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Movie Success Prediction Using Data Mining Prediction of movie success using data mining
 
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Movie Success Prediction Using Data Mining Prediction of movie success using data mining
Top K Problem - Intro to Algorithms
 
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This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
Views: 5818 Udacity
Mathematics of Machine Learning
 
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Do you need to know math to do machine learning? Yes! The big 4 math disciplines that make up machine learning are linear algebra, probability theory, calculus, and statistics. I'm going to cover how each are used by going through a linear regression problem that predicts the price of an apartment in NYC based on its price per square foot. Then we'll switch over to a logistic regression model to change it up a bit. This will be a hands-on way to see how each of these disciplines are used in the field. Code for this video (with coding challenge): https://github.com/llSourcell/math_of_machine_learning Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Sign up for the next course at The School of AI: http://theschool.ai/ More learning resources: https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/ https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning https://courses.washington.edu/css490/2012.Winter/lecture_slides/02_math_essentials.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 280180 Siraj Raval
How decision trees algorithm works
 
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In this video we describe how the decision tree algorithm works, how it selects the best features to classify the input patterns. Based on the C4.5 algorithm strategy, proposed by Quinlan, 1993.
Views: 64723 Thales Sehn Körting
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: 476269 Brandon Weinberg
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 459279 Thales Sehn Körting
Deep Learning: Intelligence from Big Data
 
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Deep Learning: Intelligence from Big Data Tue Sep 16, 2014 6:00 pm - 8:30 pm Stanford Graduate School of Business Knight Management Center – Cemex Auditorium 641 Knight Way, Stanford, CA A machine learning approach inspired by the human brain, Deep Learning is taking many industries by storm. Empowered by the latest generation of commodity computing, Deep Learning begins to derive significant value from Big Data. It has already radically improved the computer’s ability to recognize speech and identify objects in images, two fundamental hallmarks of human intelligence. Industry giants such as Google, Facebook, and Baidu have acquired most of the dominant players in this space to improve their product offerings. At the same time, startup entrepreneurs are creating a new paradigm, Intelligence as a Service, by providing APIs that democratize access to Deep Learning algorithms. Join us on September 16, 2014 to learn more about this exciting new technology and be introduced to some of the new application domains, the business models, and the key players in this emerging field. Moderator Steve Jurvetson, Partner, DFJ Ventures Panelists Adam Berenzweig, Co-founder and CTO, Clarifai Naveen Rao, Co-founder and CEO, Nervana Systems Elliot Turner, Founder and CEO, AlchemyAPI Ilya Sutskever, Research Scientist, Google Brain Demo Companies**: Clarifai | SkyMind | Ersatz Labs | AlchemyAPI ** Follow (@VLAB) on Twitter and Event Hashtag #VLABdl
Views: 550906 vlabvideos
Data and Predictive Analytics: Moneyball in Hollywood w Legendary Entertainment (CXOTalk #276)
 
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The movie industry has adopted "moneyball" techniques, based on data and analytics, to drive box office success. Join CXOTalk host Michael Krigsman in conversation with analytics pioneer, Matthew Marolda, to explore how Legendary Entertainment uses analytics to drive box office success. For more information and to read the complete transcript, see https://www.cxotalk.com/episode/moneyball-movies-data-analytics-legendary-entertainment Matt Marolda is Chief Analytics Officer at Legendary Entertainment, where he started the company's Applied Analytics division, which uses data and analytics to drive strategic decisions across all aspects of the company. Before joining Matt founded StratBridge, which developed software to help many organizations in the NFL, NBA, European Football and Major League Soccer with “moneyball” player analysis, dynamic pricing and revenue analysis.
Views: 6528 CXOTALK
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 819048 statisticsfun
Complete Machine Learning Course | Learn Machine Learning | Machine Learning Tutorial | Edureka
 
