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Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 13661 Stat Pharm
PDF Data Scraping
 
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Automated web scraping services provide fast data acquirement in structured format. No matter if used for big data, data mining, artificial intelligence, machine learning or business intelligence applications. The scraped data come from various sources and forms. It can be websites, various databases, XML feeds and CSV, TXT or XLS file formats for example. Billions of PDF files stored online form a huge data library worth scraping. Have you ever tried to get any data from various PDF files? Then you know how panful it is. We have created an algorithm that allows you to extract data in an easily readable structured way. With PDFix we can recognize all logical structures and we can give you a hierarchical structure of document elements in a correct reading order. With the PDFix SDK we believe your web crawler can be programmed to access the PDF files and: - Search Text inside PDFs – you can find and extract specific information - Detect and Export Tables - Extract Annotations - Detect and Extract Related Images - Use Regular Expression, Pattern Matching - Detect and Scrape information from Charts Structured format You will need the scraped data from PDFs in various formats. With the PDFix you will get a structured output in: - CSV - HTML - XML - JSON
Views: 803 Team PDFix
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: 1017456 David Langer
The Best Way to Prepare a Dataset Easily
 
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In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 191393 Siraj Raval
The beauty of data visualization - David McCandless
 
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View full lesson: http://ed.ted.com/lessons/david-mccandless-the-beauty-of-data-visualization David McCandless turns complex data sets, like worldwide military spending, media buzz, and Facebook status updates, into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world. Talk by David McCandless.
Views: 635459 TED-Ed
DATA SCIENCE FREE BOOKS ONLINE | DOWNLOAD YOUR COPY
 
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Here's a list of 10 must read book on Data Science & Machine Learning. Foundations of DATA SCIENCE Book www.cs.cornell.edu/jeh/book.pdf Understanding Machine Learning Book www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf The Elements of Statistical Learning Book web.stanford.edu/~hastie/Papers/ESLII.pdf An Introduction to Statistical Learning Book www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf Mining of Massive Data Sets Book infolab.stanford.edu/~ullman/mmds/book.pdf
Views: 2110 DATA SCIENCE
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 397417 APMonitor.com
Top 10 Must Free Resource for Data Science and Machine Learning
 
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Must Read Books to kick start in AI | Machine Learning | Data Science Resource Links : Machine Learning Yearning By Andrew Ng http://www.mlyearning.org/ Think Stats: Probability and Statistics for Programmers by Allen Downey http://www.greenteapress.com/thinkstats/ Foundations of Data Science https://www.cs.cornell.edu/jeh/book.pdf Understanding Machine Learning: From Theory to Algorithms http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/ Python Data Science Handbook https://jakevdp.github.io/PythonDataScienceHandbook/ GitHub - https://github.com/jakevdp/PythonDataScienceHandbook The Elements of Statistical Learning https://web.stanford.edu/~hastie/ElemStatLearn//printings/ESLII_print10.pdf Natural Language Processing with Python https://www.nltk.org/book/ Probabilistic Programming & Bayesian Methods for Hackers http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/ GitHub - https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers Deep Learning http://www.deeplearningbook.org/ A Programmer's Guide to Data Mining http://guidetodatamining.com/ An Introduction to Statistical Learning Video Lectures - https://www.alsharif.info/iom530 Book - http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf Machine Learning and Big Data http://www.kareemalkaseer.com/books/ml/word-of-intro Thank you and Happy Learning
Views: 530 Pinku Ki Pathshala
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 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 Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Telegram: https://t.me/edurekaupdates
Views: 515998 edureka!
Import Data and Analyze with Python
 
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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 213368 APMonitor.com
Video tutorial for crowdsourcing PDF data mining in Crowdcrafting
 
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This video shows how you can import from Dropbox PDF files into Crowdcrafting, to crowdsource the analysis of the PDF documents with just a few clicks.
Mathematics of Machine Learning
 
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Do you need to know math to do machine learning? Yes! The big 4 math disciplines that make up machine learning are linear algebra, probability theory, calculus, and statistics. I'm going to cover how each are used by going through a linear regression problem that predicts the price of an apartment in NYC based on its price per square foot. Then we'll switch over to a logistic regression model to change it up a bit. This will be a hands-on way to see how each of these disciplines are used in the field. Code for this video (with coding challenge): https://github.com/llSourcell/math_of_machine_learning Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Sign up for the next course at The School of AI: http://theschool.ai/ More learning resources: https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/ https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning https://courses.washington.edu/css490/2012.Winter/lecture_slides/02_math_essentials.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 265411 Siraj Raval
Lecture: Mathematics of Big Data and Machine Learning
 
