandas tutorial series, we're going to expand things a bit. Let's consider that we're multi-billionaires, or multi-millionaires, but it's more fun to be billionaires, and we're trying to diversify our portfolio as much as possible. We want to have all types of asset classes, so we've got stocks, bonds, maybe a money market account, and now we're looking to get into real estate to be solid. You've all seen the commercials right? You buy a CD for $60, attend some $500 seminar, and you're set to start making your 6 figure at a time investments into property, right? Okay, maybe not, but we definitely want to do some research and have some sort of strategy for buying real estate. So, what governs the prices of homes, and do we need to do the research to find this out? Generally, no, you don't really need to do that digging, we know the factors. The factors for home prices are governed by: The economy, interest rates, and demographics. These are the three major influences in general for real estate value. Now, of course, if you're buying land, various other things matter, how level is it, are we going to need to do some work to the land before we can actually lay foundation, how is drainage etc. If there is a house, then we have even more factors, like the roof, windows, heating/AC, floors, foundation, and so on. We can begin to consider these factors later, but first we'll start at the macro level. You will see how quickly our data sets inflate here as it is, it'll blow up fast. So, our first step is to just collect the data. Quandl still represents a great place to start, but this time let's automate the data grabbing. We're going to pull housing data for the 50 states first, but then we stand to try to gather other data as well. We definitely dont want to be manually pulling this data. First, if you do not already have an account, you need to get one. This will give you an API key and unlimited API requests to the free data, which is awesome. Once you create an account, go to your account / me, whatever they are calling it at the time, and then find the section marked API key. That's your key, which you will need. Next, we want to grab the Quandl module. We really don't need the module to make requests at all, but it's a very small module, and the size is worth the slight ease it gives us, so might as well. Open up your terminal/cmd.exe and do pip install quandl (again, remember to specify the full path to pip if pip is not recognized). Next, we're ready to rumble, open up a new editor. http://pythonprogramming.net https://twitter.com/sentdex
Views: 105780 sentdex
IOM 528 - Data Warehousing Business Intelligence, and Data Mining - Professor Arif Ansari
 
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Professor Arif Ansari This course helps to build Business Analytics skill set required by companies. At least sixty percent of the class time is spent on data mining which is especially useful to companies, because it allows you to understand customers to a level not possible before. This course is about how companies apply two new technologies, data warehousing (DW) and data mining (DM, including business intelligence, BI) to empower their employees, and build and manage a customer-centric business model. Besides learning the strategic role DW and DM plays in an enterprise, you will also get a close-up look at DW and DM by working on cases and gaining hands-on experience using software tools. Students taking this class will get an overview of the technologies of DW and BI/DM from a managerial perspective. Finance companies have started data mining, example: Capital One Credit Card Company. Real Estate companies are now using neural networks to evaluate the price of homes.
Learn Web Scraping, Data Mining Course , Lecture 01
 
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Download Any web scraper from https://webscrapingtools.net/ Ultimate Extractor for Every Task Whenever you need to extract some typical data from multiple web pages, Web Content Extractor is the ultimate solution. Extract product pricing data, grab real estate data; parse Forex and stock market figures; extract book, song or movie information; gather news and articles on a certain topic; extract web content on hotels or car rentals in a given country; collect information from dating sites or job web-resources - this is merely a short list of Web Content Extractor possible applications. Of course, you are not limited with the above; the tool perfectly works with any kind of web information and thanks to fine customization it can deal with any website whatsoever. Powerful Web Crawler Engine Inside Powerful, multi-threaded web crawler engine provides for quick and efficient data extraction. Web Content Extractor supports password protected websites and can access the Internet via multiple proxy-servers ensuring speed and reliability. Not only does the crawler support downloading with up to 20 simultaneous threads, it is also highly configurable. You can set it to ignore certain URLs or include them into the extraction basing on a title or a URL match. Such flexibility means accurate web scraping at high pace, as well as is an additional way to customize the process. Wide Exporting Capabilities In addition to its immerse extracting power, the program also features wide exporting capabilities. You can save gathered data into a plain CSV or text file, export to HTML or XML, as well as to put the data right into a given database format using the built-in possibility to export information into MSSQL/MySQL script or directly into any ODBC-compatible destination. This allows you to apply the scraped data immediately - say, perform an in-depth analysis using spreadsheet application, create a summary report and upload it via FTP, or import the data into your own application or service's database. Enjoy Automation! Web Content Extractor provides serious automation of the website scraping task. Usually, you only need to specify a basic extraction pattern (done in few clicks too) and run the extraction process. The program automatically scans the provided URLs and scrapes all the info that meets the specified template. And command line options allow to set the program to work with any third-party scheduler. The program doesn't try to outsmart a user though. Yes, it determines elements on a page and the type of the data field suggesting the extraction results as a preview, but you can always make necessary changes or adjust the program's choice manually if needed. Reliable, highly automated, powerful web scraping software Web Content Extractor is certainly a tool you need if your business is somehow related to web data extraction. Being a huge time-saver, this tool has probably the best value for money, plus you can try it for free! Download the free evaluation version now!
Views: 27 WebScrapingTools
Using Insights for ArcGIS with Python/R
 