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** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training ** *** Topics Wise Machine Learning Podcast : https://castbox.fm/channel/id1832236?country=us *** This Edureka Machine Learning video on "Machine Learning Full Course" will provide you with detailed and comprehensive knowledge of Machine Learning. It will provide you with the in-depth knowledge of the different types of Machine Learning with the different algorithms that lie under each category with a demo for each algorithm and the approach one should take to solve these problems. This Machine Learning tutorial will be covering the following topics: 1:44 What is Data Science? 3:09 Data Science Peripherals 3:37 What is Machine learning? 4:10 Features of Machine Learning 4:46 How it works? 5:36 Applications of Machine Learning 13:21 Market Trend of Machine Learning 14:29 Machine Learning Life Cycle 17:26 Important Python Libraries 19:20 Types of Machine Learning 19:33 Supervised Learning 20:50 Unsupervised Learning 21:52 Reinforcement Learning 23:23 Detailed Supervised Learning 24:50 Supervised Learning Algorithms 26:28 Linear Regression 28:53 Use Case(with Demo) 35:23 Model Fitting 36:36 Need for Logistic Regression 37:33 What is Logistic Regression? 39:46 What is Decision Tree? 49:33 What is Random Forest? 57:10 What is Naïve Bayes? 1:09:16 Detailed Unsupervised Learning 1:10:15 What is Clustering? 1:12:13 Types of Clustering 1:25:57 Market Basket Analysis 1:27:02 Association Rule Mining 1:29:06 Example 1:29:44 Apriori Algorithm 1:39:11 Detailed Reinforcement Learning 1:41:46 Reward Maximization 1:44:13 The Epsilon Greedy Algorithm 1:44:59 Markov Decision Process 1:47:19 Q-Learning Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV ------------------------------------------------------------------------------------ Instagram: https://www.instagram.com/edureka_learning/ Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #edurekamachinelearning #machinelearningcourse #machinelearningforbeginners #machinelearningtraining #machinelearningalgorithms #machinelearningusingpython #machinelearningproject ------------------------------------------------------------------------------------ About the Masters Program Edureka’s Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR. ------------------------------------------------------------------------------- Why Go for this Course? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning --------------------------------------------------------------------------------- Who should go for this course? Edureka’s Python Machine Learning Certification Course is a good fit for the below professionals: Developers aspiring to be a ‘Machine Learning Engineer' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics 'Python' professionals who want to design automatic predictive models Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For TensorflowTraining and Certification, Call us at US: +18336900808 (Toll Free) or India: +918861301699 Or, write back to us at [email protected]
Views: 127142 edureka!
SD IEEE Dotnet 03 Criminals and crime hotspot detection using data mining algorithms
 
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How SVM (Support Vector Machine) algorithm works
 
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In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share
Views: 553894 Thales Sehn Körting
HACKER | Algorithm -  FULL MOVIE ENGLISH SUBTITLE
 
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HACKER | Algorithm - FULL MOVIE ENGLISH SUBTITLE A freelance computer hacker discovers a mysterious government computer program. He breaks into the program and is thrust into a revolution. Director: Jon Schiefer (as Jonathan Schiefer) Writer: Jon Schiefer (as Jonathan Schiefer) Stars: Raphael Barker, Keith Barletta, Julie Ceballos | LIKE THIS? PLEASE DONT FORGET TO SUBSCRIBE GUYS! THANKS SUPPORT ME! Donate To My Channel: https://www.paypal.me/esctvl Youtube: https://www.youtube.com/channel/UCZkkGRFIYWQUfCWjab2fApw Instagram: https://www.instagram.com/esctvl/ Twitter: https://twitter.com/esctvl
Views: 16236 Escape Travel 360
Technical Course: Cluster Analysis: K-Means Algorithm for Clustering
 
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K-Means Algorithm for clustering by Gaurav Vohra, founder of Jigsaw Academy. This is a clip from the Clustering module of our course on analytics. Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training Follow us on: https://www.facebook.com/jigsawacademy https://twitter.com/jigsawacademy http://jigsawacademy.com/
Views: 205368 Jigsaw Academy
Data Science Full Course | Learn Data Science - Full Course | Data Science for Beginners | Edureka
 
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** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science Full Course" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then moves to Machine Learning and Finally ends the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This Data Science tutorial will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Data Scientist Salary Trends 6:37 Data Scientist Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 Machine Learning Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 68965 edureka!
Hashing and Hash table in data structure and algorithm
 
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This video lecture is produced by S. Saurabh. He is B.Tech from IIT and MS from USA. hashing in data structure hash table hash function hashing in dbms To study interview questions on Linked List watch http://www.youtube.com/playlist?list=PL3D11462114F778D7&feature=view_all To prepare for programming Interview Questions on Binary Trees http://www.youtube.com/playlist?list=PLC3855D81E15BC990&feature=view_all To study programming Interview questions on Stack, Queues, Arrays visit http://www.youtube.com/playlist?list=PL65BCEDD6788C3F27&feature=view_all To watch all Programming Interview Questions visit http://www.youtube.com/playlist?list=PLD629C50E1A85BF84&feature=view_all To learn about Pointers in C visit http://www.youtube.com/playlist?list=PLC68607ACFA43C084&feature=view_all To learn C programming from IITian S.Saurabh visit http://www.youtube.com/playlist?list=PL3C47C530C457BACD&feature=view_all
Views: 334245 saurabhschool
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 140415 Brandon Weinberg
Shai Linne 4th Best Rapper? Data Mining Algorithm Proves So!
 
