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Advantages of Data Mining in Different Fields
 
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Advantages of Data Mining in Different Fields
Data Mining | Techniques | Benefits  | Disadvantages |  Challenges in Implementing DATA MINE
 
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SDMCET Anurag Devoor (2SD15CS016) Under Guidance of Prof. Yashoda S
Views: 15 Anurag Devoor
Data Mining (Introduction for Business Students)
 
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This short revision video introduces the concept of data mining. Data mining is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records or dependencies. There are many potential business benefits from effective data mining, including: Identifying previously unseen relationships between business data sets Better predicting future trends & behaviours Extract commercial (e.g. performance insights) from big data sets Generating actionable strategies built on data insights (e.g. positioning and targeting for market segments) Data mining is a particularly powerful series of techniques to support marketing competitiveness. Examples include: Sales forecasting: analysing when customers bought to predict when they will buy again Database marketing: examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles Market segmentation: a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation or gender E-commerce basket analysis: using mined data to predict future customer behavior by past performance, including purchases and preferences
Views: 5182 tutor2u
Benefits of Data Mining  - Olu Campbell
 
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Learn about the benefits and latest updates of the data mining and its classification by Olu Campbell.
Views: 402 Olu Campbell
Advantages of Data mining in Data science
 
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In this article, we will learn the profits of the data. As was in our original blog covering all mining issues. So blog understands the importance of information about computer use by getting a variety of software for mining. https://www.besanttechnologies.com/training-courses/data-warehousing-training/datascience-training-institute-in-chennai https://www.besanttechnologies.com/training-courses/data-science-training-in-bangalore https://www.besanttechnologies.com/data-science-training-in-kalyan-nagar http://www.besanttechnologies.in/data-science-training-in-kalyan-nagar.html https://www.gangboard.com/big-data-training/data-science-training http://www.trainingpune.in/data-science-training-in-pune.html
Views: 198 Nila shri
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #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: 257111 Last moment tuitions
Data Science for Business: Data Mining Process and CRISP DM
 
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This lesson provides an introduction to the data mining process with a focus on CRISP-DM. This video was created by Cognitir (formerly Import Classes). Cognitir is a global company that provides live training courses to business & finance professionals globally to help them acquire in-demand tech skills. For additional free resources and information about training courses, please visit: www.cognitir.com
Views: 16052 Cognitir
data mining techniques in support of science data stewardship
 
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Subscribe today and give the gift of knowledge to yourself or a friend data mining techniques in support of science data stewardship Data Mining Techniques in Support of Science Data Stewardship. Eric A. Kihn , M. Zhizhin NOAA/NGDC RAS/CGDS. Presentation outline. I. Background for the talk II. What is science data stewardship? III. What is data mining? IV. Techniques for SDS IV. Conclusions. Slideshow 3090551 by cody show1 : Data mining techniques in support of science data stewardship show2 : Presentation outline show3 : Motivation for this presentation show4 : What is being presented show5 : Nature june 10 1999 show6 : Ph d s and networked data show7 : Data mining techniques in support of science data stewardship show8 : Ngdc holdings mbytes by data type show9 : What is science data stewardship show10 : Why the emphasis on data mining now answer layers of data archives show11 : Levels of information analysis show12 : Data mining techniques in support of science data stewardship show13 : What is data mining show14 : Definition of data mining show15 : Application to environmental data show16 : Categories of knowledge tools show17 : Why fuzzy logic show18 : Fuzzy logic show19 : Definition of a fuzzy set show20 : Fuzzy logic1 show21 : Data mining techniques in support of science data stewardship show22 : List of events show23 : What is fuzzy clustering show24 : Types of fuzzy cluster algorithms show25 : Mountain fuzzy clustering algorithm show26 : 2d density mountains show27 : 2d mountain clustering show28 : Mountain fuzzy clustering show29 : Subtractive clustering show30 : Subtractive clustering advantages show31 : Techniques for sds
Views: 7 slideshow this
Introduction to Data Mining: Histograms & Box Plots
 
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In this Data Mining Fundamentals tutorial, we discuss different visualization techniques, starting with the most popular: histograms and box plots. We discuss the unique benefits of both, and provide examples of when you can use each for your data exploration and visualization. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCszg0 Watch the latest video tutorials here: https://hubs.ly/H0hCszm0 See what our past attendees are saying here: https://hubs.ly/H0hCsP00 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- 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: 4312 Data Science Dojo
BADM 1.1: Data Mining Applications
 
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This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 3396 Galit Shmueli
DATA MINING EXPLAINED IN HINDI | "ITNA SARA DATA??"
 