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In this session, you will learn how to leverage both Python and R to extend Insights capabilities and visualize outputs from these kernels. Both Python and R are widely used languages, popular among academia and industry alike. Insights allows you to leverage these powerful languages within Insights has many capabilities to bring the easy to use experiences of Insights together with these languages. -------------------------------------------------------------------------------------------------------------------------- Follow us on Social Media! Twitter: https://twitter.com/Esri Facebook: https://facebook.com/EsriGIS LinkedIn: https://www.linkedin.com/company/esri Instagram: https://www.instagram.com/esrigram The Science of Where: http://www.esri.com
Views: 386 Esri Events
Mozenda PT1 - Real Estate Wholesale - Champaign
 
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Schwaps (http://www.schwaps.com) founder, Justin McClelland (http://www.justinmcclelland.com), chronicles his company in its beginning phase. In this video he gives a walk-through of how he used Mozenda software to pull address info for investor condo owners.
Views: 257 Justin McClelland
Website listing real estate transaction data to open
 
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A new website listing real estate transaction info opens tomorrow. Prospective buyers and sellers can use the website to see valuable information related to property sales in their area. Bookkeeping for real estate transactions has increased dramatically recently. Deals must be keyed into a website established by the Ministry of the Interior. To meet the new requirements, land administration agencies have had to hire new personnel.Hsiao Chao-yangLand Administration AgentWe had to increase our staff by 10 percent which led to additional costs. Also there are too many sales forms.After two and a half months of registering and collating data, tomorrow the website opens for business. Users choose the information they want to see, including sale prices, rent or presale prices. They then enter t
Joining 30 year mortgage rate - p.13 Data Analysis with Python and Pandas Tutorial
 
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Welcome to Part 13 of our Data Analysis with Python and Pandas, using Real Estate investing as an example. At this point, we've learned quite a bit about what Pandas has to offer us, and we'll come up here with a bit of a challenge! As we've covered so far, we can make relatively low-risk investments based on divergence between highly correlated state pairs and probably do just fine. We'll cover testing this strategy later on, but, for now, let's look into acquiring the other necessary data that comprises housing values: Interest rates. Now, there are many different types of mortgage rates both in the way interest is accrued as well as the time-frame for the loan. Opinions vary over the years, and depending on the current market situation, on whether you want a 10 year, 15 year, or 30 year mortgage. Then you have to consider if you want an adjustable rate, or maybe along the way you decide you want to re-finance your home. At the end of the data, all of this data is finite, but ultimately will likely be a bit too noisy. For now, let's just keep it simple, and look into the 30 year conventional mortgage rate. Now, this data should be very negatively correlated with the House Price Index (HPI). Before even bothering with this code, I would automatically assume and expect that the correlation wont be as negatively strong as the higher-than-90% that we were getting with state HPI correlation, certainly less than -0.9, but also it should be greater than -0.5. The interest rate is of course important, but correlation to the overall HPI was so very strong because these were very similar statistics. The interest rate is of course related, but not as directly as other HPI values, or the US HPI. Sample code and text-based tutorial: http://pythonprogramming.net/joining-mortgage-rate-data-analysis-python-pandas-tutorial/ http://pythonprogramming.net https://twitter.com/sentdex
Views: 20402 sentdex
Data Mining Company In India
 
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http://www.labdhie.com/ - Labdhie Support Services is the go-to company for all your data mining or research needs. Watch the demo. Be it internet research, data collection, real estate MLS, linkedin research or even blog search, we do it all.
Views: 522 Labdhie
How to extract Real Estate Property details from Sansalvadorcity website
 
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www.webharvy.com NOTES : 1. Login to the website before configuration / mining 2. To know more about URL based Autopagination - Refer https://www.webharvy.com/tour3.html#URLAuto 3. Use 'Capture Following Text' feature wherever it works fine
Views: 61 sysnucleus
Paul Lucey - The impact of analytics and data on the mining industry
 