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MIT RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 View the complete course: https://ocw.mit.edu/RESLL-005F12 Instructor: Jeremy Kepner Jeremy Kepner talked about his newly released book, "Mathematics of Big Data," which serves as the motivational material for the D4M course. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 109103 MIT OpenCourseWare
Count min sketch | Efficient algorithm for counting stream of data | system design components
 
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Count Min sketch is a simple technique to summarize large amounts of frequency data. which is widely used in many places where there is a streaming big data. Donate/Patreon: https://www.patreon.com/techdummies CODE: ---------------------------------------------------------------------------- By Varun Vats: https://gist.github.com/VarunVats9/7f379199d7658b96d479ee3c945f1b4a Applications of count min sketch: ---------------------------------------------------------------------------- http://theory.stanford.edu/~tim/s15/l/l2.pdf http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/util/sketch/CountMinSketch.html Applications using Count Tracking There are dozens of applications of count tracking and in particular, the Count-Min sketch datastructure that goes beyond the task of approximating data distributions. We give three examples. 1. A more general query is to identify the Heavy-Hitters, that is, the query HH(k) returns theset of items which have large frequency (say 1/k of the overall frequency). Count trackingcan be used to directly answer this query, by considering the frequency of each item. Whenthere are very many possible items, answering the query in this way can be quite slow. Theprocess can be sped up immensely by keeping additional information about the frequenciesof groups of items [6], at the expense of storing additional sketches. As well as being ofinterest in mining applications, finding heavy-hitters is also of interest in the context of signalprocessing. Here, viewing the signal as defining a data distribution, recovering the heavy-hitters is key to building the best approximation of the signal. As a result, the Count-Minsketch can be used in compressed sensing, a signal acquisition paradigm that has recentlyrevolutionized signal processing [7]. 2. One application where very large data sets arise is in Natural Language Processing (NLP).Here, it is important to keep statistics on the frequency of word combinations, such as pairsor triplets of words that occur in sequence. In one experiment, researchers compacted a large6 Page 7 90GB corpus down to a (memory friendly) 8GB Count-Min sketch [8]. This proved to be justas effective for their word similarity tasks as using the exact data. 3. A third example is in designing a mechanism to help users pick a safe password. To makepassword guessing difficult, we can track the frequency of passwords online and disallowcurrently popular ones. This is precisely the count tracking problem. Recently, this wasput into practice using the Count-Min data structure to do count tracking (see http://www.youtube.com/watch?v=qo1cOJFEF0U). A nice feature of this solution is that the impactof a false positive—erroneously declaring a rare password choice to be too popular and sodisallowing it—is only a mild inconvenience to the user
Importing Data into R - How to import csv and text files into R
 
