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Search results โ€œDirected data mining techniques and toolsโ€
Tools Used in Data Visualization ll Data Analytics ll Explained in Hindi
 
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๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Views: 6962 5 Minutes Engineering
Type Of Data Visualization ll Table and Histogram Explained with Examples in Hindi
 
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๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Views: 5752 5 Minutes Engineering
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 135101 nptelhrd
Enterprise Connectors - Social Media Data Mining
 
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This is a replay of the webinar covering using the CData Enterprise Connectors for FireDAC to connect to Twitter and Facebook to mine social media data. The examples are in Delphi, but they could also easily be adaptable for C++Builder too.
Diablo III Datamining Application Demonstration Video 2
 
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Second update to our new tool, titled "Deckard's Data Dump". Questions, comments, etc. can be directed to us at [email protected] Keep an eye out for us on diablo.incgamers!
Views: 9282 Jom Darbert
DATA MINING   1 Data Visualization   3 1 3  Graph Visualization
 
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https://www.coursera.org/learn/datavisualization
Views: 177 Ryo Eng
Techniques and Tools Towards a Data Driven Product at GitHub
 
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JD Maturen, Analytics Lead at GitHub, explains how Github gauges how their product is being used by millions of monthly users with a directed combination of ongoing qualitative and quantitative research. Be sure to subscribe and follow New Relic at: https://twitter.com/NewRelic https://www.facebook.com/NewRelic https://www.youtube.com/NewRelicInc
Views: 1202 New Relic
Turn Data into Knowledge
 
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What if you could harness technology and information more effectively to catapult your business? This video explores how businesses are using the latest tools to advance from data aggregation to digital transformation. The goal? Turning data into knowledge. Enjoy this video from CCC. And if youโ€™d like to learn more about ways to harness enterprise data science to accelerate product time-to-market, and make smarter business decisions, visit www.copyright.com/data to access a new white paper Enterprise Data Science: Transition from the Era of Big Data to the Knowledge Era. Winner of a Silver Telly Award in the category Branded Content: Directing. http://www.tellyawards.com/winners/2018/branded-content/craft-directing Winner of a Bronze Telly Award in the category Branded Content: Use of graphics. http://www.tellyawards.com/winners/2018/branded-content/craft-use-of-graphic
DATA MINING   1 Data Visualization   2 1 2  Mapping
 
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https://www.coursera.org/learn/datavisualization
Views: 112 Ryo Eng
Qualitative and Quantitative research in hindi  | HMI series
 
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For full course:https://goo.gl/J9Fgo7 HMI notes form : https://goo.gl/forms/W81y9DtAJGModoZF3 Topic wise: HMI(human machine interaction):https://goo.gl/bdZVyu 3 level of processing:https://goo.gl/YDyj1K Fundamental principle of interaction:https://goo.gl/xCqzoL Norman Seven stages of action : https://goo.gl/vdrVFC Human Centric Design : https://goo.gl/Pfikhf Goal directed Design : https://goo.gl/yUtifk Qualitative and Quantitative research:https://goo.gl/a3izUE Interview Techniques for Qualitative Research :https://goo.gl/AYQHhF Gestalt Principles : https://goo.gl/Jto36p GUI ( Graphical user interface ) Full concept : https://goo.gl/2oWqgN Advantages and Disadvantages of Graphical System (GUI) : https://goo.gl/HxiSjR Design an KIOSK:https://goo.gl/Z1eizX Design mobile app and portal sum:https://goo.gl/6nF3UK whatsapp: 7038604912
Views: 103616 Last moment tuitions
Moving and Clustering Data with Sqoop and Spark
 
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Efficiently transferring bulk data is an essential Big Data skill. Learn how to cluster dataโˆ’key technique for statistical data analysisโˆ’using Apache Sqoopโ„ข and Apache Sparkโ„ข tp evaluate flu data.
Views: 1356 OracleAcademyChannel
Basics of Social Network Analysis
 