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Check the article here: http://mining4meaning.com/2015/02/13/raplyzer/ Shai is a Christian rapper: http://lampmode.com/artists/shai-linne/ Author: @ericmalmi Here's the list!: http://koti.kapsi.fi/emalmi/raplyzer_results.html ● SEND ME CHH NEWS TO HAVE YOUR CHANNEL FEATURED ● ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ●Twitter: @NatuMyers ●Instagram: @NatuMyers ●Requests: [email protected] ●Check out my personal site: http://natumyers.com ●Join Team Unashamed: http://goo.gl/9F9HyN ●Facebook: https://www.facebook.com/AltermaxMedia ●My music: goo.gl/21mU9X ●Soundcloud: soundcloud.com/NatuMyers
Lecture 10 - Neural Networks
 
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Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on May 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 364307 caltech
Lecture 05 - Training Versus Testing
 
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Training versus Testing - The difference between training and testing in mathematical terms. What makes a learning model able to generalize? Lecture 5 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 17, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 160239 caltech
Automatic Recommendation for Online Users Using Web Usage Mining | ieee data mining projects
 
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Automatic Recommendation for Online Users Using Web Usage Mining 2012 ieee data mining project in java (mtech,btech,mca). Read more http://ieee-projects10.com/automatic-recommendation-for-online-users-using-web-usage-mining/
Views: 2525 satya narayana
Humans Need Not Apply
 
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Support Grey making videos: https://www.patreon.com/cgpgrey ## Robots, Etc: Terex Port automation: http://www.terex.com/port-solutions/en/products/new-equipment/automated-guided-vehicles/lift-agv/index.htm Command | Cat MieStar System.: http://www.catminestarsystem.com/capability_sets/command Bosch Automotive Technology: http://www.bosch-automotivetechnology.com/en/de/specials/specials_for_more_driving_safety/automated_driving/automated_driving.html Atlas Update: https://www.youtube.com/watch?v=SD6Okylclb8&list=UU7vVhkEfw4nOGp8TyDk7RcQ Kiva Systems: http://www.kivasystems.com PhantomX running Phoenix code: https://www.youtube.com/watch?v=rAeQn5QnyXo iRobot, Do You: https://www.youtube.com/watch?v=da-5Uw8GBks&list=UUB6E-44uKOyRW9hX378XEyg New pharmacy robot at QEHB: https://www.youtube.com/watch?v=_Ql1ZHSkUPk Briggo Coffee Experience: http://vimeo.com/77993254 John Deere Autosteer ITEC Pro 2010. In use while cultivating: https://www.youtube.com/watch?v=VAPfImWdkDw&t=19s The Duel: Timo Boll vs. KUKA Robot: https://www.youtube.com/watch?v=tIIJME8-au8 Baxter with the Power of Intera 3: https://www.youtube.com/watch?v=DKR_pje7X2A&list=UUpSQ-euTEYaq5VtmEWukyiQ Baxter Research Robot SDK 1.0: https://www.youtube.com/watch?v=wgQLzin4I9M&list=UUpSQ-euTEYaq5VtmEWukyiQ&index=11 Baxter the Bartender: https://www.youtube.com/watch?v=AeTs9tLsUmc&list=UUpSQ-euTEYaq5VtmEWukyiQ Online Cash Registers Touch-Screen EPOS System Demonstration: https://www.youtube.com/watch?v=3yA22B0rC4o Self-Service Check in: https://www.youtube.com/watch?v=OafuIBDzxxU Robot to play Flappy Bird: https://www.youtube.com/watch?v=kHkMaWZFePI e-david from University of Konstanz, Germany: https://vimeo.com/68859229 Sedasys: http://www.sedasys.com/ Empty Car Convoy: http://www.youtube.com/watch?v=EPTIXldrq3Q Clever robots for crops: http://www.crops-robots.eu/index.php?option=com_content&view=article&id=62&Itemid=61 Autonomously folding a pile of 5 previously-unseen towels: https://www.youtube.com/watch?v=gy5g33S0Gzo#t=94 LS3 Follow Tight: https://www.youtube.com/watch?v=hNUeSUXOc-w Robotic Handling material: https://www.youtube.com/watch?v=pT3XoqJ7lIY Caterpillar automation project: http://www.catminestarsystem.com/articles/autonomous-haulage-improves-mine-site-safety Universal Robots has reinvented industrial robotics: https://www.youtube.com/watch?v=UQj-1yZFEZI Introducing WildCat: https://www.youtube.com/watch?v=wE3fmFTtP9g The Human Brain Project - Video Overview: https://www.youtube.com/watch?v=JqMpGrM5ECo This Robot Is Changing How We Cure Diseases: https://www.youtube.com/watch?v=ra0e97Wiqds Jeopardy! - Watson Game 2: https://www.youtube.com/watch?v=kDA-7O1q4oo What Will You Do With Watson?: https://www.youtube.com/watch?v=Y_cqBP08yuA ## Other Credits Mandelbrot set: https://www.youtube.com/watch?v=NGMRB4O922I&list=UUoxcjq-8xIDTYp3uz647V5A Moore's law graph: http://en.wikipedia.org/wiki/File:PPTMooresLawai.jpg Apple II 1977: https://www.youtube.com/watch?v=CxJwy8NsXFs Beer Robot Fail m2803: https://www.youtube.com/watch?v=N4Lb_3_NMjE All Wales Ambulance Promotional Video: https://www.youtube.com/watch?v=658aiRoVp6s Clyde Robinson: https://www.flickr.com/photos/crobj/4312159033/in/photostream/ Time lapse Painting - Monster Spa: https://www.youtube.com/watch?v=ED14i8qLxr4
Views: 11404271 CGP Grey
Three principles for data science: predictability, stability, and computability
 
