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Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 141695 SciShow
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: 397282 YouTube NJIT
Introduction to data mining and architecture  in hindi
 
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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://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [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: 188806 Last moment tuitions
How data mining works
 
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In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 217936 Thales Sehn Körting
Last Minute Tutorials | Data mining | Introduction | Examples
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 38307 Last Minute Tutorials
What is Bitcoin Mining?
 
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For more information: https://www.bitcoinmining.com and https://www.weusecoins.com What is Bitcoin Mining? Have you ever wondered how Bitcoin is generated? This short video is an animated introduction to Bitcoin Mining. Credits: Voice - Chris Rice (www.ricevoice.com) Motion Graphics - Fabian Rühle (www.fabianruehle.de) Music/Sound Design - Christian Barth (www.akkord-arbeiter.de) Andrew Mottl (www.andrewmottl.com)
Views: 6709988 BitcoinMiningCom
Introduction to Data Analysis and Mining: what is it?
 
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Here I give an introduction to the course of data exploration (data analysis) and data mining. I also show an example dataset My web page: www.imperial.ac.uk/people/n.sadawi
Views: 66408 Noureddin Sadawi
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning, definition & explanation
 
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What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 6593 The Audiopedia
How data mining works
 
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Data mining concepts Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[8] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[9] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.Data mining Data mining involves six common classes of tasks: Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation. Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam". Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets. Summarization – providing a more compact representation of the data set, including visualization and report generation.
Views: 394 Technology mart
How Facebook Data Mining, And Your Info, Is Influencing The 2016 Election | TODAY
 
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With the 2016 presidential election only 27 days away, we’re taking a look at how the campaigns are taking to social media in the hopes of trying to win the all-important millennial vote and how data mining on Facebook and other social platforms is influencing your view of the election. NBC News’ Jo Ling Kent reports for TODAY. Red, White and You is brought to you by Amazon. » Subscribe to TODAY: http://on.today.com/SubscribeToTODAY » Watch the latest from TODAY: http://bit.ly/LatestTODAY About: TODAY brings you the latest headlines and expert tips on money, health and parenting. We wake up every morning to give you and your family all you need to start your day. If it matters to you, it matters to us. We are in the people business. Subscribe to our channel for exclusive TODAY archival footage & our original web series. Connect with TODAY Online! Visit TODAY's Website: http://on.today.com/ReadTODAY Find TODAY on Facebook: http://on.today.com/LikeTODAY Follow TODAY on Twitter: http://on.today.com/FollowTODAY Follow TODAY on Google+: http://on.today.com/PlusTODAY Follow TODAY on Instagram: http://on.today.com/InstaTODAY Follow TODAY on Pinterest: http://on.today.com/PinTODAY How Facebook Data Mining, And Your Info, Is Influencing The 2016 Election | TODAY
Views: 5171 TODAY
Data mining tutorial for beginners FREE Training 01
 
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Published on Aug 2, 2014 1 intro data mining and scraping next tutorial here: http://youtu.be/gb4ufqFkT7A please comment below if you have any questions. Tq Category Education License Standard YouTube License
Views: 108922 Red Team Cyber Security
What is Data Mining
 
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Small introduction on Data Mining - What is Data Mining Data Mining is a tool to Extract Hidden data.
Data Mining #Facebook
 
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This is how to mine personal Data from facebook. https://satoshibox.com/3z6dwcvsks5fnxahns5w3z3d facebook,data mining,data,facebook data mining,data mining facebook,data mining for facebook,facebook data mining using r programming,data mining facebook using r,facebook data leak,data mining facebook using curl,data mining facebook using linux,mining data on facebook,data mining for facebook using graph api,mining data on facebook with python,mining facebook posts,facebook api,data science
Views: 3 IT- Guy
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 58804 edureka!
Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Data Mining and Warehousing Part 1 | IBPS SO IT 2018
 
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Hello Friends, This is Sumit Nirala. This is 1st Part of Data Warehousing, in this video we have discussed the architecture of data mining and data ware housing.
Views: 2390 Apni Pathshala
Data Mining IT 5th Session5 "Adomd.Net"
 
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We will create simple "ASP.Net Web Application" that uses "OLAP Project" which was already created in the previous sessions
Views: 1280 Mounir Alyousef
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: 2654 Galit Shmueli
Introduction to data mining it-446 chapter one part a
 