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Basics of Social Network Analysis In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research. The freeware sound tune 'MFF - Intro - 160bpm' by Kenny Phoenix http://www.last.fm/music/Kenny+Phoenix was downloaded from Flash Kit http://www.flashkit.com/loops/Techno-Dance/Techno/MFF_-_In-Kenny_Ph-10412/index.php The video's content includes: Why Social Network Analysis (SNA)? Enables us to segment data based on user behavior. Understand natural groups that have formed: a. topics b. personal characteristics Understand who are the important people in these groups. Analysing Social Networks: Data Collection Methods: a. Surveys b. Interviews c. Observations Analysis: a. Computational analysis of matrices Relationships: A. is connected to B. SNA Introduction: [from] A. Directed Graph [to] B. e.g. Twitter replies and mentions A. Undirected Graph B. e.g. family relationships What is Social Network Analysis? Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)). Social Network Analysis Basics: Node and Edge Node: โ€œactorโ€ or people on which relationships act Edge: relationship connecting nodes; can be directional Social Network Analysis Basics: Cohesive Sub-group Cohesive Sub-group: a. well-connected group, clique, or cluster, e.g. A, B, D, and E Social Network Analysis Basics: Key Metrics Centrality (group or individual measure): a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only). b. Measure at the individual node or group level. Cohesion (group measure): a. Ease with which a network can connect. b. Aggregate measure of shortest path between each node pair at network level reflects average distance. Density (group measure): a. Robustness of the network. b. Number of connections that exist in the group out of 100% possible. Betweenness (individual measure): a. Shortest paths between each node pair that a node is on. b. Measure at the individual node level. Social Network Analysis Basics: Node Roles: Node Roles: Peripheral โ€“ below average centrality, e.g. C. Central connector โ€“ above average centrality, e.g. D. Broker โ€“ above average betweenness, e.g. E. References and Reading Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb.ย American Behavioral Scientist,ย 43(3), pp. 475-492. Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. Inย Collaboration Technologies and Systems (CTS), 2014 International Conference onย IEEE, pp. 3-8. Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters.ย Pew Research Internet Project.
Views: 40416 Alexandra Ott
Type Of Data Visualization ll Line Chart,Area Chart, Pie Chart and Flowchart Explained in Hindi
 
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๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Views: 5436 5 Minutes Engineering
Data mining- Clustering based on Expectation-Maximization (EM) algorithm- PASS MSC PROJECTS
 
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Ph: 0452 4243340; Mobile: 9840992340; http://pandianss.com Pandian Systems and Solutions Pvt Ltd 2nd Floor, No 393 Annanagar Main Road Indian Bank Complex Madurai - 625020, Tamil Nadu India E-Mail: [email protected], [email protected]
Views: 259 Pass Tutors
"Artificial Intelligence with Bayesian Networks" with Dr. Lionel Jouffe
 
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Title: Artificial Intelligence with Bayesian Networks - Data Mining, Knowledge Modeling and Causal Analysis Speaker: Dr. Lionel Jouffe Date: 1/12/2018 Abstract: Probabilistic models based on directed acyclic graphs have a long and rich tradition, beginning with the work of geneticist Sewall Wright in the 1920s. Variants have appeared in many fields. Within Statistics, such models are known as directed graphical models; within Cognitive Science and Artificial Intelligence, such models are known as Bayesian Networks (BNs), a term coined in 1985 by UCLA Professor Judea Pearl to honor the Rev. Thomas Bayes (1702-1761), whose rule for updating probabilities in the light of new evidence is the foundation of the approach. BNs provide an elegant and sound approach to represent uncertainty and to carry out rigorous probabilistic inference by propagating the pieces of evidence gathered on a subset of variables on the remaining variables. BNs are not only effective for representing expertโ€™s belief, uncertain knowledge and vague linguistic representations of knowledge via an intuitive graphical representation, but are also a powerful Knowledge Discovery tool when associated with Machine Learning/Data Mining techniques. In 2004, the MIT Press of Technology (Massachusetts Institute of Technology) classified Bayesian Machine Learning at the 4th rank among the โ€œ10 Emerging Technologies That Will Change Your Worldโ€. Most recently, Judea Pearl, the father of BNs, received the 2012 ACM A.M. Turing Award, the most prestigious award in Computer Science, widely considered the "Nobel Prize in Computer Science," for contributions that transformed Artificial Intelligence, especially for the development of the theoretical foundations for reasoning under uncertainty using BNs. Over the last 25 years, BNs have then emerged as a practically feasible form of knowledge representation and as a new comprehensive data analysis framework. With the ever-increasing computing power, their computational efficiency and inherently visual structure make them attractive for exploring and explaining complex problems. BNs are now a powerful tool for deep understanding of very complex and high-dimensional problem domains. Deep understanding means knowing, not merely how things behaved yesterday, but also how things will behave under new hypothetical circumstances tomorrow. More specifically, a BN allows explicit reasoning, and deliberate reasoning to allow the anticipation of the consequences of actions that have not yet been taken. We will use this 45-minute tutorial for describing what BN are, how we can design these probabilistic expert systems by expertise and how we can use data to automatically machine-learn these models. SPEAKER Dr. Lionel Jouffe, Co-founder and CEO of Bayesia S.A.S. Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has been working in the field of Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development BayesiaLab, which has since emerged as a leading software package for knowledge discovery, data mining and knowledge modeling using Bayesian networks. BayesiaLab enjoys broad acceptance in academic communities as well as in business and industry. MODERATOR Plamen Petrov, Director of Cognitive Technology, KPMG LLP; SIGAI Industry Liaison Officer MODERATOR Rose Paradis, Data Scientist at Leidos Health and Life Sciences; SIGAI Secretary/Treasurer
Prediction of effective rainfall and crop water needs using data mining techniques
 
