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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: 156267 Last moment tuitions
Extracting and Mining Of Data From PDF and WEB
 
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ABSTRACT In most of the Universities, results are published on web or send via PDF files. Currently many of the colleges use manual process to analyze the results. Sadly the college staff has to manually fill the student result details and then analyze the rankings accordingly. Our proposed system will extract the data automatically from PDF and web, create dynamic database and analyze data, for this system make use of PDF Extractor, Pattern matching techniques, data mining, Web mining technique and sorting technique.
Weka Tutorial 24: Model Comparison (Model Evaluation)
 
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In this tutorial, you will learn how to use Weka Experimenter to compare the performances of multiple classifiers on single or multiple datasets. Please subscribe to get more updates and like if the tutorial is useful. Link in: http://www.linkedin.com/pub/rushdi-shams/3b/83b/9b3
Views: 27055 Rushdi Shams
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 101769 LearnEveryone
KEEL Data mining tool demo
 
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KEEL Data minig tool Demo of installation and Working
Views: 3680 Manukumar K J
CMPE 239 Social Media Data Mining
 
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CMPE 239- Preventing Foodborne Illness by Data Mining Social Media Source: http://192.5.53.208/u/kautz/papers/sadilek-kautz.pdf
Views: 29 Romin Oushana
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
PDF Data Extraction and Automation 3.1
 
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Learn how to read and extract PDF data. Whether in native text format or scanned images, UiPath allows you to navigate, identify and use PDF data however you need. Read PDF. Read PDF with OCR.
Views: 105880 UiPath
Intelligent Heart Disease Prediction System Using Data Mining Techniques
 
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Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
 
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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1484220 ExcelIsFun
Packet Hacking Village 2017 - YALDA - LARGE SCALE DATA MINING FOR THREAT INTELLIGENCE - GITA ZIABARI
 
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YALDA - LARGE SCALE DATA MINING FOR THREAT INTELLIGENCE GITA ZIABARI, SENIOR THREAT RESEARCH ENGINEER AT FIDELIS CYBERSECURITY PRESENTATION SLIDES: PHV2017-GZIABARI.PDF SLIDES AVAILABLE ON: https://www.wallofsheep.com Every SOC is deluged by massive amounts of logs, suspect files, alerts and data that make it impossible to respond to everything. It is essential to find the signal in the noise to be able to best protect an organization. This talk will cover techniques to automate the processing of data mining malware to derive key indicators to find active threats against an enterprise. Techniques will be discussed covering how to tune the automation to avoid false positives and the many struggles we have had in creating appropriate whitelists. We'll also discuss techniques for organizations to find and process intelligence for attacks targeting them specifically that no vendor can sell or provide them. Audiences would also learn about method of automatically identifying malicious data submitted to a malware analysis sandbox. Gita Ziabari (Twitter: @gitaziabari) is working at Fidelis Cybersecurity as a Senior Threat Research Engineer. She has more than 13 years of experience in threat research, networking, testing and building automated frameworks. Her expertise is writing automated tools for data mining. She has unique approaches and techniques in automation. Brought to you by Aries Security - https://www.ariessecurity.com
Views: 114 Wall of Sheep
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: 137684 SciShow
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: 8357 Dropout Technical
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: 430122 Brandon Weinberg
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 860401 David Langer
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 63806 WekaMOOC
More Data Mining with Weka (4.6: Cost-sensitive classification vs. cost-sensitive learning)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Cost-sensitive classification vs. cost-sensitive learning http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7851 WekaMOOC
Web Scraping, Screen Scraping, Web Data Mining, Data Extractor
 