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Introduction to data mining in Arabic
Views: 2408 mmi ibr
BADM 1.2: Data Mining in a Nutshell
 
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What is Data Mining? How is it different from Statistics? 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: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.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 Networks: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 1064 Galit Shmueli
Introduction to Data Mining: Document & Transaction Data
 
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Part two of data types, we discuss document data and transaction data, and how it works in data mining. -- 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/H0f8M0m0 See what our past attendees are saying here: https://hubs.ly/H0f8M0v0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 6113 Data Science Dojo
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: 441177 Brandon Weinberg
Data Mining - Decision tree
 
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Decision tree represents decisions and decision Making. Root Node,Internal Node,Branch Node and leaf Node are the Parts of Decision tree Decision tree is also called Classification tree. Examples & Advantages for decision tree is explained. Data mining,text Mining,information Extraction,Machine Learning and Pattern Recognition are the fileds were decision tree is used. ID3,c4.5,CART,CHAID, MARS are some of the decision tree algorithms. when Decision tree is used for classification task, it is also called classification tree.
#FixCopyright:  Copyright & Research - Text & Data Mining (TDM) Explained
 
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Read our blog post analysing the European Commission's (EC) text and data mining (TDM) exception and providing recommendations on how to improve it: http://bit.ly/2cE60sp Copy (short for Copyright) explains what text and data mining (TDM) is all about, and what hurdles researchers are currently facing. We also have a blog post on the TDM bits in the EC's Impact Assessment accompanying the proposal: http://bit.ly/2du9sYe Read more about the EC's copyright reform proposals in general: http://bit.ly/2cvAh0a
Views: 3194 FixCopyright
IS GPU MINING STILL PROFITABLE? - Mining Adventure Part 1
 
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Graphics card stock has long been tapped out due to cryptocurrency miners, but does what they're doing make any sense? Let's find out. Sign up for Crunchyroll today at https://www.crunchyroll.com/linus Massdrop's AKG K7XX headphones are available now at $199.99 USD for a limited time: http://dro.ps/linusk7xx Buy Graphics Cards! Amazon: http://geni.us/bFc5H Newegg: http://geni.us/b39hZvA Discuss on the forum: https://linustechtips.com/main/topic/858834-is-gpu-mining-still-profitable-mining-adventure-part-1/ Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/75969-linus-tech-tips-affiliates-referral-programs-and-sponsors Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/LinusTechTips/ Linus Tech Tips posters at http://crowdmade.com/linustechtips Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxmFU3lWY iTunes Download Link: https://itunes.apple.com/us/album/supernova/id936805712 Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnirvana Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 2156295 Linus Tech Tips
Facebook data mining: the app developer's defense
 
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Mark Zuckerberg says Aleksandr Kogan is to blame for the Facebook data mining debacle. But Kogan says Facebook's own developer tools made it possible. Subscribe to the "60 Minutes" Channel HERE: http://bit.ly/1S7CLRu Watch Full Episodes of "60 Minutes" HERE: http://cbsn.ws/1Qkjo1F Get more "60 Minutes" from "60 Minutes: Overtime" HERE: http://cbsn.ws/1KG3sdr Relive past episodies and interviews with "60 Rewind" HERE: http://cbsn.ws/1PlZiGI Follow "60 Minutes" on Instagram HERE: http://bit.ly/23Xv8Ry Like "60 Minutes" on Facebook HERE: http://on.fb.me/1Xb1Dao Follow "60 Minutes" on Twitter HERE: http://bit.ly/1KxUsqX Follow "60 Minutes" on Google+ HERE: http://bit.ly/1KxUvmG Get unlimited ad-free viewing of the latest stories plus access to classic 60 Minutes archives, 60 Overtime, and exclusive extras. Subscribe to 60 Minutes All Access HERE: http://cbsn.ws/23XvRSS Get the latest news and best in original reporting from CBS News delivered to your inbox. Subscribe to newsletters HERE: http://cbsn.ws/1RqHw7T Get your news on the go! Download CBS News mobile apps HERE: http://cbsn.ws/1Xb1WC8 Get new episodes of shows you love across devices the next day, stream local news live, and watch full seasons of CBS fan favorites anytime, anywhere with CBS All Access. Try it free! http://bit.ly/1OQA29B --- "60 Minutes," the most successful television broadcast in history. Offering hard-hitting investigative reports, interviews, feature segments and profiles of people in the news, the broadcast began in 1968 and is still a hit, 50 seasons later, regularly making Nielsen's Top 10. "60 Minutes" has won more Emmy Awards than any other primetime broadcast, including a special Lifetime Achievement Emmy. It has also won every major broadcast journalism award over its tenure, including 20 Peabody and 18 DuPont Columbia University awards for excellence in television broadcasting. Other distinguished awards won multiple times include the George Polk, RTNDA Edward R. Murrow, Investigative Reporters and Editors, RFK Journalism, Sigma Delta Chi and Gerald Loeb Awards for Distinguished Business and Financial Reporting. "60 Minutes" premiered on CBS Sept. 24, 1968. Jeff Fager is the program's executive producer. The correspondents and contributors of "60 Minutes" are Bill Whitaker, Steve Kroft, Lara Logan, Scott Pelley, Lesley Stahl, Anderson Cooper, Sharyn Alfonsi, Jon Wertheim, Norah O'Donnell and Oprah Winfrey. "60 Minutes" airs Sundays at 7 p.m. ET/PT. Check your local listings.
Views: 6825 60 Minutes
TBYI: Data Mining
 