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In this video you will learn how to import your flat files into R. Want to take the interactive coding exercises and earn a certificate? Join DataCamp today, and start our intermediate R tutorial for free: https://www.datacamp.com/courses/importing-data-into-r In this first chapter, we'll start with flat files. They're typically simple text files that contain table data. Have a look at states.csv, a flat file containing comma-separated values. The data lists basic information on some US states. The first line here gives the names of the different columns or fields. After that, each line is a record, and the fields are separated by a comma, hence the name comma-separated values. For example, there's the state Hawaii with the capital Honolulu and a total population of 1.42 million. What would that data look like in R? Well, actually, the structure nicely corresponds to a data frame in R, that ideally looks like this: the rows in the data frame correspond to the records and the columns of the data frame correspond to the fields. The field names are used to name the data frame columns. But how to go from the CSV file to this data frame? The mother of all these data import functions is the read.table() function. It can read in any file in table format and create a data frame from it. The number of arguments you can specify for this function is huge, so I won't go through each and every one of these arguments. Instead, let's have a look at the read.table() call that imports states.csv and try to understand what happens. The first argument of the read.table() function is the path to the file you want to import into R. If the file is in your current working directory, simply passing the filename as a character string works. If your file is located somewhere else, things get tricky. Depending on the platform you're working on, Linux, Microsoft, Mac, whatever, file paths are specified differently. To build a path to a file in a platform-independent way, you can use the file.path() function. Now for the header argument. If you set this to TRUE, you tell R that the first row of the text file contains the variable names, which is the case here. read.table() sets this argument FALSE by default, which would mean that the first row is already an observation. Next, sep is the argument that specifies how fields in a record are separated. For our csv file here, the field separator is a comma, so we use a comma inside quotes. Finally, the stringsAsFactors argument is pretty important. It's TRUE by default, which means that columns, or variables, that are strings, are imported into R as factors, the data structure to store categorical variables. In this case, the column containing the country names shouldn't be a factor, so we set stringsAsFactors to FALSE. If we actually run this call now, we indeed get a data frame with 5 observations and 4 variables, that corresponds nicely to the CSV file we started with. The read table function works fine, but it's pretty tiring to specify all these arguments every time, right? CSV files are a common and standardized type of flat files. That's why the utils package also provides the read.csv function. This function is a wrapper around the read.table() function, so read.csv() calls read.table() behind the scenes, but with different default arguments to match with the CSV format. More specifically, the default for header is TRUE and for sep is a comma, so you don't have to manually specify these anymore. This means that this read.table() call from before is thus exactly the same as this read.csv() call. Apart from CSV files, there are also other types of flat files. Take this tab-delimited file, states.txt, with the same data: To import it with read.table(), you again have to specify a bunch of arguments. This time, you should point to the .txt file instead of the .csv file, and the sep argument should be set to a tab, so backslash t. You can also use the read.delim() function, which again is a wrapper around read.table; the default arguments for header and sep are adapted, among some others. The result of both calls is again a nice translation of the flat file to a an R data frame. Now, there's one last thing I want to discuss here. Have a look at this US csv file and its european counterpart, states_eu.csv. You'll notice that the Europeans use commas for decimal points, while normally one uses the dot. This means that they can't use the comma as the field-delimiter anymore, they need a semicolon. To deal with this easily, R provides the read.csv2() function. Both the sep argument as the dec argument, to tell which character is used for decimal points, are different. Likewise, for read.delim() you have a read.delim2() alternative. Can you spot the differences again? This time, only the dec argument had to change.
Views: 56069 DataCamp
Basic Data Analysis with Java : Business Intelligence | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2w2lQqE]. The aim of this video is to deal with Business Intelligence. It will use Apache POI for creating and reading spreadsheets, as well as show what users will do in MS Excel o Understand why as a data analyst, you need to save time using MS Excel o Perform some reads and writes of existing MS Excel spreadsheets For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 3003 Packt Video
Excel Magic Trick 1336: Power Query: Import Big Data Text Files: Connection Only or Data Model?
 
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Download File: http://people.highline.edu/mgirvin/excelisfun.htm See how to use Import 10 Text Files and Append (combine) then into a single Proper Data Set before making a PivotTable Report. Compare and Contrast whether we should use Connection Only or Data Model to store the data. 1. (00:18) Introduction & Look at Text Files that Contain 7 Million Transactional Records 2. (01:43) Power Query (Get & Transform) Import From Folder to append (combine) 10 Text Files that contain 7 Millions transactional records. 3. (05:07) Load Data as Connection Only and Make PivotTable 4. (08:17) Load Data into Data Model and Make PivotTable. 5. (10:46) Summary
Views: 32677 ExcelIsFun
R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 28204 DataCamp
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 68171 deltaDNA
The Government Will Use Your Kids As Guinea Pigs And For Data Mining!
 
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This is ALL documented from government legislation and sources. Here are links to the documents he talks about but after searching for some of them on Google, the links go to blank pages as if the Department of Education doesn't want you to see them. How did that happen? Really, try finding them yourself. H.R. 1 Page 67 and 169 http://www.gpo.gov/fdsys/pkg/BILLS-111hr1enr/pdf/BILLS-111hr1enr.pdf Race To The Top Executive Summary http://www2.ed.gov/programs/racetothetop/executive-summary.pdf Promoting Grit, Tenacity, and Perseverance:Critical Factors for Success in the 21st Century http://www.ed.gov/edblogs/technology/files/2013/02/OET-Draft-Grit-Report-2-17-13.pdf Links to FERPA http://www2.ed.gov/policy/gen/guid/fpco/ferpa www.ed.gov/policy/gen/guid/fpco Please also read this article: http://bit.ly/167Cphd Oh, and please go to my website to learn about our founding principles: http://www.idezignmedia.com/constitution/index.html
Views: 1665 Eddie Stannard
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 167163 Timothy DAuria
ROC Curves and Area Under the Curve (AUC) Explained
 