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Prediction of effective rainfall and crop water needs using data mining techniques- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORKING 1. A Non-Monetary Mechanism for Optimal Rate Control Through Efficient Cost Allocation 2. A Probabilistic Framework for Structural Analysis and Community Detection in Directed Networks 3. A Ternary Unification Framework for Optimizing TCAM-Based Packet Classification Systems 4. Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements 5. Accurate Recovery of Internet Traffic Data: A Sequential Tensor Completion Approach 6. Achieving High Scalability Through Hybrid Switching in Software-Defined Networking 7. Adaptive Caching Networks With Optimality Guarantees 8. Analysis of Millimeter-Wave Multi-Hop Networks With Full-Duplex Buffered Relays 9. Anomaly Detection and Attribution in Networks With Temporally Correlated Traffic 10. Approximation Algorithms for Sweep Coverage Problem With Multiple Mobile Sensors 11. Asynchronously Coordinated Multi-Timescale Beamforming Architecture for Multi-Cell Networks 12. Attack Vulnerability of Power Systems Under an Equal Load Redistribution Model 13. Congestion Avoidance and Load Balancing in Content Placement and Request Redirection for Mobile CDN 14. Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network Architecture 15. Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market 16. Distributed Packet Forwarding and Caching Based on Stochastic NetworkUtility Maximization 17. Dynamic, Fine-Grained Data Plane Monitoring With Monocle 18. Dynamically Updatable Ternary Segmented Aging Bloom Filter for OpenFlow-Compliant Low-Power Packet Processing 19. Efficient and Flexible Crowdsourcing of Specialized Tasks With Precedence Constraints 20. Efficient Embedding of Scale-Free Graphs in the Hyperbolic Plane 21. Encoding Short Ranges in TCAM Without Expansion: Efficient Algorithm and Applications 22. Enhancing Fault Tolerance and Resource Utilization in Unidirectional Quorum-Based Cycle Routing 23. Enhancing Localization Scalability and Accuracy via Opportunistic Sensing 24. Every Timestamp Counts: Accurate Tracking of Network Latencies Using Reconcilable Difference Aggregator 25. Fast Rerouting Against Multi-Link Failures Without Topology Constraint 26. FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks 27. Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services 28. Greenput: A Power-Saving Algorithm That Achieves Maximum Throughput in Wireless Networks 29. ICE Buckets: Improved Counter Estimation for Network Measurement 30. Incentivizing Wi-Fi Network Crowdsourcing: A Contract Theoretic Approach 31. Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks 32. Joint Resource Allocation for Software-Defined Networking, Caching, and Computing 33. Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints 34. Memory-Efficient and Ultra-Fast Network Lookup and Forwarding Using Othello Hashing 35. Minimizing Controller Response Time Through Flow Redirecting in SDNs 36. MobiT: Distributed and Congestion-Resilient Trajectory-Based Routing for Vehicular Delay Tolerant Networks
Introduction to Text Analytics with R: VSM, LSA, & SVD
 