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Input FORMAT SUPPORT [website, webpage, Text, pdf, CSV, database] Output FORMAT SUPPORT [ excel, csv, tsv, pdf, xml, html, sql, MySql ] » Hotel website [ hotel name, address, images, reviews, latitude-longitude, price ] Scraping » Hotel price scraping for marketing intelligence [againast to your competitor] » Real Estate Data Extraction » Extract Store Details » University's Web Data Scraping » Extract Product Description » Web Information Extractor » Craigslist Email Extractor » Metadata Extraction » Website Email Extraction » Scraping Business Directory » Yellow Pages Scraping » PriceGrabber Data Extraction » Scraping Property Information » Amazon Product Extraction » Download Product Images » Automate osCommerce Product Upload » Scraping Business Contact » Craigslist Posting Service » Imdb Data Extraction » Meta Data Extraction » Scraping From Dynamic Pages » Extract Lyrics Data » Email Scraping & Extraction » Scraping Customer List » Scraping Data From WebSite ----------------------------------- Expertise In -------------------------- » Hotel Website Scraping [expedia.com, hotels.com, booking.com, orbitz.com, airasia.com, easybook.com, laterooms.com, travelocity.com, thomascook.com, activehotels.com, priceline.com, lastminute.com, yatra.com, makemytrip.com etc.] » Ads Classifieds Scraping [gumtree.com, olx.com, craigslist.com etc] » Real Estate Scraping [99acres.com, www.zillow.com, www.trulia.com, www.realtor.com, www.agentimage.com, www.realtysoft.com, www.realestate.com.au etc.] » Product catalog Scraping [amazon.com , ebay.com, yellowpage, whitepage etc.] Contact if any service require [ [email protected] ]
Views: 61831 vickyrathee2005
Scales of Measurement - Nominal, Ordinal, Interval, Ratio (Part 1) - Introductory Statistics
 
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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 290714 Quantitative Specialists
Multimove - A Trajectory Data Mining Tool
 
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2013 - Mining Representative Movement Patterns through Compression NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. The 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), Goal Coast, Australia, April 2013. (acceptance rate: 11.3%) 2012 - Mining Time Relaxed Gradual Moving Object Clusters NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2012), Redondo Beach, California, November 2012. [pdf] [demo] [code] (acceptance rate: 22%) 2012 - GeT_Move: An Efficient and Unifying Spatio-Temporal Pattern Mining Algorithm for Moving Objects NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 11th International Symposium on Intelligent Data Analysis (IDA 2012), Helsinki, Finland, October 2012. 2012 - Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2012), Demo Paper, Bristol, UK, September 2012.
Views: 474 nhathai phan
Shmoocon 2012: Malware Visualization in 3D
 
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This video is part of the Infosec Video Collection at SecurityTube.net: http://www.securitytube.net Shmoocon 2012: Malware Visualization in 3D PDF :- http://www.shmoocon.org/2012/presentations/Danny_Quist-3dmalware-shmoocon2012.pdf Malware reverse engineering is greatly helped by visualization techniques. In this talk I will show you my 3D visualization enhancements to VERA for creating compelling, and useful displays of malware. This new tool provides a new method to visualize running code, show concurrent running threads of execution, visualize the temporal relationships of the code, and illustrate complicated packer original entry point detection. Real! Live! Reverse Engineering! of the past year of malware will show the utility of the program on in-the-wild samples. Danny Quist is a research scientist at Los Alamos National Laboratory and the founder of Offensive Computing, LLC. His research is in automated analysis methods for malware with software and hardware assisted techniques. He consults with both private and public sectors on system and network security. His interests include malware defense, reverse engineering, exploitation methods, virtual machines, and automatic classification systems. Danny holds a Ph.D. from the New Mexico Institute of Mining and Technology. He is the master of the Five Point Exploding Packer Technique. Danny has presented at several industry conferences including Blackhat, RSA, ShmooCon, Vizsec, and Defcon.
Views: 1709 SecurityTubeCons
Best books on Data Mining
 
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Best books on Data Mining
Views: 268 Books Magazines
Predicting the Winning Team with Machine Learning
 
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Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 78902 Siraj Raval
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 345530 APMonitor.com
I will do web scraping and data mining for lead generation
 