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An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 315350 Data School
Unstructured data processing with Apache Hadoop and Apache Spark
 
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This video demonstrates how easily one can use hTRUNK to extract and process Unstructured data with Apache Hadoop and Apache Spark
Views: 5326 hTRUNK
The best stats you've ever seen | Hans Rosling
 
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http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2913073 TED
Data Analytics for Beginners | Introduction to Data Analytics | Data Analytics Tutorial
 
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Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing Survey Data • What is Business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems • Coding, coding tip • Data Cleaning • Univariate Data Analysis • Statistics Describing a continuous variable distribution • Standard deviation • Distribution and percentiles • Analysis of categorical data • Observed Vs Expected Distribution • Identifying and solving business use cases • Recognizing, defining, structuring and analyzing the problem • Interpreting results and making the decision • Case Study Get started with Data Analytics with this tutorial. Happy Learning 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: 269994 ACADGILD
Tutorial #2 - Platforms for Big Data Analytics
 
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Platforms for Big Data Analytics with Dr. Chandan Reddy, Wayne State Tutorial Information: http://dmkd.cs.wayne.edu/TUTORIAL/Bigdata/ The paper is available at: http://dmkd.cs.wayne.edu/Papers/JBD14.pdf A Survey on Platforms for Big Data Analytics, Journal of Big Data, 2014
Concept Drift Detector in Data Stream Mining
 
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Jorge Casillas, Shuo Wang, Xin Yao, Concept Drift Detection in Histogram-Based Straightforward Data Stream Classification, 6th International Workshop on Data Science and Big Data Analytics, IEEE International Conference on Data Mining, November 17-20, 2018 - Singapore http://decsai.ugr.es/~casillas/downloads/papers/casillas-ci44-icdm18.pdf This presentation shows a novel algorithm to accurately detect changes in non-stationary data streams in a very efficiently way. If you want to know how the yacare caiman, the cheetah and the racer snake are related to this research, do not stop watching the video! More videos here: http://decsai.ugr.es/~casillas/videos.html
Views: 218 Jorge Casillas
Analyzing And Visualizing Data With Excel 2016
 
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In this workshop, get an introduction to the latest analysis and visualization capabilities in Excel 2016. See how to import data from different sources, create mash/ups between data sources, and prepare the data for analysis. After preparing the data, learn about how business calculations - from simple to more advanced - can be expressed using DAX, how the result can be visualized and shared.
Views: 34033 Microsoft Power BI
K mean clustering algorithm with solve example
 
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#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 432304 Last moment tuitions
A.I. Is Monitoring You Right Now and Here’s How It's Using Your Data
 
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There's wisdom in crowds, and scientists are applying artificial intelligence and machine learning to better predict global crises and outbreaks. Read More: You Could Live On One Of These Moons With an Oxygen Mask and Heavy Jacket https://www.youtube.com/watch?v=9t0Cziw6AbI Subscribe! https://www.youtube.com/user/DNewsChannel Read More: Identifying Behaviors in Crowd Scenes Using Stability Analysis for Dynamical Systems http://crcv.ucf.edu/papers/pamiLatest.pdf “A method is proposed for identifying five crowd behaviors (bottlenecks, fountainheads, lanes, arches, and blocking) in visual scenes.” Tracking in High Density Crowds Data Set http://crcv.ucf.edu/data/tracking.php “The Static Floor Field is aimed at capturing attractive and constant properties of the scene. These properties include preferred areas, such as dominant paths often taken by the crowd as it moves through the scene, and preferred exit locations.” Can Crowds Predict the Future? https://www.smithsonianmag.com/smart-news/can-crowds-predict-the-future-180948116/ “The Good Judgement Project is using the IARPA game as “a vehicle for social-science research to determine the most effective means of eliciting and aggregating geopolitical forecasts from a widely dispersed forecaster pool.” ____________________ Seeker inspires us to see the world through the lens of science and evokes a sense of curiosity, optimism and adventure. Visit the Seeker website https://www.seeker.com/ Subscribe now! https://www.youtube.com/user/DNewsChannel Seeker on Twitter http://twitter.com/seeker Seeker on Facebook https://www.facebook.com/SeekerMedia/ Seeker http://www.seeker.com/
Views: 148168 Seeker
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Sample Technical Proposal Mining Plan Marble
 