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Part 7 of this video series includes specific coverage of: โ€“ The trade-offs of expanding the text analytics feature space with n-grams. โ€“ How bag-of-words representations map to the vector space model (VSM). โ€“ Usage of the dot product between document vectors as a proxy for correlation. โ€“ Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics. โ€“ How LSA is implemented using singular value decomposition (SVD). โ€“ Mapping new data into the lower dimensional SVD space. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead โ€“ it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: โ€“ Tokenization, stemming, and n-grams โ€“ The bag-of-words and vector space models โ€“ Feature engineering for textual data (e.g. cosine similarity between documents) โ€“ Feature extraction using singular value decomposition (SVD) โ€“ Training classification models using textual data โ€“ Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hD3WT0 Watch the latest video tutorials here: https://hubs.ly/H0hD3X30 See what our past attendees are saying here: https://hubs.ly/H0hD3X90 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 12433 Data Science Dojo
The Basics of Data Classification
 
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The Basics of Data Classification training session with Michele Robinson, California Department of Technology. OIS Training Resources Link https://cdt.ca.gov/security/resources/#Training-Resources
Views: 4641 californiacio
evaluation of predictive data mining algorithms in soil data classification for- IEEE PROJECTS 2018
 
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evaluation of predictive data mining algorithms in soil data classification for optimized crop - IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORKING 1. A Non-Monetary Mechanism for Optimal Rate Control Through Efficient Cost Allocation 2. A Probabilistic Framework for Structural Analysis and Community Detection in Directed Networks 3. A Ternary Unification Framework for Optimizing TCAM-Based Packet Classification Systems 4. Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements 5. Accurate Recovery of Internet Traffic Data: A Sequential Tensor Completion Approach 6. Achieving High Scalability Through Hybrid Switching in Software-Defined Networking 7. Adaptive Caching Networks With Optimality Guarantees 8. Analysis of Millimeter-Wave Multi-Hop Networks With Full-Duplex Buffered Relays 9. Anomaly Detection and Attribution in Networks With Temporally Correlated Traffic 10. Approximation Algorithms for Sweep Coverage Problem With Multiple Mobile Sensors 11. Asynchronously Coordinated Multi-Timescale Beamforming Architecture for Multi-Cell Networks 12. Attack Vulnerability of Power Systems Under an Equal Load Redistribution Model 13. Congestion Avoidance and Load Balancing in Content Placement and Request Redirection for Mobile CDN 14. Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network Architecture 15. Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market 16. Distributed Packet Forwarding and Caching Based on Stochastic NetworkUtility Maximization 17. Dynamic, Fine-Grained Data Plane Monitoring With Monocle 18. Dynamically Updatable Ternary Segmented Aging Bloom Filter for OpenFlow-Compliant Low-Power Packet Processing 19. Efficient and Flexible Crowdsourcing of Specialized Tasks With Precedence Constraints 20. Efficient Embedding of Scale-Free Graphs in the Hyperbolic Plane 21. Encoding Short Ranges in TCAM Without Expansion: Efficient Algorithm and Applications 22. Enhancing Fault Tolerance and Resource Utilization in Unidirectional Quorum-Based Cycle Routing 23. Enhancing Localization Scalability and Accuracy via Opportunistic Sensing 24. Every Timestamp Counts: Accurate Tracking of Network Latencies Using Reconcilable Difference Aggregator 25. Fast Rerouting Against Multi-Link Failures Without Topology Constraint 26. FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks 27. Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services 28. Greenput: A Power-Saving Algorithm That Achieves Maximum Throughput in Wireless Networks 29. ICE Buckets: Improved Counter Estimation for Network Measurement 30. Incentivizing Wi-Fi Network Crowdsourcing: A Contract Theoretic Approach 31. Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks 32. Joint Resource Allocation for Software-Defined Networking, Caching, and Computing 33. Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints 34. Memory-Efficient and Ultra-Fast Network Lookup and Forwarding Using Othello Hashing 35. Minimizing Controller Response Time Through Flow Redirecting in SDNs
Views: 5 Micans Infotech
DAG(direct acyclic graph) in hindi
 