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Speaker: Bin Yu, Chancellor’s Professor of Statistics at the University of California at Berkeley Berkeley Distinguished Lectures in Data Science, Fall 2017 https://bids.berkeley.edu/news/berkeley-distinguished-lectures-data-science Title: Three principles for data science: predictability, stability, and computability Date: September 12, 2017 Time: 4:10pm to 5:00pm Locations: BIDS, 190 Doe Library, UC Berkeley ABSTRACT In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions. Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future. Stability (relative to data and model perturbations) is also a minimum requirement for interpretability and reproducibility of data driven results (cf. Yu, 2013). It is closely related to uncertainty assessment. Obviously, both prediction and stability principles can not be employed without feasible computational algorithms, hence the importance of computability. The three principles will be demonstrated in the context of two neuroscience collaborative projects with the Gallant Lab and through analytical connections. In particular, the first project adds stability to predictive modeling used for reconstruction of movies from fMRI brain signlas to gain interpretability of the predictive model. The second project uses predictive transfer learning that combines AlexNet, GoogleNet and VGG with single V4 neuron data for state-of-the-art prediction performance. Moreover, it provides stable function characterization of neurons via (manifold) deep dream images from the predictive models in the difficult primate visual cortex V4. Our V4 results lend support, to a certain extent, to the resemblance of these CNNs to a primate brain. SPEAKER Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley and a former Chair of Statistics at Berkeley. She is founding co-director of the Microsoft Joint Lab at Peking University on Statistics and Information Technology. Her group at Berkeley is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine. In order to solve data problems in these domain areas, her group employs quantitative critical thinking and develops statistical and machine learning algorithms and theory. She has published more than 100 scientific papers in premier journals in statistics, machine learning, information theory, signal processing, remote sensing, neuroscience, genomics, and networks. She is a member of the U.S. National Academy of Sciences and fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, an invited speaker at ICIAM in 2011, the Tukey Memorial Lecturer of the Bernoulli Society in 2012, and an invited speaker at the Rietz Lecture of Institute of Mathematical Statistics (IMS) in 2016. She was IMS president in 2013–2014, and she is a fellow of IMS, ASA, AAAS, and IEEE. She has served or is serving on leadership committees of NAS-BMSA, SAMSI, IPAM, and ICERM and on editorial boards for the Journal of Machine Learning, Annals of Statistics, and Annual Review of Statistics. BERKELEY DISTINGUISHED LECTURES IN DATA SCIENCE https://bids.berkeley.edu/news/berkeley-distinguished-lectures-data-science The Berkeley Distinguished Lectures in Data Science, co-hosted by the Berkeley Institute for Data Science (BIDS) and the Berkeley Division of Data Sciences, features faculty doing visionary research that illustrates the character of the ongoing data, computational, inferential revolution. In this inaugural Fall 2017 "local edition," we bring forward Berkeley faculty working in these areas as part of enriching the active connections among colleagues campus-wide. All campus community members are welcome and encouraged to attend. Arrive at 3:30pm for tea, coffee, and discussion.
Quora Question Similarity Prediction Algorithm using Artificial Intelligence and Machine Learning
 