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More Details : http://wwa.stonecrushersolution.org/solutions/solutions.html Home About Products Solutions Contact Home Sample Technical Proposal Mining Plan Marble Sample Technical Proposal Mining Plan Marble Database Design Proposal Sample - Pdfsdocuments.comDatabase Design Proposal Sample.pdf ... 2.4 Design data mining algorithms 9. ... FYP Proposal Sample ... The Sample Request for Proposal ... Technical Design and ...Read moreResponse to RFP No. MED13002 State of West ia ...TECHNICAL PROPOSAL . ... 2.4.4 Sample Reports ... responsible for establishing the WV Medicaid RAC program.Read morePlease note: This is a sample PhD thesis proposal for …... This is a sample PhD thesis proposal for the School of ... Plan and execute field work on Cameron ... Technical Comment, v 283, p ...Read moreproject proposal of marble business ethiopiaHome project proposal of marble business ... sample business plan at project proposal granite businessreport ... Mission Report on mining in Ethiopia The ...Read moreA Guide to Proposal Planning and Writing - OEMAA Guide to Proposal Planning and Writing ... who can help you plan your proposal before you start writing. ... , and the sample letter proposalRead moreMining Software Business Plan Sample - Market …Start your own business plan ? Mining Software Business Plan. ... awareness will be technical papers by researchers involved in ... as this sample plan.Read moreTHE REPUBLIC OF SUDAN MULTI DONOR TRUST …MULTI DONOR TRUST FUND PROJECT PROPOSAL COMMUNITY DEVELOPMENT FUND PROJECT (CDF) ... CAP Community Action Plan ... for example, communities …Read moreAnnexure - environmentclearance.nic.inGEO-ORE TECHNICAL CONSULTANTS ... REVIEW OF MINING PLAN Name of the Mine Marble Mine, ... No proposal was made in the mining plan for exploration & no …Read moreSAMPLE TEMPLATE FOR PREPARING PROJECT …... target population and implementation plan ... This template is intended to serve as a sample to assist in writing a project proposal. ... SAMPLE Project Budget ...Read morePROPOSAL EXPLORATION PROGRAM OF LIMESTONE …PROPOSAL EXPLORATION PROGRAM OF LIMESTONE PROSPECTING ... of the sample site shall be marked. ... mining plan and for the calculation of reserves. b) ...Read moreStandards Australia Project Proposal FormMining. Manufacturing and ... apply the Small Complexity rating. Small - Technical report with low complexity, ... Proposal Form – Standards Development Projects ...Read moreSAP Big Data Analytics Proposal - George Mason …Project Proposal: SAP Big Data Analytics on Mobile Usage ... SAP BIG DATA ANALYTICS 6 3 TECHNICAL ... conducted on a holdout sample set of data where the …Read moreAdvanced Proposal Management TrainingAdvanced Proposal Management. ... - Troubleshooting your proposal development plan and proposal risk ... Technical Services, KMC Mining “The instructor is ...Read moreREQUEST FOR PROPOSALSREQUEST FOR PROPOSALS. ... Technical Proposal a. Company identifiers: i. ... plan for the employment of minority persons, women, ...Read moreTimmins Marketing Material RFPSection 6: Sample Work ... work plan, timeframe for completion and fee basis, according to the following guidelines. 3.1 Technical ProposalRead moreSample Business Proposals : Examples Assist …Sample business proposals are complete sample business proposals ... Mining Safety Sample Proposal; ... Temp Agency Services Sample Proposal; Technical Resume Sample;Read morecomplete feasibility report of a stone …2014-2-12?·?... http://goo.gl/Pajuu7 More About complete feasibility report of a stone ... marble quarry project pdf - BINQ Mining ... proposal template ...Read moreShore Gold Diamond Project7.2 Reclamation and Closure Plan ... Star-Orion South Diamond Project Page 7 In this proposal, ... Star-Orion South Diamond Project Page 9 2.2.2 Mining Industry in ...Read moreSAMPLE “REQUEST FOR PROPOSALS” TEMPLATEEnclosed with this Request for Proposal is a sample copy ... ? A management plan for the ... the Contractor must perform “The Work” as listed in this document.Read moreJackhammers Used In Marble Mining - Crusher USAHome? jackhammers used in marble mining ... Mining glossary of technical terms and definitions for equipment and processes in the mining
Views: 13 Estela Gallegos
Transform Unstructured Data - H2L video
 