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Think Before You Ink is a video mini-series created by the University of British Columbia's Digital Tattoo project that aims to raise awareness among the general public about issues surrounding digital identity and citizenship. Ever wonder how Amazon knew you'd want to buy that slap chop set? Or how Netflix predicted you'd love House of Cards before you even knew about it? Data Mining is the powerful technology behind this predictive magic. To learn more about data mining and how it impacts your daily life, watch the video above! And don't forget to visit our website at www.digitaltattoo.ubc.ca to learn more. Music offered by Syril: Licensed for public use. CC copyright. https://www.youtube.com/watch?v=BArOuD_UBGE
Facebook data mining: Obama did it first | Amanda Head
 
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Amanda Head of TheRebel.media says when it comes to using Facebook to win elections, Team Obama made Trump's campaign look like amateurs... https://www.therebel.media/facebook_data_mining_obama_did_it_first_and_to_a_greater_degree_than_trump Never miss a new Rebel video: http://www.youtube.com/c/RebelMediaTV JOIN http://www.Facebook.com/JoinTheRebel *** http://www.Twitter.com/TheRebelTV
Views: 5025 Rebel Media
What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean?
 
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What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean? EVOLUTIONARY DATA MINING meaning - EVOLUTIONARY DATA MINING definition - EVOLUTIONARY DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms. While it can be used for mining data from DNA sequences, it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value ... of a user-specified goal attribute based on the values of other attributes." For instance, a banking institution might want to predict whether a customer's credit would be "good" or "bad" based on their age, income and current savings. Evolutionary algorithms for data mining work by creating a series of random rules to be checked against a training dataset. The rules which most closely fit the data are selected and are mutated. The process is iterated many times and eventually, a rule will arise that approaches 100% similarity with the training data. This rule is then checked against a test dataset, which was previously invisible to the genetic algorithm. Before databases can be mined for data using evolutionary algorithms, it first has to be cleaned, which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results. If data comes from more than one database, they can be integrated, or combined, at this point. When dealing with large datasets, it might be beneficial to also reduce the amount of data being handled. One common method of data reduction works by getting a normalized sample of data from the database, resulting in much faster, yet statistically equivalent results. At this point, the data is split into two equal but mutually exclusive elements, a test and a training dataset. The training dataset will be used to let rules evolve which match it closely. The test dataset will then either confirm or deny these rules. Evolutionary algorithms work by trying to emulate natural evolution. First, a random series of "rules" are set on the training dataset, which try to generalize the data into formulas. The rules are checked, and the ones that fit the data best are kept, the rules that do not fit the data are discarded. The rules that were kept are then mutated, and multiplied to create new rules. This process iterates as necessary in order to produce a rule that matches the dataset as closely as possible. When this rule is obtained, it is then checked against the test dataset. If the rule still matches the data, then the rule is valid and is kept. If it does not match the data, then it is discarded and the process begins by selecting random rules again.
Views: 141 The Audiopedia
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.
Data Mining Classification - Basic Concepts
 