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#Call_9821876104 #BestInstitute #NTANET Thank you for watching our lectures. These Lectures are created for Thorough Understanding of Concepts for the Competitive examinations specially for UGC NET Computer Science and Applications. For Complete Study Material for UGC NET Exam preparation please call/whatsapp us at 9821876104/02 or email us at [email protected] . If you liked the video and it was helpful for you Please like the video and share it on Facebook with your friends so that others can also get benefitted from them. You can also check and visit out other playlists. I am sharing the links of other playlists here. You can also Add me on Facebook at facebook.com/Himanshu.kaushik.2590 or visit our websites www.gatelectures.com and www.digiimento.com Checkout our other playlists  Programming and Data Structures https://goo.gl/66Ndja  C Programming https://goo.gl/HGSbCR  Operating System https://goo.gl/1qp6gj  Digital Logic https://goo.gl/qhsRwH  Discrete Mathematics https://goo.gl/FjsiEk  Computer Networks https://goo.gl/DuRQPv  Theory of Computation https://goo.gl/CNKn25  Database Management System https://goo.gl/vJpDDU  UGC NET Paper 1 https://goo.gl/97Cpvo  Previous year paper Solutions https://goo.gl/KEDL9f  Compiler Design https://goo.gl/RykvXj  Artificial Intelligence https://goo.gl/WRdyYb  C Plus plus https://goo.gl/YPJsCi  Linear Programming Problem https://goo.gl/r58RxQ  UGC NET Paper 2 Solutions https://goo.gl/3Pia9w  Computer Graphics https://goo.gl/tRFa39  Microprocessor 8085 https://goo.gl/Z57Uit  DSSSB Computer Science https://goo.gl/kiZVvx  Placement Preparation https://goo.gl/siG3Ta  Daily Current Affairs  UPPSC LT Grade Teacher https://goo.gl/iZ3CY2  Current Affairs https://goo.gl/kJdnX5  NIELIT Scientist https://goo.gl/4GKNXG  Quantitative Aptitude https://goo.gl/Rzs6fj
BEST DATA MINING PROJECTS FOR STUDENTS IN ENGLAND
 
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Views: 24 RANJITH KUMAR
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 485810 sentdex
BEST DATA MINING PROJECTS FOR STUDENTS IN BAHRAIN
 
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Views: 29 kumar ranjith
SD IEEE Dotnet 09 A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I
 
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Data mining process for collecting Android apps behavior  -  Manual Training  Mode
 
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Detecting malicious Android applications using Support Vector Machines The purpose of this project is to identify malware (malicious Software)for Android platform using Support Vector Machine (SVM). This SVM, will be able to identify malicious applications before installing on the device and before malware can get any sensitive information. This way our data will be safe from malware. This video shows the basic way for collecting applications behavior data, Data-mining process. This data will be used for training a SVM. For more information : [email protected]
Views: 1079 Iker Burguera
Moneyball (Breaking Biases)
 
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Top 6 Analytical Movie Moments I'm sure I'm missing any number of different scenes spanning multiple movies. Here were the top six I've come across in my lifetime. I've themed them according to the different lessons they teach us in analysis. #2: Moneyball on "Breaking Biases"
Views: 1020379 TheFightinAnalyst
Backpropagation in 5 Minutes (tutorial)
 
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Let's discuss the math behind back-propagation. We'll go over the 3 terms from Calculus you need to understand it (derivatives, partial derivatives, and the chain rule and implement it programmatically. Code for this video: https://github.com/llSourcell/how_to_do_math_for_deep_learning Please Subscribe! And like. And comment. That's what keeps me going. I've used this code in a previous video. I had to keep the code as simple as possible in order to add on these mathematical explanations and keep it at around 5 minutes. More Learning resources: https://mihaiv.wordpress.com/2010/02/08/backpropagation-algorithm/ http://outlace.com/Computational-Graph/ http://briandolhansky.com/blog/2013/9/27/artificial-neural-networks-backpropagation-part-4 https://jeremykun.com/2012/12/09/neural-networks-and-backpropagation/ https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Forgot to add my patron shoutout at the end so special thanks to Patrons Tim Jiang, HG Oh, Hoang, Advait Shinde, Vijay Daniel & Umesh Rangasamy Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 166173 Siraj Raval
IEEE DATAMINING TOPICS - FINAL YEAR IEEE COMPUTER SCIENCE PROJECTS
 