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नमस्कार दोस्तों,आज की वीडियो में में आप सभी को DATA MINING के बारे में बताने जा रहा हूँ की आखिर DATA MINING क्या होती है और क्या ये हमारे किसी काम आती हैं या नहीं और आखिर हमारे ज़िन्दगी में इसकी कितनी जरुरत है। आशा करता हूँ आपको ये वीडियो पसंद आएगी अगर आपको वीडियो पसंद आये तो वीडियो को LIKE SHARE और चैनल को SUBSCRIBE जरूर से करे। धन्यवाद। जय हिन्द वन्दे मातरम subscribe our channel on youtube: https://www.youtube.com/channel/UCR_kAPwG59SxWRaUfzk3qoQ facebook: https://www.facebook.com/dropouttechnical/ twitter: https://twitter.com/dropoutechnical google+: https://plus.google.com/u/0/103031877017890269380 -~-~~-~~~-~~-~- Please watch: "MOTO X4 my opinion |"Best phone??"|"worth buy at 21000"🔥" https://www.youtube.com/watch?v=r54C6_667uU -~-~~-~~~-~~-~-
Views: 14649 Dropout Technical
DWM -  Benefits and Users of Data Warehouse
 
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In this video, we cover the following topics- 1. Benefits of Data Warehouse 2. Users of Data Warehouse Link of previous video- https://youtu.be/556RfSpK5Qk Tutorial lecture by Anisha Lalwani
Views: 1187 topNotch Tutorials
Introduction to Data Mining: Data Aggregation
 
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In this Data Mining Fundamentals tutorial, we discuss our first data cleaning strategy, data aggregation. Aggregation is combining two or more attributes (or objects) into a single attribute (or object). -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCnj10 Watch the latest video tutorials here: https://hubs.ly/H0hCnHV0 See what our past attendees are saying here: https://hubs.ly/H0hCnj40 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- 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: 10947 Data Science Dojo
Analyzing the Software Development Life-Cycle using Data-Mining Techniques
 
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by Andreas Platschek At: FOSDEM 2017 One of the major challenges for certification in the SIL2LinuxMP project, isto show that Linux does not only define a development process, but alsofollows it. To this end (and far beyond!) the meta-data of commits to theLinux kernel are analyzed. The talk covers everything from gathering the data, to distributing it toevery one in the project while keeping it the data up-to-date and of courseour first analysis results. Each of these phases contain their own set ofproblems that needed to be considered, leading to a framework called DLCDM(Development Life-Cylce Data-Mining) that is introduced for the first timeduring this talk. One of the major challenges for certification in the SIL2LinuxMP project, isto show that Linux does not only define a development process, but alsofollows it. To this end (and far beyond!) the meta-data of commits to theLinux kernel are analyzed. There are several intended outputs we hope to getout of this analysis, some examples are: - Competence of persons involved (IEC 61508-1, 6.2.13/6.2.14) - Dependencies amongst developers (Independence of persons doing code reviews) - Identify patches that did not get enough review (based on patch complexity, experience of author, reviews, etc.) - Automatic notification of patches in our configuration - Bug analysis (based on Fixes: tag) - Subsystem dependencies and conflicts The talk covers everything from gathering the data, to distributing it toevery one in the project while keeping it the data up-to-date and of courseour first analysis results. Each of these phases contain their own set ofproblems that needed to be considered, leading to a framework called DLCDM(Development Life-Cylce Data-Mining) that is introduced for the first timeduring this talk. Room: UD2.120 (Chavanne) Scheduled start: 2017-02-04 12:00:00
Views: 233 FOSDEM
What is Data Mining?
 