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For Hand Made Notes Visit: https://goo.gl/VNFWyt Full course : https://goo.gl/S9FYDQ Topicwise: Compiler Design Introduction Lecture : https://goo.gl/QWUHLE Assembler and Assembly Language : https://goo.gl/MGrJZc Assembly language statement : https://bit.ly/2G6y9MC https://bit.ly/2G6y9MC : https://bit.ly/2ujoQDt Flow chart of two pass assembler : https://goo.gl/TWLNP8 Macros and Macroprocessors : https://goo.gl/8v39jo Macro vs Subroutine : https://goo.gl/iVhwuw Macros pass 1 and pass 2 flowchart : https://goo.gl/vDAhUw Phases of compiler : https://goo.gl/H4VR9y Eliminate left recursion and left factoring : https://goo.gl/q4HNPE How to Find First and Follow Basics : https://goo.gl/2GKYXT First and Follow solved example : https://goo.gl/cFJm72 Predictive Parser : https://goo.gl/THRXME Predictive Parser part 2 : https://goo.gl/GNM4uG Recursive Descent parser : https://goo.gl/CNCvQ2 Operator Precedence Parser : https://goo.gl/7pSj2Z Operator Precedence Parser part 2 : https://goo.gl/UkGFDn LR Parsing | LR (0) item : https://goo.gl/Uc8RFn SLR (1) parsing : https://goo.gl/2Xk5es Examples of LR(0) or SLR(1) : https://goo.gl/nUjH4R DAG(direct acyclic graph) : https://goo.gl/GVw8Co Editor Basics with Architecture : https://goo.gl/E2ovsA LEX tool full basic concept : https://goo.gl/MKQiP4 Yacc (Yet another compiler compiler ) : https://goo.gl/aX8JPi VIVA: spcc basic concept:https://goo.gl/6nuhJx Forward reference problem and compiler:https://goo.gl/p7o4ts first and follow:https://goo.gl/vSdWkf More videos coming soon so Subscribe kark rakho
Views: 125556 Last moment tuitions
A Fast Clustering Based Feature Subset Selection Algorithm for High Dimensional Data
 
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Title: A Fast Clustering Based Feature Subset Selection Algorithm for High Dimensional Data Domain: Data mining Description: Feature subset selection can be viewed as the process of identifying and removing as many irrelevant and redundant features as possible. This is because irrelevant features do not contribute to the predictive accuracy and redundant features do not redound to getting a better predictor for that they provide mostly information which is already present in other feature(s). Of the many feature subset selection algorithms, some can effectively eliminate irrelevant features but fail to handle redundant features yet some of others can eliminate the irrelevant while taking care of the redundant features. Our proposed FAST algorithm falls into the second group. Traditionally, feature subset selection research has focused on searching for relevant features. A well-known example is Relief which weighs each feature according to its ability to discriminate instances under different targets based on distance-based criteria function. However, Relief is ineffective at removing redundant features as two predictive but highly correlated features are likely both to be highly weighted. Relief-F extends Relief, enabling this method to work with noisy and incomplete data sets and to deal with multiclass problems, but still cannot identify redundant features. Advantages: โ€ข Good feature subsets contain features highly correlated with (predictive of) the class, yet uncorrelated with (not predictive of) each other. โ€ข The efficiently and effectively deal with both irrelevant and redundant features, and obtain a good feature subset. โ€ข Generally all the six algorithms achieve significant reduction of dimensionality by selecting only a small portion of the original features. โ€ข The null hypothesis of the Friedman test is that all the feature selection algorithms are equivalent in terms of run time. For more details contact: E-Mail: [email protected] Purchase The Whole Project Kit for Rs 5000%. Project Kit: โ€ข 1 Review PPT โ€ข 2nd Review PPT โ€ข Full Coding with described algorithm โ€ข Video File โ€ข Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 โ€“ 2018 data mining projects 5. 2017 โ€“ 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 โ€“ 2018 ieee titles 8. 2017 โ€“ 2018 base paper 9. 2017 โ€“ 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 โ€“ 2018 data mining weka projects 13. 2017 โ€“ 2018 b.e projects 14. 2017 โ€“ 2018 m.e projects 15. 2017 โ€“ 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 โ€“ 2018 ieee base paper free download 23. 2017 โ€“ 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
CO-CLUSTERING
 