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TSYS Center for Research and Development (TCRD) is a premier center for academic and industrial research needs. We at TRCD provide complete support for final year Post graduate Student (M.E / M.Tech / M. Sc/ MCA/ M-phil) who are doing course in computer science and Information technology to do their final year project and journal work. For Latest IEEE DATA MINING Projects Contact: TSYS Center for Research and Development (TSYS Academic Projects) Ph.No: 9841103123 / 044-42607879, Visit us: http://www.tsys.co.in/ Email: [email protected] IEEE TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING 2016 TOPICS 1. A Simple Message-Optimal Algorithm for Random Sampling from a Distributed Stream 2. Online Learning from Trapezoidal Data Streams 3. Quality-Aware Subgraph Matching Over Inconsistent Probabilistic Graph Databases 4. CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites 5. Online Subgraph Skyline Analysis over Knowledge Graphs 6. K Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis 7. ATD: Anomalous Topic Discovery in High Dimensional Discrete Data 8. Multilabel Classification via Co-evolutionary Multilabel Hypernetwork 9. Learning to Find Topic Experts in Twitter via Different Relations 10. Analytic Queries over Geospatial Time-Series Data Using Distributed Hash Tables 11. RSkNN: kNN Search on Road Networks by Incorporating Social Influence 12. Unsupervised Visual Hashing with Semantic Assistant for Content-based Image Retrieval 13. A Scalable Data Chunk Similarity based Compression Approach for Efficient Big Sensing Data Processing on Cloud 14. Network Motif Discovery: A GPU Approach 15. Crowdsourced Data Management: A Survey 16. Resolving Multi-Party Privacy Conflicts in Social Media 17. Improving Construction of Conditional Probability Tables for Ranked Nodes in Bayesian Networks 18. Clearing Contamination in Large Networks 19. Private Over-threshold Aggregation Protocols over Distributed Databases 20. Challenges in Data Crowdsourcing 21. Efficient R-Tree Based Indexing Scheme for Server-Centric Cloud Storage System
Views: 1011 Tsys Globalsolutions
Movie Predictor
 
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Video outlining our masters level project "The Movie Predictor" in Advanced Information Retrieval !!
Views: 69 Yashas t.r
AWS re: Invent BDT 305: Transforming Big Data with Spark and Shark
 
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The Berkeley AMPLab is developing a new open source data analysis software stack by deeply integrating machine learning and data analytics at scale (Algorithms), cloud and cluster computing (Machines) and crowdsourcing (People) to make sense of massive data. Current application efforts focus on cancer genomics, real-time traffic prediction, and collaborative analytics for mobile devices. In this talk, we present an overview of this stack and demonstrate key components: Spark and Shark.
Views: 1235 Amazon Web Services
YouTube, YouTubers and You - VPRO documentary - 2017
 
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Kids that want to become a YouTuber or a YouTube star as they grow up, that’s the unexpected outcome of the YouTube generation. A number of professional YouTubers give us a glimpse into the wonderful world of the YouTube industry. In ‘YouTube, YouTubers and You’, VPRO backlight meets with famous YouTubers like Kwebbelkop, and explores how YouTube and the almighty YouTube algorithm impact people’s lives. Original title: YouTubeland, Like and Subscribe In this YouTube documentary, we will question what outstanding earnings model is behind YouTube, and how will this model determine our future media landscape? YouTube has become one of the most powerful online platforms in this fast-moving world, with thousands of YouTubers looking to get to the sacred YouTube Golden play button. Nowadays, almost every kid in the world is dreaming of becoming a rich and famous YouTube star thanks to the video platform. How will YouTube and YouTubers determine our future media landscape? YouTube exists for 12 years now and has turned the media landscape upside down, bringing fame and wealth to many at hand. Both makers and advertisers are faced with this dominant position. Every day, hordes of young people start their own YouTube channel and aim to score YouTube money thanks to the YouTube Ad revenue, but also thanks to the influencers marketing. Will they be part of the richest YouTubers or the top 10 YouTubers? A true gold fever that causes these YouTube creators to constantly look up the boundaries in various YouTube challenges. The most successful Dutch YouTuber, Kwebbelkop, has more than seven million YouTube subscribers on his gaming YouTube channel. All of his subscribers watch massively every day how he plays video games or vlogs about his personal life. His success is huge. He is constantly recognized on the street and lives with his girlfriend Azzy, who is also a YouTuber, in a luxurious apartment in Amsterdam. His mother, better known as Kwebbelmom, is the manager of his million empire. With Kwebbelkop, we visit VidCon in Los Angeles, the main meeting place for YouTuber fans, online influencers and the platform industry. However, not everyone on YouTube is so successful. YouTube has a huge cemetery of videos without views. A glimpse in the kitchen in the world of the YouTubers, the marketers, the managers, the career and advertising opportunities, but also the psychological consequences of an online earnings model with 'You' as a unique selling point, from the first YouTube video to the first YouTube button, what does it take to be a YouTuber today? Originally broadcasted by VPRO in 2017. © VPRO Backlight October 2017 On VPRO broadcast you will find nonfiction videos with English subtitles, French subtitles and Spanish subtitles, such as documentaries, short interviews and documentary series. VPRO Documentary publishes one new subtitled documentary about current affairs, finance, sustainability, climate change or politics every week. We research subjects like politics, world economy, society and science with experts and try to grasp the essence of prominent trends and developments. Subscribe to our channel for great, subtitled, recent documentaries. Visit additional youtube channels bij VPRO broadcast: VPRO Broadcast, all international VPRO programs: https://www.youtube.com/VPRObroadcast VPRO DOK, German only documentaries: https://www.youtube.com/channel/UCBi0VEPANmiT5zOoGvCi8Sg VPRO Metropolis, remarkable stories from all over the world: https://www.youtube.com/user/VPROmetropolis VPRO World Stories, the travel series of VPRO: https://www.youtube.com/VPROworldstories VPRO Extra, additional footage and one off's: https://www.youtube.com/channel/UCTLrhK07g6LP-JtT0VVE56A www.VPRObroadcast.com Credits: Director: Nordin Lasfar Research: Halil Opzamuk English, French and Spanish subtitles: Ericsson. French and Spanish subtitles are co-funded by European Union.
Views: 29797 vpro documentary
Wil van der Aalst - Alexander von Humboldt-Professorship 2018 (EN)
 