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NJIT School of Management professor Stephan P Kudyba describes what data mining is and how it is being used in the business world.
Views: 428617 YouTube NJIT
Benefits of Data Mining
 
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In this Presentation, we are trying to describe that What are the major benefits and advantages of Data Mining. Click Here: https://bit.ly/2YqyZcB Follow us on Twitter: https://twitter.com/MaxbpoO Follow us on Pinterest: https://www.pinterest.com/maxbpoo/ Related Terms: data mining companies benefits of data mining services data mining services USA data mining service providers in India data entry services data entry outsourcing data processing & outsourced services
Views: 29 MAX BPO
Data Mining for beginners and pros
 
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heyaaa bros dis iz my first video of the channel plzzzz like and subscribe its all about data mining
Views: 57 DeTechBros
Power of Data Mining
 
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Synopsis of the sources for data mining advantages and disadvantages, powerpoint for marketing for New Mexico Highlands University
Views: 134 Karen Boggs
THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING
 
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Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html ********************************************* Computer Applications: An International Journal (CAIJ), Vol.4, No.1/2/3/4, November 2017 DOI:10.5121/caij.2017.4401 THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING Yuvika Priyadarshini Researcher, Jharkhand Rai University, Ranchi. ABSTRACT The aim of this study is to identify the extent of Data mining activities that are practiced by banks, Data mining is the ability to link structured and unstructured information with the changing rules by which people apply it. It is not a technology, but a solution that applies information technologies. Currently several industries including like banking, finance, retail, insurance, publicity, database marketing, sales predict, etc are Data Mining tools for Customer . Leading banks are using Data Mining tools for customer segmentation and benefit, credit scoring and approval, predicting payment lapse, marketing, detecting illegal transactions, etc. The Banking is realizing that it is possible to gain competitive advantage deploy data mining. This article provides the effectiveness of Data mining technique in organized Banking. It also discusses standard tasks involved in data mining; evaluate various data mining applications in different sectors KEYWORDS Definition of Data Mining and its task, Effectiveness of Data Mining Technique, Application of Data Mining in Banking, Global Banking Industry Trends, Effective Data Mining Component and Capabilities, Data Mining Strategy, Benefit of Data Mining Program in Banking
Views: 56 aircc journal
Data warehouse and Data mining Important for Comps Mumbai university
 
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Thanks alot to atharva for making this video
Views: 6466 Last moment tuitions
L3: Data Warehousing and Data Mining |Characteristics | Advantage | Evolution of Database Technology
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L2: Data Warehousing and Data Mining |Characteristics | Advantage |Evolution of Database Technology Namaskar, In Today's lecture, i will cover Characteristics, Advantage, Evaluation of Database Technology of subject Data Warehousing and Data Mining which is one of the important subjects of computer science and engineering I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 558 University Academy
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#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: 325522 Last moment tuitions
DNAlytics - Data mining service for personalized medicine
 
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http://www.dnalytics.com - DNAlytics aims at being a reference European partner for the analytical / computational needs of the healthcare industry in the field of personalized medicine, helping patients benefit from it. In order to materialize this vision, DNAlytics exploits its expertise in Data Mining / Machine Learning, Statistics, Intensive Computing and Web technologies.
Views: 4031 Thibault Helleputte
What Is Data Warehousing? | Functions | Types | Advantages | e-Commerce
 