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We got up close and personal with Paul Lucey, CEO of Mine Vision Systems at the 2016 International Mining and Resources Conference. In this video, Paul explains how using data can speed up the decision making process and what he thinks is coming next in mining technology. IMARC returns to the Melbourne Convention & Exhibition Centre 30 October - 2 November 2017. For more information please visit http://imarcmelbourne.com/
"Open for Business:" Online Analytics, Behavioral Targeting, Data Mining | Aug. 2, 2011
 
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The School of Business partnered with the Wharton Club of South Florida on August 2 to present a panel of some of the country's leading voices on the intersection of Internet marketing and digital media. The panel examined the latest cutting-edge changes in online analytics, social media, commerce, and advertising that are revolutionizing how companies use data to understand and communicate with their customers. See how the University of Miami School of Business Administration connects with thought leaders from around the world at http://bus.miami.edu
Saint Louis, MO Real Estate Wholesale - Buyer Tip - JustinMcClelland.com
 
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Schwaps (http://www.schwaps.com/wholesale) founder, Justin McClelland (http://www.justinmcclelland.com), chronicles his company in its beginning phase. In this video he gives a tip about how to locate buyers for your wholesale/retail properties.
Views: 336 Justin McClelland
Mozenda - Data Mining - Web Crawler - CaptureDefinitions
 
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http://www.twitter.com/jbmcclelland Justin McClelland (http://www.justinmcclelland.com), provides various how-to demonstrations and example applications of the Mozenda software (http://www.getmozenda.com ). The Mozenda, Software as a Service (SaaS), platform is ideal for performing comprehensive web data gathering (a.k.a web data extraction, screen scraping, web crawling, web harvesting, etc.)
Views: 323 Justin McClelland
Mozenda - Data Mining - Web Crawler - RefineList
 
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http://www.twitter.com/jbmcclelland Justin McClelland (http://www.justinmcclelland.com), provides various how-to demonstrations and example applications of the Mozenda software (http://www.getmozenda.com ). The Mozenda, Software as a Service (SaaS), platform is ideal for performing comprehensive web data gathering (a.k.a web data extraction, screen scraping, web crawling, web harvesting, etc.)
Views: 342 Justin McClelland
Craigslist Data Extraction - Real Estate - WebHarvy
 
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Scraping data from craigslist.com using WebHarvy - www.webharvy.com
Views: 445 sysnucleus
Web Scraping, Screen Scraping, Web Data Mining, Data Extractor
 
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Input FORMAT SUPPORT [website, webpage, Text, pdf, CSV, database] Output FORMAT SUPPORT [ excel, csv, tsv, pdf, xml, html, sql, MySql ] » Hotel website [ hotel name, address, images, reviews, latitude-longitude, price ] Scraping » Hotel price scraping for marketing intelligence [againast to your competitor] » Real Estate Data Extraction » Extract Store Details » University's Web Data Scraping » Extract Product Description » Web Information Extractor » Craigslist Email Extractor » Metadata Extraction » Website Email Extraction » Scraping Business Directory » Yellow Pages Scraping » PriceGrabber Data Extraction » Scraping Property Information » Amazon Product Extraction » Download Product Images » Automate osCommerce Product Upload » Scraping Business Contact » Craigslist Posting Service » Imdb Data Extraction » Meta Data Extraction » Scraping From Dynamic Pages » Extract Lyrics Data » Email Scraping & Extraction » Scraping Customer List » Scraping Data From WebSite ----------------------------------- Expertise In -------------------------- » Hotel Website Scraping [expedia.com, hotels.com, booking.com, orbitz.com, airasia.com, easybook.com, laterooms.com, travelocity.com, thomascook.com, activehotels.com, priceline.com, lastminute.com, yatra.com, makemytrip.com etc.] » Ads Classifieds Scraping [gumtree.com, olx.com, craigslist.com etc] » Real Estate Scraping [99acres.com, www.zillow.com, www.trulia.com, www.realtor.com, www.agentimage.com, www.realtysoft.com, www.realestate.com.au etc.] » Product catalog Scraping [amazon.com , ebay.com, yellowpage, whitepage etc.] Contact if any service require [ [email protected] ]
Views: 62146 vickyrathee2005
Text By the Bay 2015: Sudeep Das, Learning From the Diner's Experience
 