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Classification in Data Mining with classification algorithms. Explanation on classification algorithm the decision tree technique with Example.
Obama's win: data mining
 
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Brian Todd reports on the Obama campaign's successful data-mining efforts to target potential voters.
Views: 8293 CNN
Data Mining IT 5th Session3 (Creating OLAP Cube)
 
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Session3: ( Creating OLAP Cube Depending on Existing Data Source "Customers DataBase" ) includes the Following Steps: 1# Creating new Data Source 2# Creating new Data Source View 3# Determining cube's Dimensions (and Creating Custom Hierarchies) 4# Creating the Cube
Views: 591 Mounir Alyousef
Betfair trading strategies - How to approach Data Mining
 
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It's great looking at historical data, but it can be misleading. After having a conversation this week with somebody about the positive and negative aspects of data mining, I thought I'd give you my views on the subject.
Views: 4306 betangeltv
Ninja Tracks - Data Mining
 
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Album: Revolution Dominion Composed By: Kaveh Cohen, Michael Nielsen Official: http://ninjatracks.com/ Soundcloud: https://soundcloud.com/ninjatracks Facebook: https://www.facebook.com/ninjatracks Playlist: https://www.youtube.com/playlist?list=PLlbnzwCkgkTB7xBdLOfwPt4vXghX8mzQD Image Albums:- http://theprimes1.imgur.com/ http://theprimes2.imgur.com/ http://theprimes3.imgur.com/ http://theprimes4.imgur.com/ http://theprimes5.imgur.com/ http://theprimes6.imgur.com/ http://theprimes7.imgur.com/ http://theprimes8.imgur.com/ http://theprimes9.imgur.com/ http://theprimes10.imgur.com/ http://theprimes11.imgur.com/ http://theprimes12.imgur.com/ http://theprimes13.imgur.com/ http://theprimes14.imgur.com/ http://theprimes15.imgur.com/ (New Images Goes Here) Additional Links in channel description. Main Image Source: http://wallbase.cc Note for the new Artists: If you would like to submit your own track or Artwork then please follow below instructions. You can either send me an message in here with download link or you can email me at [email protected] - If it's a Track then it needs to be at least 320 kbps with small description/Genre on the track. - If it's a Artwork then it always needs to be 16:9 aspect ratio (No logos in bottom corners) Make sure you subscribe to these two channels. Backup Channel: http://goo.gl/P5T9gI One Hour Station: http://goo.gl/44ZRnG Note:- I'm not the creator of this Music or Image, All rights belongs to respective owners. Feel free to Message me if you know the original Image Artist. This video is purely fan-made, it's done for entertainment purposes only. Have Fun! ▂▂▂▂▂▂▂▂▂▂▂▂▂ Note for the new Artists: ✖✖ If you would like to submit your track, visual art for promotion. ✖✖ If you want to add any kind of information which belongs to the video (audio or visual) ✖✖ If you have any issues regarding any of the videos. Please look for my email address in my channel's about page, please do not send me message on this channel. ▂▂▂▂▂▂▂▂▂▂▂▂▂ Submission Requirements: ✖✖ Audio - Please specify the genre on submission ✖✖ Please provide all your social media links for description. ✖✖ Audio - Must be minimum 320kbps ▂▂▂▂▂▂▂▂ Copyright Info © ✔ Be aware all music and pictures belongs to the original artists. ✔ I am in no position to give anyone permission to use this.
Views: 42866 ThePrimeCronus
What is Data Mining - Data Science Jargon for Beginners
 