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What Is Data Warehousing? | Functions | Types | Advantages | e-Commerce The primary concept of data warehousing is that the data stored for business analysis can most effectively be accessed by separating it from the data in the operational systems. A data warehouse is a collection of computer-based information that is critical to successful execution of enterprise initiatives. A data warehouse is more than an archive for corporate data and more than a new way of accessing corporate data. A data warehouse is a subject-oriented repository designed with enterprise-wide access in mind. It provides tools to satisfy the information needs of the employees organizational levels-not just for complex data queries, but as general facility for getting quick, accurate and often insightful information. A data warehouse is designed so that its users can recognize the information they want and access that information using simple tools. Functions:- Allow existing transactions and legacy systems to continue in operation. Consolidates data from various transaction systems into a coherent set. Allows analysis of virtual information about current operations of decision support. Types:- Physical data warehouse:- It is the actual physical database into which all the corporate data for the data warehouse are gathered along with the schemas and the processing logics. Logical data warehouse:- It contains all the metadata, business rules and processing logics required to scrub, organize, package and pre-process the data. It also contains information required to find and access the actual data, wherever it actually resides. Data library:- This is sub set of the enterprise wide data warehouse and performs the role of a departmental, regional or functional data warehouse. As part of the data warehouse process, the organization builds a series of data libraries over time and eventually links them via an enterprise wide logical data warehouse. ADVANTAGES OF DATA WAREHOUSE:- More cost effective decision making Better enterprise intelligence Enhanced customer service Business reengineering Information systems reengineering #BikkiMahato The best part is: it is all completely free! ------------------------------------------------------------------------------ Follow :) Youtube: https://www.youtube.com/c/BikkiMahato Facebook: https://www.facebook.com/mahatobikki Facebook Page:https://www.facebook.com/youtubebikki Twitter:https://twitter.com/mahato_bikki Instagram:https://www.instagram.com/bikkimahato Google+:https://plus.google.com/u/0/+BikkiMahato Blogger:https://bikkimahato.blogspot.in Pinterest:https://in.pinterest.com/bikkimahato123/ LinkedIn:https://www.linkedin.com/in/bikkimahato ------------------------------------------------------------------------------ Support :) Tez : 8100147475 Paytm : 8100147475 PhonePe : 8100147475 Patreon : https://www.patreon.com/bikkimahato Instamojo : https://www.instamojo.com/@bikkimahato Paypal : https://www.paypal.me/bikkimahato ------------------------------------------------------------------------------ Send me letters! 30/2/C Dharmatala Road Belur-Howrah-711202 West Bengal,India
Views: 509 Bikki Mahato
Meta S. Brown (Keynote): CRISP-DM; The dominant process for data mining
 
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CRISP-DM stands for Cross-Industry Standard Process for Data Mining, is an industry-proven way to guide your data mining efforts. This talk covers this dominant process, what it is, how it is developed, where it is today and why it's time for you to get involved.
Views: 5710 PyData
Data Mining Using R: Introduction to Data Mining Techniques | Machine Learning - ExcelR
 
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ExcelR Data Mining Tutorial for Beginners 2018 - Introduction to various Data mining unsupervised techniques namely Clustering, Dimension Reduction, Association Rules, Recommender System or Collaborative filtering, Network Analytics. Things you will learn in this video 1)What is DataMining 2)DataMining in Nutshell 3)Types of methods 4)DataMining process 5)Approaches 6)Types of Clustering Algorithms To buy eLearning course on DataScience click here https://goo.gl/oMiQMw To enroll for the virtual online course click here https://goo.gl/m4MYd8 To register for classroom training click here https://goo.gl/UyU2ve SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For Introduction to Clustering Analysis clicks here https://goo.gl/wuXN48 For Introduction to K-mean clustering click here https://goo.gl/PYqXRJ #ExcelRSolutions #DataMining#clusteringTechniques #datascience #datasciencetutorial #datascienceforbeginners #datasciencecourse ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
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
The Data-Mining Revolution: MUM prepares students for the skills and jobs of the future
 