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Hi Welcome to my Gig. Here I'm expert in Data Mining, web Scraping, web crawling, Email extraction, Data Entry, Data Conversion and so on. I have lots of experience in this field. So just send your requirements to me before place the order. Here is my working area for this gig: Website scraping Data Mining Yellowpages scraping Business Lead generation Social Media Scrape Email list extraction Big database scraping/collection Use proxies for scraping Download images & content Extract data from PDF Data Entry, Copy Paste, CSV, PNG, PDF, EXCEL, OCR file conversion Please knock before place the order so that we can mutually accept the cost and delivery schedule of the project. Thanks place Order Here:https://www.fiverr.com/foysal123/do-web-scraping-and-data-mining-for-lead-generation
Views: 124 Foysal Rahaman
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 63742 deltaDNA
Veri Madenciliği Nedir?
 
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Veri Madenciliği, Veri Madenciliği Dersleri, Veri Madenciliği Nedir?, Veri Madenciliği Eğitim Seti, Excel-Weka İlişkisi TAGS veri madenciliği yöntemleri, veri madenciliği nedir, microsoft excell, makro nedir, makrolarla excell dersleri, mining data, mining, datamining, what is data, data mining pdf, data mining techniques, data analysis, mineria, mineria de datos, data warehouse, data warehousing, database, data mining algorithm, data mining ppt, database mining, data mining software, big data, clustering, data mining tools, google data mining, classification, big data mining, smite, datamining smite, smite data mining, coursera, smite reddit, smite patch notes, smite wiki,0 gw2 data mining, big data analytics, bigdata, big data mining, big data, slideshare, kaggle, data scientist, hadoop,0 data mining meaning, jurnal data mining, python data mining, data mining adalah, nptel, python, data mining pdf TAGS http://kodkolik.net/
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
Views: 271282 Examrace
Advanced Data Mining with Weka (5.2: Building models)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 2: Building models http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/7XXl63 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2043 WekaMOOC
Data Mining with Weka (4.5: Support vector machines)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 5: Support vector machines http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 43036 WekaMOOC
Veri Madenciliği(Excel İşlemleri)-Bölüm 1
 
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Veri Madenciliği, Veri Madenciliği(Excel İşlemleri), Veri Madenciliği(Excel İşlemleri)-Bölüm 1 TAGS veri madenciliği yöntemleri, veri madenciliği nedir, microsoft excell, makro nedir, makrolarla excell dersleri, mining data, mining, datamining, what is data, data mining pdf, data mining techniques, data analysis, mineria, mineria de datos, data warehouse, data warehousing, database, data mining algorithm, data mining ppt, database mining, data mining software, big data, clustering, data mining tools, google data mining, classification, big data mining, smite, datamining smite, smite data mining, coursera, smite reddit, smite patch notes, smite wiki,0 gw2 data mining, big data analytics, bigdata, big data mining, big data, slideshare, kaggle, data scientist, hadoop,0 data mining meaning, jurnal data mining, python data mining, data mining adalah, nptel, python, data mining pdf TAGS https://www.kodkolik.net/
Sampling: Simple Random, Convenience, systematic, cluster, stratified - Statistics Help
 
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This video describes five common methods of sampling in data collection. Each has a helpful diagrammatic representation. You might like to read my blog: https://creativemaths.net/blog/
Views: 680868 Dr Nic's Maths and Stats
More Data Mining with Weka (3.4: Learning association rules)
 
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More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Learning association rules http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 12474 WekaMOOC
LinkedIn Hacks to Generate a Ton of Leads from LinkedIn - Lead Generation using LinkedIn
 
