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Rattle - Data Mining in R
 
25:47
Overview of using Rattle - a GUI data mining tool in R. Overview covers some of the basic operations that can be performed in Rattle such as loading data, exploring the data and applying some of the data mining algorithms on the data - all this without actually having to type any R code
Views: 37904 Melvin L
Doing predictive modeling using R - Rattle (Togaware)
 
02:11:19
This session covers equivalent of all SAS procedures using free software - R Rattle. Hypothesis testing, Linear and Logistic regression, Cluster Analysis. Introduction to Random Forests, SVM, Boosting etc. www.learnanalytics.in
Views: 26234 Learn Analytics
Rattle for Data Mining - Using R without programming (CRAN)
 
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www.learnanalytics.in demostrates use of an free and open source platform to build sophisticated predictive models. We demonstrate using R package Rattle to do data analysis without writing a line of r code. We cover hypothesis testing, descriptive statistics, linear and logistic regression with a flavor of machine learning (Random Forest, SVM etc.). Also using graphs such as ROC curves and Area under curves (AUC) to compare various models. To download the dataset and follow on your own follow http://www.learnanalytics.in/datasets/Credit_Scoring.zip
Views: 43552 Learn Analytics
Data Mining with Rattle:Clustering  for beginners
 
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PS: minute 04:06 on commencera PAR !! je me suis trompée :D
Views: 2188 Fatma Karoui
Data Mining Tool:Rattle R GUI
 
23:27
Link to download R Console: https://cran.r-project.org/
Views: 3343 Chandrakala Badaga
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 73912 edureka!
Tationem: Intro to Data Mining
 
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A brief video describing data mining, more from the business intelligence perspective.
Views: 51 Tationem
Logistic Regression: Part 1 ("Data Mining for Business Intelligence")
 
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Part of the MBA-level course "Data Mining for Business Intelligence" offered by Prof Galit Shmueli @ University of Maryland's Smith School of Business and @ Indian School of Business. Logistic regression is introduced in the context of predictive analytics. Related resources: Textbook "Data Mining for Business Intelligence" by Shmueli, Bruce & Patel (http://dataminingbook.com)
Views: 8562 Galit Shmueli
Rattle Tutorial - How to Open The Sample Weather Dataset in Rattle
 
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This is a quick tutorial on how to open the sample weather.csv dataset in Rattle. This weather dataset is very helpful in learning basic R and Data Mining concepts from books and guides etc. If you don't have rattle make sure you get it by following the official set-up guide here: http://rattle.togaware.com/rattle-install-mac.html (For Mac) Please drop a comment if you want more tutorial in R, Rattle or Data mining and the required area.
Views: 1642 Spellogram
Optimal Decision Tree with Rattle
 
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Learn an easy way to build a decision tree with Rattle
KEEL Data mining tool demo
 
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KEEL Data minig tool Demo of installation and Working
Views: 4144 Manukumar K J
Introduction to R for Data Mining
 
01:00:34
Introduction to R for Data Mining
Views: 156 Timothy Kipkirui
Datenanalyse/Data Mining mit Rattle
 
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Datenanalyse mit Rattle. Rattle ist eine R-Erweiterung und bietet eine Oberfläche zur Datenanalyse/Data Mining an. Dieses Video basiert auf dem Buch "Data Mining with Rattle and R" (http://www.r-statistik.de/Literatur/literatur.html#Rattle). Zur Verfügung gestellt von Günter Faes (http://www.faes.de/) über das Ad-Oculos-Projekt (http://ad-oculos.faes.de/).
Views: 934 r-statistik
LASI14 Workshop: Introduction to Data Mining for Educational Researchers pt1 June 30, 2014
 
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Learning Analytics Summer Institute (LASI 2014) June 30, 2014 Workshop: Introduction to Data Mining for Educational Researchers, pt1 Christopher Brooks (University of Michigan), Zach Pardos (UC Berkeley), Vitomir Kovanovic (Simon Fraser University), Srecko Joksimovic (Simon Fraser University) http://solaresearch.org/conferences/lasi/lasi2014/lasi-2014-program-monday/ https://sites.google.com/a/umich.edu/lak-2014-tutorial-introduction-to-data-mining-for-educational-researchers/lasi-2014
Data Mining and Benford's Law analysis in R with Rattle package
 
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Distribution analysis of first significant digits in data to discover suspected value in accounting process.
Views: 1540 Giuseppe Caferra
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Introduction to Cluster Analysis with R - an Example
 
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Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 108247 Bharatendra Rai
Learning Data Mining with R : Example – Using a Single Line of Code in R | 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/2lXhDAx]. The aim of this video is to show how powerful R is as a data language. We will query an internal example dataset and show how it can be filtered and aggregated on. • Learn about the structure of the internal mtcars dataset • Filter on the dataset • Aggregate on the dataset 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: 922 Packt Video
Learning Data Mining with R : The Course Overview | packtpub.com
 
03:37
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/2lXhDAx]. This video gives an overview of the entire course. 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: 339 Packt Video
Rattle R Gui  Tool Bar
 
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Rattle is a Gui written as a data mining and training tool for the R statistical programming language. Rattle is used by government departments, not for profits, and within the business community. Rattle is an open source project, and is free available from http://www.togaware.com . Rattle is currently used in business, scientific, law enforcement, defense and environmental areas.
Views: 3770 OZg3n1u5
Statistical Aspects of Data Mining (Stats 202) Day 12
 
53:06
Google Tech Talks August 7, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 10986 GoogleTechTalks
Statistical Aspects of Data Mining (Stats 202) Day 8
 