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http://www.mum.edu Prof. Anil Maheshwari, Ph.D., discusses the new immersion program Maharishi University of Management has just launched to train students in the next wave of data-mining software. In today's data-driven economy there is an urgent need for more sophisticated software programs to mine and better utilize data coming in over multiple platforms from diverse sectors of the economy, not only for business, but also for higher education. To help Maharishi University of Management students build essential skills in analytics technology, we recently joined the IBM Academic Initiative, which offers participating schools no-charge access to IBM software, discounted hardware, course materials, training and curriculum development—over 6,000 universities and 30,000 faculty members worldwide are members of the program. "We are using industrial strength tools such as IBM SPSS Modeler," Dr. Maheshwari said, "along with open-source tools, to provide our students a strong data-mining toolkit to engage with Big Data, and generate interesting insights and new knowledge." Students will learn more than just how to operate the software, but how to use it effectively as a business tool. Dr. Maheshwari said, "Our students will have end-to-end skills to discern what is the business problem, what is the data being generated, how do I mine the data, how do I generate intelligence out of it and feed it back to the business so the business can actually benefit from it. That whole cycle is what we're training, not just the tool itself." Industry analysts have identified predictive analytics as the fastest growing software category for company spending. They also expect that the need for staff with these capabilities will outpace available skill sets in many organizations. This will mean that expertise in data mining and predictive analytics will be highly sought after for years to come. "Having this kind of software suite on their resumes can be a big advantage for our students headed for IT/management jobs," said Dr. Maheshwari. For more videos about MUM, visit our Video Café: http://www.mum.edu/video-cafe At MUM, Consciousness-Based education connects everything you learn to the underlying wholeness of life. So each class becomes relevant, because the knowledge of that subject is connected with your own inner intelligence. You study traditional subjects, but you also systematically cultivate your inner potential developing your creativity and learning ability. Your awareness expands, improving your ability to see the big picture, and to relate to others. Maharishi University of Management (MUM) offers undergraduate and graduate degree programs in the arts, sciences, business, and humanities. The University is accredited through the doctoral level by the Higher Learning Commission. Founded in 1971 by Maharishi Mahesh Yogi, the University features Consciousness-Based education to develop students' inner potential. All students and faculty practice the Transcendental Meditation technique, which extensive published research has found boosts learning ability, improves brain functioning, and reduces stress. Maharishi University uses the block system in which each student takes one course at a time. Students report they learn more without the stress of taking 4-5 courses at once. The University has a strong focus on sustainability and natural health, and serves organic vegetarian meals. The B.S. in Sustainable Living is MUM's most popular undergraduate major. Maharishi University of Management: http://www.mum.edu Consciousness-Based education: http://www.mum.edu/cbe BS Sustainable Living: http://www.mum.edu/sustainable_living/ Transcendental Meditation: http://www.mum.edu/tm Research: http://www.mum.edu/tm_research Block system: http://www.mum.edu/cbe/block Sustainability: http://www.mum.edu/sustainability Natural health: http://www.mum.edu/cbe/natural_health Organic veg meals: http://www.mum.edu/campus/dining
Sampling & its 8 Types: Research Methodology
 
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Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm types of sampling types of sampling pdf probability sampling types of sampling in hindi random sampling cluster sampling non probability sampling systematic sampling
Views: 384732 Examrace
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn
 
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This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this video, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes video are as follows: 1. What is Naive Bayes? ( 01:06 ) 2. Naive Bayes and Machine Learning ( 05:45 ) 3. Why do we need Naive Bayes? ( 05:46 ) 4. Understanding Naive Bayes Classifier ( 06:30 ) 5. Advantages of Naive Bayes Classifier ( 20:17 ) 6. Demo - Text Classification using Naive Bayes ( 22:36 ) To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/Cw9wqy #NaiveBayes #MachineLearningAlgorithms #DataScienceCourse #DataScience #SimplilearnMachineLearning - - - - - - - - Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Naive-Bayes-Classifier-l3dZ6ZNFjo0&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 45241 Simplilearn
Enhanced Resource Allocation: Business Use of Predictive Analytics and Data Mining
 