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LinkedIn Hacks Free PDF : http://www.ebizuniverse.com/linkedin-lead-generation/ Are you looking to get more leads for your business? What is your lead generation strategy? Is it word of mouth? Are you sitting and waiting for a new referral to come in? Are you attending networking lunches and hoping the prospects would call you? What if I told you there was a better way to generate leads? I want you to imagine what an extra 5-10 leads per day would do for your business. Does that mean more sales? Hiring new staff? What I want to do for you in this video is to show you how you can generate more qualified leads and referrals for your business and keep generating it on a consistent basis using a tool you might already be familiar with. First you have to understand that there is a shift going on in the business world right now led by digital media. The old way of doing business is out. There’s a new and better way of doing business using digital media. Businesses who are not willing to adapt their business growth techniques to the current digital landscape are going to continue to see their revenue decline. Digital media is mandatory if you want to grow your business. Social Media and Social networks: There is a lot going on in the Social media world. But I want to focus on LinkedIn. LinkedIn is hands down the #1 social network for client facing companies to get new business. LinkedIn is huge if you want to generate B2B leads. Let's look at the stats: According to Social Media Marketing Industry report, LinkedIn has now surpassed Facebook as the #1 most important social platform for B2B marketers. 80% of leads generated for B2B marketers today come from LinkedIn - Kissmetrics 92% of B2B marketers leverage LinkedIn over all other social platforms - Kissmetrics LinkedIn messages get an 11x better response rate than other methods - Replicon 46% of visits to corporate websites from social come from LinkedIn - Replicon I could go on and on..if you want more proof, simply Google it to find out. So what does that mean? That means you have no choice but to leverage LinkedIn as a lead generation tool for your business. Today I’m going to show you 4 steps on how you can leverage your LinkedIn profile to generate leads: Spice Up your Profile You might already have a profile that is just your title and your job position but you want to modify it to make it attractive and look like a Lead Magnet What problems have you solved for your clients? Case studies, testimonials Contact info, skills, volunteering info, endorsements, recommendations and a Nice picture Once you have worked on your profile, the next thing you want to do is get exposure. Get Exposure - Build Connections Think of your target market - who would you like to do business with? Do you want to connect with General managers, Marketing managers...once you know who you want to connect with, Go to search and execute a search. Create Engagement: The way you create engagement is not by sending random messages that don’t make sense. Basically you want to stand out as an authority in your field. How do you do that? By sharing information that helps your target market. By liking and commenting on other posts...especially influencers in your field. Post something on your profile that makes your connections curious. Share your blogs. Publish on LinkedIn (LinkedIn Pulse) Intelligent Messaging: Once you have your profile ready, your connections built up and you start to position yourself as an authority, it is time to take your LinkedIn to the next level. This is what I call running LinkedIn on over drive. You start with a messaging program to reach your ideal prospect. I want to warn you before you take the leap and start messaging people because this is where most people fail to do it right. How many times have you received a LinkedIn connection request followed by a sales pitch. They want to tell you how great their company is and their product is and why you should buy from them. This is the wrong approach. You have to first develop a relationship before you ask for an appointment or a phone call. So how do you do it the right way? Look at the person’s profile and see what you have in common, are you connected via a friend, a group etc… Then craft a message that is non salesy which will get them to respond. Let the conversation flow and when it is the right time, ask for an appointment of a phone call. So, there you have it! If you follow these steps, you are sure to have a ton of leads for your business on a regular basis: And DON’T FORGET to like the video and subscribe to our channel. Today I’m giving a free PDF download of the steps that will help change your LinkedIn profile into a lead generating machine. Go ahead and click the link below to grab your FREE download. http://www.ebizuniverse.com/linkedin-lead-generation/
Views: 28223 eBizUniverse
Signature and Line Item Data Mining
 
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Line Item Data Extraction and Signature validation with ChronoScan Capture
Views: 227 DocuFi
Excel - Weka İlişkisi
 