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In this video I am going to give a simple and beginner definition of what data mining is in data analytics. The data science industry is very complicated, so I want to define data mining for you today. ► Full Playlist Explaining Data Jargon ( https://www.youtube.com/playlist?list=PL_9qmWdi19yDhnzqVCAhA4ALqDoqjeUOr ) ► http://jobsinthefuture.com/index.php/2017/11/25/what-is-data-mining-data-science-jargon-for-beginners/ Data Mining. This term is nearly self explanatory, but let's dig into it (haha, dig into it, data mining) and define data mining a little more to clarify any details. Data Miners explore large sets of data in order to discover patterns in the sets. Data miners look for patterns in order to define medical, buying habits, food shortages, etc... If you are going into the field of Data Analytics you will most certainly be doing a great deal of data mining. Data mining is a mass scale version of looking through thousands of people's daily biographies. What I mean by "looking through people's biographies" is you will be trying to understand how people are responding the the situation you are researching via data. Let's say your company releases a new drug to the market. This drug has been tested to stop the process of breakdown in joints that often leads to rheumatoid arthritis. Your drug ships out to 10,000 trial patients. Now you have a 10,000 person data set to manage. As the trial operates and the patients report their daily experience with the new drug you are being flooded with data about the drug. It is your job as the data miner to find the patterns and insights in order to accurately determine whether the drug is safe or not, the drug needs improvements, or perhaps the drug is not as effective as the company had hoped. In a nutshell data mining is a data analysts daily routine of researching data sets in order to learn from the data. Don't miss the Full review on Data Analytics defined and how to get a job! --- http://jobsinthefuture.com/index.php/2017/10/21/data-analyst-salary-and-how-to-become-a-data-analyst/ ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
Views: 446 Ben G Kaiser
Explanation of "SocialNetwork" for Hong Kong IT Job Advertisement Data Mining Report
 
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Explanation what is the meaning of "SocialNetwork" under the Hong Kong IT Job Skill Index. It investigates and visualizes the relationship between the top 10 highest correlation keyword with the social network analysis techniques. http://itjobanalysis.data-hk.com/
Views: 269 Cyrus Wong
Data mining on incremental data - BE IT Final Year Project 2017
 
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Data mining on incremental data - BE IT Final Year Project 2017
Advanced Excel - Data Mining Techniques using Excel
 
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Key Takeaways for the session : Breaking junk using formula and generate reports VBA to manipulate data in required format Data extraction from external files Who should attend? People from any domain who work on data in any form. Good for Engineers, Leads, Managers, Sales people, HR, MIS experts, Data scientists, IT Support, BPO, KPO etc. Feel free to write me at [email protected]
Views: 23658 xtremeExcel
Introduction to Data Mining: Basic Vocabulary
 
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It all starts with the fundamentals! In this data mining session we give you all the background information, technical terminology, and basic knowledge that you will need to hit the ground running. In part 1 of this data mining video series, we cover what data is and the basic vocabulary associated with it. Topics: - Data and Data Types - Data Quality - Data Preprocessing - Similarity and Dissimilarity - Data Exploration and Visualization -- 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/H0f8LhN0 See what our past attendees are saying here: https://hubs.ly/H0f8LhR0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 28418 Data Science Dojo
Facebook, Instagram's 10-Year Challenge May Be A Data Mining Tool | Lehren News
 
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Download The 'Lehren App': https://goo.gl/m2xNRt After data privacy controversy, now it’s the #10yearchallenge that is trending on the social media. While it may seem like a harmless meme, it’s quite possible that to companies like Facebook this is a goldmine of data. #10YearChallange, #Facebook, #MarkZukerberg Log On To Our Official Website : http://www.lehren.com Instagram : https://www.instagram.com/lehrennetworks Facebook : https://www.facebook.com/LehrenNetworks Twitter : https://twitter.com/LehrenNetworks Pinterest : https://in.pinterest.com/lehrenNetworks Google+ : https://plus.google.com/+LehrenCo
Views: 47 Lehren News
Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning
 
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Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 46672 Augmented Startups
Data Mining IT 5th Session6 "ETL Using SSIS"
 
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The Session includes the Following Steps: We Will Create simple "Integration Service Project" to Perform "Extract Transfer Load" Processes 1# EXTRACT: Extracting Data from "XML Source" 2# Creating an Empty DataBase To load the extracted Data into it. 3# TRANSFORM: Creating "Derived Column" to merge multiple columns before loading process 4# LOAD: Loading the extracted and Transformed data into the created DataBase
Views: 664 Mounir Alyousef
Domain driven data mining - Know It ALL 🔊✅
 