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Quality and Technology group (www.models.life.ku.dk) LESSONS of CHEMOMETRICS: CO-CLUSTERING This video explains the importance of co-clustering in multivariate data analysis.
Views: 6030 QualityAndTechnology
Turing Lecture: Data science or data humanities? - Melissa Terras
 
01:25:57
Opportunities, barriers, and rewards in digitally-led analysis of history, culture and society About the event What are the opportunities, issues, and rewards for researchers developing data-led approaches to answer research questions in the Arts and Humanities? How can we build and utilise appropriate computational methods for the analysis of our past and present societies? What possibilities and barriers are there in working in this crossover point from data science to the humanities? And how can the humanities contribute to development of data science approaches? From the development of Handwritten Text Recognition for archival material, and the mining of millions of words of historical newspaper archives, this talk will showcase a range of innovative international research projects, whilst also giving pointers on how others can approach this interdisciplinary space successfully. In addition, it will raise issues of how tricky yet rewarding โ€œinterdisciplinary researchโ€ โ€“ which we are all now being encouraged to do โ€“ can be. About the speaker Melissa Terras is the Professor of Digital Cultural Heritage at the University of Edinburghโ€˜s College of Arts, Humanities, and Social Sciences, leading digital aspects of research within CAHSS, and Director of Research in the new Edinburgh Futures Institute. Her research focuses on the use of computational techniques to enable research in the arts, humanities, and wider cultural heritage and information environment that would otherwise be impossible. With a background in Classical Art History and English Literature (MA, University of Glasgow), and Computing Science (MSc IT with distinction in Software and Systems, University of Glasgow), her doctorate (Engineering, University of Oxford) examined how to use image processing and machine learning to interpret and read deteriorated Ancient Roman texts. She is an Honorary Professor of Digital Humanities in UCL Department of Information Studies, where she was employed from 2003-2017, Directing UCL Centre for Digital Humanities from 2013. Books include โ€œImage to Interpretation: An Intelligent System to Aid Historians in Reading the Vindolanda Textsโ€ (2006, Oxford University Press) and and โ€œDefining Digital Humanities: A Readerโ€ (Ashgate 2013) which has been translated into Russian and Chinese. She is a Trustee of the National Library of Scotland, serves on the Board of Curators of the University of Oxford Libraries. is a Fellow of the Chartered Institute of Library and Information Professionals, and Fellow of the British Computer Society. You can generally find her on twitter @melissaterras.
"A Systems Approach to Data Privacy in the Biomedical Domain" (CRCS Lunch Seminar)
 