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Visit http://tdwi.org for more information on business intelligence and data warehousing training and education. TDWI Boston 2014 Keynote: Enhanced Resource Allocation: Business Use of Predictive Analytics and Data Mining Tony Rathburn Senior Consultant & Training Director The Modeling Agency StarSoft Solutions, Inc. Advanced technology has been a cultural obsession over the past few decades as business and government have invested heavily in pursuit of competitive advantage. The exponential growth in data repositories combined with advances in analytic techniques have left many organizations searching for the opportunities that justify these investments. Predictive analytics expert and author Tony Rathburn explores a business-driven perspective on using analytics that offers measurable organizational benefits, rapid implementation potential, minimal new investments, and lowrisk implementation strategies that can have near-immediate impact on virtually all organizations.
Views: 537 TDWI
Data Mining: Exploring the Ethical Dilemmas
 
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This event focused on researchers who employ data mining techniques in their work. In this thematic context we aim to better understand the cross-disciplinary practice of data mining and its associated implications, such as privacy issues, ethics and the interplay with open data. PhD students as well as early career and experienced researchers from around the UK came together to explore how they manage data that they have created when undertaking mining projects, and a panel session helped to identify key questions that researchers face when encountering these implications. For more details on the event, visit www.ses.ac.uk/2018/07/17/data-mining
Part 14  Techniques for Working with Traditional Methods
 
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Techniques for Working with Traditional Methods, Various Data Science Disciplines, Field of Data Science, data scientist, Statistics, Data Mining, Predictive analytics, Data Science, Business Analytics., data analytics, data science, business intelligence., machine learning, Data Science Disciplines, Connecting the Data Science Disciplines, Benefits of Data Science, Popular Data Science Techniques, Popular Data Science Tools, Careers in Data Science,
Big Data Analytics in Hindi | big data analytics tutorial
 
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Big Data Analytics in Hindi | big data analytics tutorial Welcome to this video of big data analytics. This video of big data analytics will explain you the concept of big data, sources of big data and big data analytics, what are the benefits of big data analytics. We are doing online transactions; we are using debit cards, credit cards, atm cards. In all this we are creating data. We are into online shopping, we are in to online selling. This also creates big data. Universities are offering online courses, online examinations ,all these transactions are creating big data. We are into online booking of tickets, online hotel booking. We are using social media like face book, twitter and other social media sources. We are sharing pics , videos and text messages there, what all is that. That is data, we can say big data. Traffic monitoring, aircraft monitoring etc , all these things are creating big data. After watching this video viewers will be able to know about various sources of big data. This video is produced in Hindi; so that it can be easily understood by all the person's who like to learn the concept of big data in Hindi. In big data analytics, we can have idea of customer behavior when they buy online from websites. While selling something on a online selling website, with the help of big data analytics, we can have idea about the type and price of the product that mostly customer buy online. What is the use of selling something that no one buys. Also Visit our website http://www.ethtimes.com Our facebook page http://www.facebook.com/learningseveryday Please watch full video for complete concept of big data analytic s. -~-~~-~~~-~~-~- Please watch: "PRIME Number Program in C Language in Hindi video" https://www.youtube.com/watch?v=0daznihE3Xk -~-~~-~~~-~~-~-
Views: 24918 Learning Everyday
Decision Tree Solved | Id3 Algorithm (concept and numerical) | Machine Learning (2019)
 