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In Informatica Developer, create a Data Processor transformation with a parser to transform a flat file source in PDF or text format to a flat file target in XML format. In this demo, we create a Data Processor transformation, create and configure a Script with a parser, preview the example source, defined the parser, preview the Data Processor transformation, and then add the Data Processor transformation to a mapping.
Views: 15305 Informatica Support
Spark Tutorial For Beginners | Big Data Spark Tutorial | Apache Spark Tutorial | Simplilearn
 
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This Spark Tutorial For Beginner will give an overview on history of spark, Batch vs real-time processing, Limitations of MapReduce in Hadoop, Introduction to Spark, Components of Spark Project and a comparision between Hadoop ecosystem and Spark. This Spark Tutorial will explain: 1. History of Spark 2. Introduction to Spark 3. Spark Components 4. Spark Advantages Apache Spark is an open-source cluster-computing framework. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance. 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=Bigdata-Spark-QaoJNXW6SQo&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: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Bigdata-Spark-QaoJNXW6SQo&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: 91334 Simplilearn
U-SQL – A new language to process big data using C#/SQL
 
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Big data processing requires a language that hides the complexity of managing scale and provides easy integration of your custom code to handle the complex processing requirements ranging from data cleanup to advanced processing of unstructured data. The new U-SQL language from Microsoft unifies the declarative power of SQL and the extensibility of a modern programming language. This presentation provides a hands-on introduction to U-SQL, showing how easy it is let U-SQL scale code for you. See Azure Data Lake to learn more and start using U-SQL: http://aka.ms/AzureDataLake
The Data Science of Hollywood: 6 Fundamental Emotional Arcs of Movies
 
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Full paper: https://arxiv.org/ftp/arxiv/papers/1807/1807.02221.pdf The Data Science of Hollywood: Using Emotional Arcs of Movies to Drive Business Model Innovation in Entertainment Industries Much of business literature addresses the issues of consumer-centric design: how can businesses design customized services and products which accurately reflect consumer preferences? This paper uses data science natural language processing methodology to explore whether and to what extent emotions shape consumer preferences for media and entertainment content. Using a unique filtered dataset of 6,174 movie scripts, we generate a mapping of screen content to capture the emotional trajectory of each motion picture. We then combine the obtained mappings into clusters which represent groupings of consumer emotional journeys. These clusters are used to predict overall success parameters of the movies including box office revenues, viewer satisfaction levels (captured by IMDb ratings), awards, as well as the number of viewers’ and critics’ reviews. We find that like books all movie stories are dominated by 6 basic shapes. The highest box offices are associated with the Man in a Hole shape which is characterized by an emotional fall followed by an emotional rise. This shape results in financially successful movies irrespective of genre and production budget. Yet, Man in a Hole succeeds not because it produces most “liked” movies but because it generates most “talked about” movies. Interestingly, a carefully chosen combination of production budget and genre may produce a financially successful movie with any emotional shape. Implications of this analysis for generating on-demand content and for driving business model innovation in entertainment industries are discussed
Views: 676 Data-driven
Exporting SPSS Output To Word
 
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Short tutorial on exporting SPSS to Microsoft Word to accompany my book 'Discovering Statistics Using SPSS'.
Views: 211198 Andy Field
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
 
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In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
Views: 4890 MapR Technologies
Bloom Filters
 
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Bloom filters and the analysis of the probability of false positives
Views: 53905 Yoav Freund
Scanner: Efficient Video Analysis at Scale (SIGGRAPH 2018)
 
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http://scanner.run/ http://graphics.stanford.edu/papers/scanner/scanner_sig18.pdf Scanner is a system for developing applications that efficiently process large video datasets. Scanner applications can run on a multi-core laptop, a server packed with multiple GPUs, or a large number of machines in the cloud. Scanner has been used for: * Labeling and data mining large video collections: Scanner is in use at Stanford University as the compute engine for visual data mining applications that detect people, commercials, human poses, etc. in datasets as big as 70,000 hours of TV news (12 billion frames, 20 TB) or 600 feature length movies (106 million frames). * VR Video synthesis: scaling the Surround 360 VR video stitching software, which processes fourteen 2048x2048 input videos to produce 8k stereo video output. To learn more about Scanner, see the documentation below or read the SIGGRAPH 2018 Technical Paper: “Scanner: Efficient Video Analysis at Scale” by Poms, Crichton, Hanrahan, and Fatahalian.
Views: 2014 Will Crichton
Cambridge Analytica - The Power of Big Data and Psychographics
 