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Wil van der Aalst is one of the leading minds in computer science. What is special about his research is that he does not restrict himself to issues of pure data processing using software and algorithms but investigates concrete business processes, workflows and organisational structures. He acquired his international reputation, in particular, as the founder of the still young field of process mining, whereby the flow of activities and order of events which make up a work process are chronologically reconstructed and analysed using log data. At the same time, consideration is also given to the human actors who carry out the individual steps in the processes. In this way, complex procedures that involve several human actors can be studied, such as airport luggage handling, bank lending or insurance claims processing. Process mining can be used, for example, to ascertain whether the actual processes coincide with the envisaged process workflows or whether certain steps in a process are particularly time consuming or cost intensive. The open source tool ProM, co-developed by van der Aalst, has become a standard tool worldwide. At RWTH Aachen University, Alexander von Humboldt Professor Wil van der Aalst will bolster the cross-cutting field of process and workflow analysis and drive cooperation with the engineering, business and economics, and medicine faculties, and also industry. The Alexander von Humboldt Professorship: https://www.humboldt-professur.de/en Deutsches Video: https://youtu.be/SHFqT5f_zeM
BitYota Data Warehouse Service
 
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In this slidecast, Dev Patel and Poulomi Damany from BitYota describe the company's Data Warehouse Service. "Our vision is to make data and analytics accessible to all. There's a revolution underway and we're taking sides. We want to create a data platform that enables everyone -- from data scientists and engineers to SQL savvy analysts, business and product users, to understand their data, to build better products/services, and create new avenues of growth and productivity."
Views: 267 RichReport
Scalable Learning of Collective Behavior ieee data mining projects | mtech ieee data mining projects
 
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Scalable Learning of Collective Behavior ieee data mining 2012 projects in java,data mining projects for mtech,btech,mca in java. Read more http://ieee-projects10.com/scalable-learning-of-collective-behavior/
Views: 271 satya narayana
Webinar | Disrupting the Opioid Epidemic with Data Analytics
 
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Drug overdose and opioid-related deaths continue to increase in the United States. With access to detailed records of drug seizures, arrests, Medicaid services, pharmacy loss, ambulance and emergency room data, deaths, and more, states have the ability to combat the epidemic through insights from data analytics. Desiring to reverse this dangerous trend of drug abuse, the State of Indiana partnered with KSM Consulting, in collaboration with SAP, to develop a data analytics solution that provides meaningful information to the State. With improved insight into drug abuse trends, the State has optimized the placement of treatment centers to provide help where it’s needed most, uncovered previously unseen crime factors, such as a growing trend in pharmacy robberies, and more. In the webinar, we’ll cover: combining state and federal data sources to identify trends, defining and monitoring success through KPIs and dashboards, and funding opportunities.
Views: 135 KSM Consulting
Guide : Lec 3 | C.S. - Data Representation, Data Storage, Data Encoding ( Part 7 )  Free seo tools
 