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Google Tech Talks July 20, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 17981 GoogleTechTalks
Data Mining with R & RStudio - KMeans Clustering and Visualization
 
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Simple overview of data mining with R and RStudio.
Views: 3258 Gaurav Jetley
Togaware Rattle -- Quick Intro
 
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A one minute trip through Togaware Rattle
Views: 235 Math4IQB
Rattle R Gui  Correlation
 
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Views: 4009 OZg3n1u5
Manipulating Data in R Part 2 and Data Visualization: Winter R Institute
 
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Discussion of Manipulating Data in R Part 2 and Data Visualizationin the Winter Institute 2019 for JHSPH.
Views: 72 John Muschelli
R and data mining
 
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Short demo for using R in data mining.
Views: 170 Li Wang
Statistical Aspects of Data Mining (Stats 202) Day 11
 
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Google Tech Talks August 3, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 15552 GoogleTechTalks
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcelR
 
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ExcelR Data Mining Tutorial for Beginners 2018 - Introduction to Data mining using R language. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
support vector machine (SVM) - Rattle R language Machine learning
 
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Predicting Customer churn in telecom industry using a machine learning algorithm called support vector machine (SVM) in R programming language using GUI called Rattle. 5:53 skip intro for support vector machines. 7:12 customer churn Data info 12:14 R, R studio , Rattle Quick install info
Views: 669 Brandon Macaulay
Statistical Aspects of Data Mining (Stats 202) Day 9
 
34:03
Google Tech Talks July 24, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 12738 GoogleTechTalks
Data mining algorithms with SQL Server and R part 1   Dejan Sarka HD
 
01:32:06
Breakout session from DevWeek 2015 http://devweek.com/ DevWeek is the UK’s leading conference for professional software developers, architects and analysts. With insights on the latest technologies, best practice and frameworks from industry-leading experts, plus hands-on workshop sessions, DevWeek is your chance to sharpen your skills - and ensure every member of your team is up to date. Please visit http://devweek.com/ for information on the latest event. ----------------------------------------­----------------------------------------­----- DevWeek is part of DevWeek Events, a series of software development conferences and workshops, including DevWeek's sister conference 'Software Architect' (http://software-architect.co.uk/), brought to you by Publicis UK. ----------------------------------------­----------------------------------------­-----
Views: 575 DevWeek Events
Google Analytics Data Mining with R (includes 3 Real Applications)
 
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R is already a Swiss army knife for data analysis largely due its 6000 libraries but until now it lacked an interface to the Google Analytics API. The release of RGoogleAnalytics library solves this problem. What this means is that digital analysts can now fully use the analytical capabilities of R to fully explore their Google Analytics Data. In this webinar, Andy Granowitz, ‎Developer Advocate (Google Analytics) & Kushan Shah, Contributor & maintainer of RGoogleAnalytics Library will show you how to use R for Google Analytics data mining & generate some great insights. Useful Resources:http://bit.ly/r-googleanalytics-resources
Views: 30171 Tatvic Analytics
Web Data Mining com R
 
01:15:35
Web Data Mining com R
Views: 1471 Antonio Correa
Business Analytics with R | Stages of Analytics | Data Mining | What is R | Edureka
 
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Watch Sample Class recording: http://www.edureka.co/r-for-analytics?utm_source=Youtube&utm_medium=referral&utm_campaign=bar-stages-analytics Business Analytics with R is designed for those with a keep interest towards analytics that can be implemented in multiple industrial domains and scenarios. Topics covered in the Webinar: 1. What is Business Analytics? 2. Who Uses R and How? 3. What is R? 4. Why to Use R? 5. R Products 6. Job Trends in R 7. Get Started with R 8. Use Case Implementation Related Posts: http://www.edureka.co/blog/why-learn-r/?utm_source=youtube&utm_medium=referral&utm_campaign=bar-stages-analytics Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. For more information, please write back to us at [email protected] Call us at US : 1800 275 9730 (toll free) or India : +91-8880862004
Views: 3628 edureka!
Advanced Data Mining with Weka (2.2: Weka’s MOA package)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Weka’s MOA package http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3052 WekaMOOC
R Programming, Data Mining
 
01:13:15
R Programming, Data Mining
Views: 527 ScholarsPro
data mining with DAMEWARE - PART 1 - Intro and Registration
 
05:39
data mining web application on massive data sets
Views: 346 DAMEmedia
Introduction to Data Mining: Euclidean Distance & Cosine Similarity
 
04:51
In this Data Mining Fundamentals tutorial, we continue our introduction to similarity and dissimilarity by discussing euclidean distance and cosine similarity. We will show you how to calculate the euclidean distance and construct a distance matrix. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M8m0 See what our past attendees are saying here: https://hubs.ly/H0f8Lts0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 24211 Data Science Dojo
Text Mining - Part I
 
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Tutorial sobre Mineração de Dados (Data Mining) utilizando o software WEKA. Acesso http://mineracaodedados.wordpress.com o maior site sobre Data Mining do Brasil.
Views: 10454 Flávio Clésio
Introduction to Data Science with R Data Analysis Part 2
 
01:23:43
Part 2 in a in-depth hands-on series of videos introducing the viewer to Data Science using R. The video series illustrates the complete Data Mining project . Part 3 in a in-depth hands-on series of videos introducing the viewer to Data Science using R. The video series illustrates the complete Data Mining project . Part 1 in a in-depth hands-on series of videos introducing the viewer to Data Science using R. The video series illustrates the complete Data Mining project .
Views: 28 Adam Bigelow

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