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Decision Tree is a supervised learning method used for classification and regression. It is a tree which helps us by assisting us in decision-making! Decision tree builds classification or regression models in the form of a tree structure. It breaks down a data set into smaller and smaller subsets and simultaneously decision tree is incrementally developed. The final tree is a tree with decision nodes and leaf nodes. A decision node has two or more branches. Leaf node represents a classification or decision. We cannot do more split on leaf nodes. The topmost decision node in a tree which corresponds to the best predictor called root node. Decision trees can handle both categorical and numerical data. #codewrestling #decisiontree #machinelearning #id3 Common terms used with Decision trees: Root Node: It represents entire population or sample and this further gets divided into two or more homogeneous sets. Splitting: It is a process of dividing a node into two or more sub-nodes. Decision Node: When a sub-node splits into further sub-nodes, then it is called decision node. Leaf/ Terminal Node: Nodes do not split is called Leaf or Terminal node. Pruning: When we remove sub-nodes of a decision node, this process is called pruning. You can say opposite process of splitting. Branch / Sub-Tree: A sub section of entire tree is called branch or sub-tree. Parent and Child Node: A node, which is divided into sub-nodes is called parent node of sub-nodes whereas sub-nodes are the child of parent node. How does Decision Tree works ? Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables. Advantages of Decision Tree: 1. Easy to Understand: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Its graphical representation is very intuitive and users can easily relate their hypothesis. 2. Useful in Data exploration: Decision tree is one of the fastest way to identify most significant variables and relation between two or more variables. With the help of decision trees, we can create new variables / features that has better power to predict target variable. It can also be used in data exploration stage. For e.g., we are working on a problem where we have information available in hundreds of variables, there decision tree will help to identify most significant variable. 3 Decision trees implicitly perform variable screening or feature selection. 4. Decision trees require relatively little effort from users for data preparation. 5. Less data cleaning required: It requires less data cleaning compared to some other modeling techniques. It is not influenced by outliers and missing values to a fair degree. 6. Data type is not a constraint: It can handle both numerical and categorical variables. Can also handle multi-output problems. ID3 Algorithm Key Factors: Entropy- It is the measure of randomness or ‘impurity’ in the dataset. Information Gain: It is the measure of decrease in entropy after the dataset is split. Ask me A Question: [email protected] Music: https://www.bensound.com For Decision Trees slides comment below 😀
Views: 1656 Code Wrestling
What is machine learning and how to learn it ?
 
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http://www.LearnCodeOnline.in Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com
Views: 828818 Hitesh Choudhary
Decision Tree Important Points ll Machine Learning ll DMW ll Data Analytics ll Explained in Hindi
 
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Decision Tree Explained with Example https://youtu.be/RVuy1ezN_qA 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 37886 5 Minutes Engineering
What is Business Intelligence (BI)?
 
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There are many definitions for Business Intelligence, or BI. To put it simply, BI is about delivering relevant and reliable information to the right people at the right time with the goal of achieving better decisions faster. If you wanna have efficient access to accurate, understandable and actionable information on demand, then BI might be right for your organization. For more information, contact Hitachi Solutions Canada (canada.hitachi-solutions.com).
Views: 397741 Hitachi Solutions Canada
Data Reduction Techniques:Theory & Practice I
 
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This session will give the introductory information on reduction techniques and introduce one of the well known applications of such known as data deduplication. Data deduplication and other methods of reducing storage consumption play a vital role in affordably managing today’s explosive growth of data. Optimizing the use of storage is part of a broader strategy to provide an efficient information infrastructure that is responsive to dynamic business requirements. This presentation will explore the significance of deduplication from both the theoretical and practiocal aspects related to specific capacity optimization techniques within the context of information lifecycle management. The benefits of optimizing storage capacity span cost savings, risk reduction, and process improvement. Capital expenditures on networked storage equipment and floor space can be reduced or deferred. Ongoing operating expenses for power, cooling, and labor can also be reduced because there is less equipment to operate and manage. Increasing the efficiency and effectiveness of their storage environments helps companies remove constraints on data growth, improve their service levels, and better leverage the increasing quantity and variety of data to improve their competitiveness.
Views: 3176 Şuayb Ş. Arslan
ONLINE SECURITY Against Data Mining Corporations Like FACEBOOK
 
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Protect Yourself From Spies w. Identisafe and VirtualShield VPN - Click Here Now! http://hidewithjordan.com/ . . Support my work: Patreon: https://goo.gl/qipbjt PayPal: https://goo.gl/wGZbmG Website: http://www.destroyingtheillusion.com (Subscribe to the newsletter to stay in touch!) Social Media: Twitter: @Jordan_Sather_ Facebook: @destroyingtheillusion Instagram: @jaysather Vids also on: Steemit/Dtube: https://goo.gl/quLMKi BitChute: https://goo.gl/mSB8VB DTI Apparel - https://bit.ly/2pPIgeu Donate via Crypto: BitCoin: 1Ce5QjiEqUnaHzAeU8jDR1mX8BdJLgdMZe Ethereum: 0x0B096d467BB4D8B65489a3Fa224FC02Be25227CE LiteCoin: LRKx8dJjV5ZTxtayh1sc6uckTJG7e9XoQD BitcCoin Cash: 15iuUBXL8ZTiYjA8oAkBv37mfnv4jpStzz Thank YOU for watching and supporting!
Data Mining and Business Intelligence for Cyber Security Applications Summer Program at BGU
 