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Guide : Lec 3 | C.S. - Data Representation, Data Storage, Data Encoding ( Part 7 ) Free seo tools on bulkping for Site Search engine optimisation Movie computer, science, data, representation, storage, encoding, techniques, methods, internal, computers, definition, graphical, graphic, tabular, system, statistical, network, dimensionality, reduction, in, multiple, feature, architecture, representations, graphs, visual, classification, machine, learning, mining, text, audio, images, vector, raster, generalization, main, memory, circuitry, intelligent, pattern, recognition, neural, algorithms, inference, clustering, indexing, software, prediction, evaluation, storing C.S. Lecture 3: Data Representation, Data Storage, Data Encoding -- Contents -- 1. Numeric Data Representation. 2. Real Number Representation. 3. Main Memory. 4. Mass Storage and Magnetic Systems. 5. File Storage and Retrieval. 6. Analog and Digital Information. 7. Representing Text, Audio, Images, Graphics, and Video. 8. Data Compression. 9. Communication Errors. 10. Error-Correcting Codes. View Part 8: BulkPing Website: BulkPing/ Computer Science Forum: BulkPing
Views: 63 wetexclusion724y
MapReduce: Simplified Data Processing on Large Clusters, presented by Jon Tedesco
 
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Student summary presentation of the original MapReduce paper from OSDI '04 for CS 598 : Cloud Computing. The original paper can be found here: www.usenix.org/event/osdi04/tech/full_papers/dean/dean.pdf Sorry if the audio is a little quiet, didn't maintain the best quality exporting from PowerPoint.
Views: 4517 Jon Tedesco
Java in production for Data Mining Research projects (JavaDayKiev'15)
 
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Alexey Zinoviev presented this paper on the JavaDayKiev'15 conference Slides: http://www.slideshare.net/zaleslaw/javadaykiev15-java-in-production-for-data-mining-research-projects This paper covers next topics: Data Mining, Machine Learning, Hadoop, Spark, MLlib
Views: 331 Alexey Zinoviev
Weka Tutorial 05: Held-out Testing (Classification)
 
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Weka machine learning tool has the option to develop a classifier and apply that to your test sets. This tutorial shows you how.
Views: 55204 Rushdi Shams
Data Analytics in Tamil
 
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Collect the data form text file and. find the particular id . using pandas package . code Explanation .
Views: 192 SK TECH WARRIOR
More Data Mining with Weka (5.2: Multilayer Perceptrons)
 
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More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 2: Multilayer Perceptrons http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 32043 WekaMOOC
unstructured datamining of hotel reviews
 
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this a project on dataming for opinion mining analysis of the hotel reviews and generate the new rating model fo hotels.
Views: 365 subash khati
How to Perform K-Means Clustering in R Statistical Computing
 
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In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com
Views: 209475 Influxity
Kernel PCA   - I
 
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Views: 226 Ahmed Fathi
Towards Complex Query Processing over Key-Value Cloud Stores
 
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Facts: Cloud infrastructures bear an ever-increasing responsibility for storing and maintaining massive volumes of data for different types of data-intensive applications. Key-value cloud-stores, have become a premium choice as the storage back-end for such applications. We need complex query processing capability to access/analyze this data. Questions: Do we have adequate solutions required to support complex queries, over data residing in such storage infrastructures? Do standard, ΓÇ£cloud-friendlyΓÇ¥ approaches, such as MapReduce-based algorithms, offer a satisfactory solution? What additional support, in the form of indexing and query processing algorithms, would expedite query processing? Can we do so, while benefiting from the simplicity of the key-value systems' interface and free-ride on their inherent scalability, elasticity, and reliability? Answers: In this talk I will present novel indexing structures and processing algorithms for complex query types. Specifically, I will first cover interval queries in depth, presenting indices and associated query processing algorithms. I will also overview indexing and query processing approaches for rank-join queries. Our contributions include key-value representations of our index and statistical structures, MapReduce algorithms to build and populate them, and query processing algorithms utilizing them, catering to idiosyncrasies of key-value stores, but inheriting their advantages. Our implementation and experimentation are over the popular HBase key-value store. I will report on the results of extensive performance evaluations, which show large performance improvements. En route, I will touch upon differences in existing key-value system architectures and their implications. The talk will conclude with the lessons we have learned, pointing to key design decisions, and promising ideas for outstanding challenges.
Views: 40 Microsoft Research
Predicting Student Success Based on Quantity and Quality of Student Participation
 
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Check out this video about student participation from CampusTech 2014!