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Description: In a presentation at the 2016 Concordia Annual Summit in New York, Mr. Alexander Nix discusses the power of big data in global elections. Cambridge Analytica’s revolutionary approach to audience targeting, data modeling, and psychographic profiling has made them a leader in behavioral microtargeting for election processes around the world. Speaker: Mr. Alexander Nix CEO, Cambridge Analytica
Views: 494833 Concordia
Big Volcano Rumbles, 2 Billion Sources | S0 News Jan.14.2019
 
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Daily Sun, Earth and Science News OTF2019: https://otf.selz.com Info: https://www.observatoryproject.com Our Websites: http://www.Suspicious0bservers.org http://www.SpaceWeatherNews.com http://www.QuakeWatch.net http://www.ObservatoryProject.com http://www.MagneticReversal.org http://www.EarthChanges.org Facebook: https://www.facebook.com/observatoryproject/ Alerts on Twitter: https://twitter.com/TheRealS0s Wanted- Earthquake Forecasters: https://youtu.be/l1iGTd84oys Earthquake Forecasting Contest: https://youtu.be/Fsa_4jAyQsI Contest Information: http://www.quakewatch.net/contest Today's Featured Links: Iron Dust Nova: https://arxiv.org/pdf/1901.03621.pdf Water-Mining Satellite: https://today.ucf.edu/steam-powered-asteroid-hoppers-developed-ucf-collaboration/ They Can't Measure Black Hole Mass: https://arxiv.org/pdf/1901.03345.pdf 2 Billion Source Announcement: https://arxiv.org/pdf/1901.03337.pdf unWISE Viewer: http://legacysurvey.org/viewer Music by NEMES1S Links for the News: TY WindMap: https://www.windy.com Earth WindMap: http://earth.nullschool.net SDO: http://sdo.gsfc.nasa.gov/data/ Helioviewer: http://www.helioviewer.org/ SOHO: http://sohodata.nascom.nasa.gov/cgi-bin/soho_movie_theater STEREO: http://stereo.gsfc.nasa.gov/cgi-bin/images GOES Satellites: http://rammb.cira.colostate.edu/ramsdis/online/goes-16.asp Earthquakes: https://earthquake.usgs.gov/earthquakes/map RSOE: http://hisz.rsoe.hu/alertmap/index2.php suspicious observers suspicious0bservers
Views: 72395 Suspicious0bservers
BIGDATA in TELUGU
 
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Follow us on https://t.me/Learnerspage Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. BIGDATA in TELUGU https://youtu.be/jdPhsYZU_5E?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. Data science & Big data in Telugu https://youtu.be/5XQ3lmPVV8M?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD). Tableau in Telugu https://youtu.be/iPvwRyeAGYA?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX In 2020 the world will generate 50 times the amount of data as in 2011. And 75 times the number of information sources (IDC, 2011). Within these data are huge, unparalleled opportunities for human advancement. But to turn opportunities into reality, people need the power of data at their fingertips. Tableau is building software to deliver exactly that. Big Data Tool R Installation in Telugu https://youtu.be/hdTLyC-KL_I?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX https://cran.r-project.org/bin/windows/base/ https://www.rstudio.com/products/rstudio/download/ R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Tableau in Telugu:How to Creat Groups in Charts https://youtu.be/i1z1lGJvQQU?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Data Warehouse in Telugu https://youtu.be/xFLE1_V7u6M Business Intelligence is a technology based on customer and profit-oriented models that reduce operating costs and provide increased profitability by improving productivity, sales, service and helps to make decision-making capabilities at no time. Business Intelligence Models are based on multidimensional analysis and key performance indicators (KPI) of an enterprise R Programming in Telugu:How to Write CVS files and Extract Data from data.[Lesson-3] https://youtu.be/oeh9fyru9-o?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX This video is about: How to write CSV file in R . How to remove the columns from data set or data frame in R R Programming Tutorial in Telugu: How to Read data in R [Lesson2] https://youtu.be/CL0RG4NTuq4?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX In This R Tutorial you will find clear way, how to read the CVS files in R Studio. How to use commands SETWD(), READ.CSV(), HEAD(), TAIL(),VIEW() FETCH DATA FROM SQL TO EXCEL https://youtu.be/IqukX_hKEnE?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Tableau in Telugu: Tableau Colors https://youtu.be/fHvg0irp1ds?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Pareto Chart Analysis https://youtu.be/TPZaIX4S1TU Pareto Analysis is a statistical technique in decision-making used for the selection of a limited number of tasks that produce significant overall effect. It uses the Pareto Principle (also known as the 80/20 rule) the idea that by doing 20% of the work you can generate 80% of the benefit of doing the entire job. Population Pyramid Chart https://youtu.be/poWV5VsideI Download the file in below link https://drive.google.com/file/d/1eWu8zXxh1QRFQj4OJAkG_S7AuHIDqY04/view A population pyramid also called an "age pyramid" is a graphical illustration that shows the distribution of various age groups in a population
Views: 20249 Learners Page
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 470298 Brandon Weinberg
What is a HashTable Data Structure - Introduction to Hash Tables , Part 0
 