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I will talk about how we are using data science to help transform OpenTable into a local dining expert who knows you very well, and can help you and others find the best dining experience wherever we travel! This entails a whole slew of tools from natural language processing, recommendation system engineering, sentiment analysis that have to work in synch to make that magical experience happen. One of our main sources of insight are the reviews left by diners on our website. In this talk, I will stress on what we are learning from our rich set of diner reviews, especially using topic modeling as a core tool. I will touch upon various possible applications of this technique that we are currently exploring in both restaurateur facing and diner facing features. Sudeep Das is a Data Scientist at OpenTable, where his main focus is on mining reviews and restaurant data to extract actionable insights and enable a personalized dining experience. He has broad experience with NLP methods, especially topic modeling and its applications. Before moving into the Data Science space, Sudeep was an Astrophysicist (Princeton PhD, UC Berkeley postdoc) researching the properties of the early universe, and co-authored about 60 peer reviewed papers. He blogs about data science, astrophysics, and random things at http://datamusing.info/. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 368 FunctionalTV
Big data and social mining
 
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Cosa sono i big data? in che modo sono utili allo studio sulla società? Dino Pedreschi, Fosca Giannotti e il gruppo di ricercatori del KDD Lab (Knowledge Discovery and Data Mining Laboratory) laboratorio congiunto del Dipartimento di Informatica dell'Università di Pisa e dell'istituto di Scienza e Tecnologie dell'Informazione ISTI-CNR) ci spiegano cosa sono le "briciole digitali" lasciate dagli utenti e come aiutano ad interpretare la complessità della vita sociale.
Views: 3798 VideoUNIPI
spocto :: Case study : Real Estate
 
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To know more - http://bit.ly/2oTHJXn spocto's unique machine learning algorithms & artificial learning which provides solution to create persona.
Views: 182 spocto Support
Constructing Predictive Model Using IBM SPSS Modeler
 
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This tutorial shows steps to construct a predictive model using IBM SPSS Modeler. We use the Boston Housing dataset for our illustration. In addition, we also discuss how to evaluate the performance of the model using different nodes such as Graph Evaluation and Data Analysis Node. I hope you enjoy it and please let me know if you have any questions. Thanks for watching. Dataset can be downloaded from here: https://drive.google.com/open?id=1slSxq8Dxq8Xy8W6l84zl17r-bJjKv3MI
Views: 20706 THE IT CHANNEL
Text By the Bay 2015: Jean Sini, Keynote: Text(ing): The Rebirth
 
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What’s going on at the intersection of texting and natural language processing? NLP is often relegated to an after-the-fact, or off-to-the-side role: spam detection or gleaning business insight from user communication and comments that have already occurred. But a new generation of applications - Luka, Thumbtack, Fountain - put the understanding of natural language front and center, often as the first thing that consumers touch. We'll take a deep look at Fountain, both how it classifies plain English questions, and how it identifies which of 70,000+ human skills is necessary to solve the question. The talk will cover both language classification and relationship extraction, particularly focusing on how human expertise is interrelated. Jean runs technology and engineering at Fountain, including systems architecture, security, and distributed computing. Prior to Fountain, Jean was CTO at One Kings Lane, managing a team of 60+ engineers, product managers, and designers. Earlier, he was VP of Data Aggregation for Mint.com, Founder of data-mining company Untangly, and co-founder of Activeweave, sold to TwelveFold Media in 2008. Jean is also an angel investor and advisor to early stage startups including Boxbee, Canva, Treasure Data and FieldWire. He holds a Masters in Computer Science from Telecom Paris at Paris University. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 210 FunctionalTV
Data Mining 2015 11 03
 
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Hidden Treasures: Dig Up Some GIS Data at Your Local Library For those who missed Jeff Essic’s presentation at NC AUG on data mining and mapping with library resources, now you can see what you missed! Jeff give us an encore presentation that will take us through the different hidden (or not obvious) links to download shapefiles, explore databases, and more. Presented on 11/3/2015
Visualizing with Text, Facts & Figures, Images, Video, Tables, and Wordclouds
 
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Learn how to make the most of text elements with tables, wordclouds, images and video. Follow the Infogram blog: https://infogram.com/blog Best Data Visualization Resources: https://infogram.com/page/best-resources (Music: Glass Boy "35=15")
Views: 1348 Infogram
Forum Datamining
 
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Forum Datamining
Views: 48 r46e9rbwzz
Mining a document in Web of Science
 
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Web of Science is a powerful sciences and social sciences database with several advantages over Google Scholar. First, it only contains journals that have a credible peer-review process. Second, it’s easier to mine a single document for more of these reliable sources of information. This video explains how to use the citation function in Web of Science to get additional articles.
178: How do I associate an action with nearby text or an image?
 