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Domain driven data mining will be explored in this video. This video series is something special. We're fully delving into all things everything. This breaks from merely pronouncing and discussing and goes further to deeply understand words and ideas. Link to Amazon.com http://amzn.to/2hFyI1h Link above take you to amazon and then amazon kicks me some money for alerting you to some awesome goods. We thank you for clicking the links. THANK for WATCHING, SUBSCRIBING, LIKING, COMMENTING, SHARING and DONATING!!! It means a lot to my family! PLEASE DONATE via VENMO for MORE EDUCATIONAL CONTENT and ENDEAVORS https://venmo.com/SeeHearSayLearn SeeHearSayLearn.com presents a series of videos to get you speaking and learning languages such as English, Spanish / Espanol, French, German, Albanian, Arabic, and more. We are working hard to get our videos uploaded. We provide you with word pronunciations, definitions, translations, stories, rhymes, riddles, jokes, tongue twisters, and anything that will help bridge the gap between your current fluency to your desired proficiency and understanding. Whether you're just learning or trying to bolster your intellectual quotient into a new stratosphere of concise and succinct communications, allocating the proper verbiage could be paramount to illustrating a picture for the recipient or merely shoving drab nondescript sounds of failure down their auditory meatuses. Run on sentence you say? I'd agree. Utilizing big complicated words isn't usually the most effective form of communication, but adapting your language to your recipient will be the most effective way to transfer your thoughts. Having a wide array of tools for each project will allow you to tailor your message for the most effect and efficient use of your time. To write, read, and listen to language takes fewer words than you might imagine. In each language, you could likely get away with understanding a few thousand words and be completely comfortable with many different language settings. Why even a few hundred can get you quite far. If ever you find any of the words to be inaccurate in any way, which may most often be the pronunciation I want to thank anyone who reaches out to send me a message regarding any errors. I will do my best to read and correct any perceived errors. Be advised that many pronunciation can vary slightly between regions. My congratulations to anyone broadening their word bank in any language. Science is clear that with more word associations languages become easier to learn and has the potential to be a protective buffer against dementia and Alzheimer's Disease. Please visit www.seehearsaylearn.com FACEBOOK FOLLOW https://www.facebook.com/seehearsaylearn TWITTER FOLLOW https://www.twitter.com/seehearsaylearn YOUTUBE SUBSCRIBE https://www.youtube.com/channel/UCeElmCkT1hfDJ7YhLCwxG_g PLEASE DONATE via VENMO for MORE EDUCATIONAL CONTENT and ENDEAVORS https://venmo.com/SeeHearSayLearn THANK for WATCHING, SUBSCRIBING, LIKING, COMMENTING, SHARING and DONATING!!! It means a lot to my family! This video series couldn't do what it does without the help of Wikipedia and its community along with so many other people to thank.
Views: 110 See Hear Say Learn
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: 4913 PyData
Data Mining with Data Visualization: Keynote at Big Data Analysis and Data Mining, 2016
 
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Keynote talk at the 3rd International Conference on Big Data Analysis and Data Mining, London, UK, 26-27 Sept 2016. Abstract: Some people believe that we live in the Age of Information. I believe it’s much more accurate to say we live in the Age of Data. With the rapid advancement of big data storage technologies and the ever-decreasing costs of hardware, our ability to derive and store data is unprecedented. However, a large gap remains between our ability to generate and store large collections of complex, time-dependent data and our ability to derive useful information and knowledge from it. Data visualization leverages our most powerful sense, vision, in order to derive knowledge and gain insight into large, multi-variate data sets that describe complicated and often time-dependent behavior. This talk presents an introduction into the world of data visualization with very different taster applications: Call Center Visualization, Computational Fluid Dynamics (CFD), marine biology, molecular dynamics, and rugby, showcasing some of visualizations strengths, weaknesses, and goals. The identity of Sally might be revealed during the talk. Connect with DataVis Bob on Facebook: https://www.facebook.com/datavisbob
Views: 215 DataVisBob Laramee
Data Mining Lecture -- Decision Tree | Solved Example (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 169666 Well Academy
Classification of Data Mining Problems v1
 
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I will explain 9 common data mining problem types. The information in this presentation is mostly based on the great book called "Data Science for Business" written by Provost and Fawcett. http://datascience.mertnuhoglu.com Please give positive or negative feedback on the presentation. Does it help? What do you suggest to make it better?
Views: 8976 Mert Nuhoglu

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