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Excel, Excel - Weka, Excel Weka İlişkisi, Excel Weka Eğitim Seti, Excel Weka Eğitim Serisi TAGS veri madenciliği yöntemleri, veri madenciliği nedir, microsoft excell, makro nedir, makrolarla excell dersleri, mining data, mining, datamining, what is data, data mining pdf, data mining techniques, data analysis, mineria, mineria de datos, data warehouse, data warehousing, database, data mining algorithm, data mining ppt, database mining, data mining software, big data, clustering, data mining tools, google data mining, classification, big data mining, smite, datamining smite, smite data mining, coursera, smite reddit, smite patch notes, smite wiki,0 gw2 data mining, big data analytics, bigdata, big data mining, big data, slideshare, kaggle, data scientist, hadoop,0 data mining meaning, jurnal data mining, python data mining, data mining adalah, nptel, python, data mining pdf TAGS http://kodkolik.net/ Machine Learning Group at the University of Waikato Project Software Book Publications People Related Weka 3: Data Mining Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. We have put together several free online courses that teach machine learning and data mining using Weka. Check out the website for the courses for details on when and how to enrol. The videos for the courses are available on Youtube. Yes, it is possible to apply Weka to big data!
Introduction to Text Analysis with NVivo 11 for Windows
 
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It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share. http://www.qsrinternational.com
Views: 115428 NVivo by QSR
Advanced Data Mining with Weka (1.4: Looking at forecasts)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 4: Looking at forecasts http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 4540 WekaMOOC
Datawatch and Angoss - fast data prep and analytics
 
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In today’s high speed analytics marketplace it is no surprise that data volumes and sources are expanding at an accelerating rate. On a daily basis, analysts spend up to 80 percent of their time collecting data from numerous sources such as the web, pdf’s, text reports, log files and many more to prepare it for analysis. Analysts are further challenged to make this data actionable with the use of predictive modeling. The alliance between Datawatch and Angoss offers businesses the fastest and most easy-to-use applications which significantly reduce time spent on data extraction, data preparation, and predictive modeling. Datawatch Monarch works with a wide range of report formats including PDF, XML, HTML, text, spool and ASCII files. Analysts can easily access data from invoices, sales reports, balance sheets, customer lists, inventory, logs and more. Data is then cleansed and consolidated into a single file for immediate consumption into any of the Angoss software applications. Analysts can now focus on translating their data into business value, without having to code, using the most easy-to-use and analyst recognized data mining and modeling techniques, such as Angoss’ best in class Decision Trees and Strategy Trees, to uncover important patterns within a dataset, identify good predictors, and produce accurate, stable and actionable predictions. Let us help you provide your business with the fastest and easiest tools for data acquisition, preparation, and business analytics.
Views: 328 Datawatch
Automated data scraping from websites into Excel
 
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Our Excel training videos on YouTube cover formulas, functions and VBA. Useful for beginners as well as advanced learners. New upload every Thursday. For details you can visit our website: http://www.familycomputerclub.com You can scrape, pull or get data from websites into Excel by performing a few simple steps. 1. record a macro to find out how one or many tables or data can be scraped from the website 2. Study the code carefully 3. Create an Excel sheet containing the links that get you the data from the appropriate web pages 4. Automate the process using a loop that creates a) New worksheets b) Automatically changes the link to the web pages that have the required data You can view the complete Excel VBA code here: http://www.familycomputerclub.com/scrpae-pull-data-from-websites-into-excel.html http://www.familycomputerclub.com/get-web-page-data-int-excel-using-vba.html Interesting Links: http://www.tushar-mehta.com/publish_train/xl_vba_cases/vba_web_pages_services/index.htm Get the book Excel 2016 Power Programming with VBA: http://amzn.to/2kDP35V If you are from India you can get this book here: http://amzn.to/2jzJGqU
Views: 505784 Dinesh Kumar Takyar
Advanced Data Mining with Weka (2.3: The MOA interface)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 3: The MOA interface http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3366 WekaMOOC
Data Mining with Weka (3.4: Decision trees)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Decision trees http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 65723 WekaMOOC
Anomaly Detection
 
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Anomaly detection video lesson describes techniques used in Exploratory Data Analysis for determining outliers. Visit our online service Assignment4Student http://assignment4student.com to find more lessons or submit your programming or math homework assignment. Free PDF presentation download: http://assignment4student.com/images/lessons/anomaly_detection.pdf
Views: 270 Assignment4Student
A Survey on Trajectory Data Mining: Techniques and Applications | Final Year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 205 myproject bazaar

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