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This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions. Wan't to learn C++? I highly recommend this book http://amzn.to/1PftaSt Donate http://bit.ly/17vCDFx STILL NEED MORE HELP? Connect one-on-one with a Programming Tutor. Click the link below: https://trk.justanswer.com/aff_c?offer_id=2&aff_id=8012&url_id=238 :)
Views: 811860 Paul Programming
Scrape Websites with Python + Beautiful Soup 4 + Requests -- Coding with Python
 
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Coding with Python -- Scrape Websites with Python + Beautiful Soup + Python Requests Scraping websites for data is often a great way to do research on any given idea. This tutorial takes you through the steps of using the Python libraries Beautiful Soup 4 (http://www.crummy.com/software/BeautifulSoup/bs4/doc/#) and Python Requests (http://docs.python-requests.org/en/latest/). Reference code available under "Actions" here: https://codingforentrepreneurs.com/projects/coding-python/scrape-beautiful-soup/ Coding for Python is a series of videos designed to help you better understand how to use python. Assumes basic knowledge of python. View all my videos: http://bit.ly/1a4Ienh Join our Newsletter: http://eepurl.com/NmMcr A few ways to learn Django, Python, Jquery, and more: 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 (80k+ 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: 416640 CodingEntrepreneurs
How Twitter users connect w/TV. What's specifically wrong with Big Data. (RBDR 9.30.2015)
 
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Today on RBDR: 1) Twitter reveals results of a study that shows a new dramatic link between television viewing and tweeting. (Story link: http://fortune.com/2015/09/21/here-are-some-tv-tips-from-twitter-everyones-favorite-second-screen/) 2) If you have a strong and positive belief about the current state of Big Data, you will want to think again after you hear our interview with Slater Victoroff, CEO of indico Data Solutions, on RBDR. (Story link: http://techcrunch.com/2015/09/10/big-data-doesnt-exist/#.nzwjs5:Q70F) RBDR is sponsored by KL Communications, a collaborative research agency specializing in customer co-creation through its proprietary CrowdWeaving service. KL is pleased to provide a white paper with insights into the unique ability of its Crowdweaving co-creation service to attract greater participation from particularly creative individuals. Visit http://klcommunications.com and http://klcommunications.com/whitepaper.pdf. Don't spend your time "searching" for today's RBDR video. Subscribe to receive a personal email as soon as the new RBDR is uploaded. Click here: http://ow.ly/CfFWE
Data Analysis with MATLAB for Excel Users
 
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This webinar highlights how MATLAB can work with Excel. Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 Ready to Buy: https://goo.gl/vsIeA5 Learn MATLAB for Free: https://goo.gl/xIiHyG Many technical professionals find that they run into limitations using Excel for their data analysis applications. This webinar highlights how MATLAB can supplement the capabilities of Excel by providing access to thousands of pre-built engineering and advanced analysis functions and versatile visualization tools. Learn more about using MATLAB with Excel: http://goo.gl/3vkFMW Learn more about MATLAB: http://goo.gl/YKadxi Through product demonstrations you will see how to: • Access data from spreadsheets • Plot data and customize figures • Perform statistical analysis and fitting • Automatically generate reports to document your analysis • Freely distribute your MATLAB functions as Excel add-ins This webinar will show new features from the latest versions of MATLAB including new data types to store and manage data commonly found in spreadsheets. Previous knowledge of MATLAB is not required. About the Presenter: Adam Filion holds a BS and MS in Aerospace Engineering from Virginia Tech. His research involved nonlinear controls of spacecraft and periodic orbits in the three-body problem. After graduating he joined the MathWorks Engineering Development Group in 2010 and moved to Applications Engineering in 2012.
Views: 242086 MATLAB