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How to associate an action in the Agent Builder with an image or text near the target data. This improves the accuracy of a capture or click action when the target object is not in the same place from page to page.
Views: 57 MozendaSupport
Data Scraping and Data Mining Services
 
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The Leader of Data mining, web scraping and data extraction services is here. If you are looking to scrape a website's data, no matter which website; Like a business directory as yellowpages, yelp or google places and big place OR A product listings website, OR a real estate, forsalebyowner, or agents website OR an online retails store with thousands of products, I will scrape/copy paste all the required data from that website for you, even if you need images, or your required data is hidden behind scripts/captcha I can do that, I have hundreds of 5 star reviews for the same service on my fiverr. Please note I am a data miner and web scraper since 2008, and I am serving my clients since then with best quality data scraping and business lists building services to extend and grow their business.Please give me a chance to send you a sample before order placement. Please check my fiverr profile for experience reference. www.fiverr.com/hajikhan Thanks and looking forward to a chance to serve you. Best regards Please must watch my gig explainer video I made it myself :) Here; https://www.fiverr.com/hajikhan/do-data-mining-extraction-web-scraping-email-list-leads
Realtor Search: Elasticsearch and Python in Practice
 
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Aleksandar Velkoski http://www.pyvideo.org/video/3545/realtor-search-elasticsearch-and-python-practice Part of our Master Member Profile project, the REALTOR search is a Web2py-based application, leveraging Elasticsearch, that aims to provide users (staff and members) with a means to query comprehensive member profiles. With relevant data gathered and presented via an easy-to-use centralized platform, staff can leverage information to enhance services provided to members and members to enhance productivity.
Views: 5209 Next Day Video
Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8
 
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Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 228729 Google Developers
Text By the Bay 2015: Jeff Sukharev, Machine Translation Approach for Name Matching in Record Link
 
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Record linkage, or entity resolution, is an important area of data mining. Name matching is a key component of systems for record linkage. Alternative spellings of the same name are a common occurrence in many applications. We use the largest collection of genealogy person records in the world together with user search query logs to build name- matching models. The procedure for building a crowd-sourced training set is outlined together with the presentation of our method. We cast the problem of learning alternative spellings as a machine translation problem at the character level. We use information retrieval evaluation methodology to show that this method substantially outperforms on our data a number of standard well known phonetic and string similarity methods in terms of precision and recall. Our result can lead to a significant practical impact in entity resolution applications. BS, MS Computer Science UC Santa Cruz, PhD candidate Computer Science UC Davis. Senior Data Scientist at Ancestry.com working on record linkage applications. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 225 FunctionalTV
CallFire Experience PT1 - Real Estate Wholesale - Champaign Illinois
 
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Schwaps (http://www.schwaps.com) founder, Justin McClelland (http://www.justinmcclelland.com), chronicles his company in its beginning phase. In this video he gives an overview of his experience with the CallFire IVR service.
Views: 1056 Justin McClelland
Advanced Custom Fields & Elementor Pro
 
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Watch the first minute to see an explanation of Advanced Custom Fields, Custom Post Types and Elementor. In this video I show you how to create advanced websites using Custom Post Types, Advanced Custom Fields and Elementor Pro Templates.
Views: 28690 Ferdy Korpershoek
Natural Language based search - user interface for Real Estate Search Engine
 
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Natural Language based Search user interface for a one of a kind Real Estate Search Engine.
Views: 481 veeduready
Mozenda - Data Mining - Web Crawler - LeadGenExample
 
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http://www.twitter.com/jbmcclelland Justin McClelland (http://www.justinmcclelland.com), provides various how-to demonstrations and example applications of the Mozenda software (http://www.getmozenda.com ). The Mozenda, Software as a Service (SaaS), platform is ideal for performing comprehensive web data gathering (a.k.a web data extraction, screen scraping, web crawling, web harvesting, etc.) Follow Me on Twitter: http://www.twitter.com/JBMcClelland
Views: 4232 Justin McClelland
Text By the Bay 2015: Stephen Merity, A Web Worth of Data: Common Crawl for NLP
 
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The Common Crawl corpus contains petabytes of web crawl data and is a treasure trove of potential experiments. To introduce you to the possibilities that web crawl data has for NLP, we will take a detailed look at how the data has been used by various experiments and how to get started with the data yourself. Stephen Merity is responsible for crawling billions of pages a month at Common Crawl, a non-profit that provides petabytes of web data free of charge. Prior to joining Common Crawl, Stephen worked with Freelancer.com and Grok Learning in Australia. He holds a Masters of CSE from Harvard University and a Bachelors (Honours) from the University of Sydney in NLP. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 1241 FunctionalTV