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The purpose of the Summer Program in Data Mining and Business Intelligence is to provide both theoretical and practical knowledge, including tools, on data mining. The program offers two academic courses (each for 3 credits), where students learn the basic tools of data mining and the utilization of machine learning techniques for solving cyber security problems. The program includes a mandatory one week internship at BGU’s Cyber Security Research Center. The internship corresponds with the course materials and contributes the practical experience component. In addition, students will take part in professional fieldtrips to leading companies, in order to enhance their understanding of data mining and cyber security To Apply: https://www.tfaforms.com/399172 For More information: www.bgu.ac.il/global
Views: 1388 BenGurionUniversity
Data Mining
 
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This Lecture talks about Data Mining.
Views: 1709 cec
Driving Business Analytics with Data Mining and Machine Learning
 
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Big Data is disrupting enterprises, and Hadoop is helping companies turn this challenge into opportunity. Explore different techniques that allow you to gain insight into your growing data and turn it into actionable decision making. Join us for an overview of how Big Data and Analytics work together, as well as the concepts of Machine Learning with Mahout. We will cover principles needed to drill down into your data through Data Mining, focusing on various techniques such as: Grouping / Clustering; Recommendation Systems; Prediction Modeling. Learn what these techniques are and how they can help you generate value for your organization. www.metascale.com #metascale
Views: 1304 MetaScale
Data Mining
 
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Views: 38933 Azis Ikwanto
Part 8  Techniques for Working with Traditional Data
 
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Techniques for Working with Traditional Data Techniques for Working with Traditional Data, Various Data Science Disciplines, Field of Data Science, data scientist, Statistics, Data Mining, Predictive analytics, Data Science, Business Analytics., data analytics, data science, business intelligence., machine learning, Data Science Disciplines, Connecting the Data Science Disciplines, Benefits of Data Science, Popular Data Science Techniques, Popular Data Science Tools, Careers in Data Science,
Data Mining Course
 
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https://experfy.com ---- Clustering and Association Rule Mining are two of the most frequently used Data Mining technique for various functional needs, especially in Marketing, Merchandising, and Campaign efforts. Clustering helps find natural and inherent structures amongst the objects, where as Association Rule is a very powerful way to identify interesting relations between objects in large commercial databases. The main motivation for the course is: i) This course specifically touches upon the scenarios where Clustering is necessary, and which Clustering technique is appropriate for which scenario. ii) This course also stresses on advantages as well as practical issues with different Clustering techniques What am I going to get from this course? Learn clustering through examples in R – that you immediately apply in your day-to-day work Over 20 lectures and 5-6 hours of content, plus 2 practice exercises on Clustering and Market Basket Analysis Learn practical Hierarchical, Non-Hierarchical, Density based clustering techniques. Also Association rules and Market Basket Analysis Related Posts: https://www.experfy.com/training/courses/clustering-and-association-rule-mining Follow us on: https://www.facebook.com/experfy https://twitter.com/experfy https://experfy.com
Views: 514 Experfy
Introduction to Data Warehousing and Data Mining
 
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The term "Data Warehousing" is now commonly used in industry. It refers to a kind of heterogeneous information system -- one in which the focus is on gathering together the data from the different operational databases within an organization, and making it available for decision making purposes. This unit explains the differences between the type of information one can obtain from a data warehouse compared with a traditional database. We looked at the problems and steps involved in building a data warehouse, and examine some of the techniques that have been proposed for constructing data warehouses. (Chapter 15)
Views: 45412 vcilt14