01:10:06
CRCS Privacy and Security Lunch Seminar (Wednesday, May 13, 2009) Speaker: Bradley Malin Title: A Systems Approach to Data Privacy in the Biomedical Domain Abstract: The healthcare community has made considerable strides in the development and deployment of information systems, with particular gains in electronic health records and cheap genome sequencing technologies. Given the recent passage of the American Recovery and Reinvestment Act of 2009, and the HITECH Act in particular, advancement and adoption of such systems is expected to grow at unprecedented rates. The quantity of patient-level data that will be generated is substantial and can enable more cost-effective care as well as support a host of secondary uses, such biomedical research and biosurveillance. At the same time, care must be taken to ensure that such records are accessed and shared without violating a patient's privacy rights. The construction and application of data privacy technologies in the biomedical domain is a complex endeavor and requires the resolution of often competing computational, organizational, regulatory, and scientific needs. In this talk, I will introduce how the Vanderbilt Health Information Privacy Laboratory builds and applies data privacy solutions to support various biomedical settings. Our solutions are rooted in computational formalisms, but are driven by real world requirements and, as such, draw upon various tools and techniques from a number of fields, including cryptography, databases and data mining, public policy, risk analysis, and statistics. Beyond a high-level overview, I will delve into recent research on how we are measuring and mitigating privacy risks when sharing patient-level data from electronic medical and genomic records from the Vanderbilt University Medical Center to local researchers and an emerging de-identified repository at the National Institutes of Health. Bio: Brad Malin is an Assistant Professor of Biomedical Informatics in the School of Medicine and an Assistant Professor of Computer Science in the School of Engineering at Vanderbilt University. He is the founder and director of the Vanderbilt Health Information Privacy Laboratory (HIPLab), which focuses on basic and applied research in a number of health-related areas, including primary care and secondary sharing of patient-specific clinical and genomic data. His research has received several awards of distinction from the American and International Medical Informatics Associations and the HIPLab is currently supported by grant funding from the National Science Foundation, National Institutes of Health, and Veterans Health Administration. For the past several years, he has directed a data privacy research and consultation team for the Electronic Medical Records and Genomics (eMERGE) project, a consortium sponsored by the National Human Genome Research Institute. He has served as a program committee member and workshop chair for numerous conferences on data mining, privacy, and medical informatics. He has also edited several volumes for Springer Lecture Notes in Computer Science, a special issue for the journal Data and Knowledge Engineering, and is currently on the editorial board of the journal Transactions on Data Privacy. He received a bachelor's in biology (2000), master's in knowledge discovery and data mining (2002), master's in public policy & management (2003), and a doctorate in computation, organizations & society (2006) from the School of Computer Science at Carnegie Mellon University. His home on the web can be found at http://www.hiplab.org/people/malin
Views: 206 Harvard's CRCS
20180709 Tabb 04 Biclustering and Biomarkers
 
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Slides for this talk can be found here: https://drive.google.com/open?id=1D8gNvz8oMJz36MPPEnOqyvuZIJcuM8kD. This series of six short talks on gene expression was designed for the University of Malawi College of Medicine Bioinformatics Training Course (https://bioinformatics.medcol.mw/), supported by EDCTP, BHRTT, and TESAII. This talk focuses on two common uses of gene expression data: biclustering and biomarkers. I start with a quick overview of how clustering works generally. Biclustering is a data mining technique that attempts to group together samples that show similar expression across genes as it attempts to group together genes that show similar expression across samples. I explain that gene expression correlations have been used to extend our functional information about genes to recognize when an uncharacterized gene always comes on and goes off in concert with a well-characterized gene. The second part of the talk attempts to explain the role of biomarkers, explaining at a high level the use of machine learning or statistical learning to identify panels of transcripts for clinical decision making.
Views: 525 David Tabb
What is Text Analytics?
 
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ttp://www.ibm.com/software/data/bigdata/ Big Data Text Analytics defined in 3 minutes with Rafael Coss, manager Big Data Enablement for IBM. This is number twelve and the final episode in this series of 'What is...' videos. Video produced, directed and edited by Gary Robinson, contact robinsg at us.ibm.com Music Track title: Clouds, composer: Dmitriy Lukyanov, publisher:Shockwave-Sound.Com Royalty Free
Views: 8398 IBM Analytics
Genetic Algorithm Vs Traditional Algorithm Explained in Hindi
 
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Views: 3242 5 Minutes Engineering
Introduction to Text Analysis with NVivo 11 for Windows
 
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Itโ€™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: 147564 NVivo by QSR
Improved Code Clone Categorization
 
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Google Tech Talk June 24, 2010 Presented by Dr. Nicholas A. Kraft. ABSTRACT Because 50% to 90% of developer effort during software maintenance is spent on program comprehension activities, techniques and tools that can reduce the effort spent by developers on these activities are required to reduce maintenance costs. One characteristic of a software system that can adversely affect its comprehensibility is the presence of similar or identical segments of code, or code clones. To promote developer awareness of the existence of code clones in a system, researchers recently have directed much attention to the problem of detecting these clones; these researchers have developed techniques and tools for clone detection and have discovered that significant portions of popular software systems such as the Linux kernel are cloned code. However, knowledge of the existence of clones is not sufficient to allow a developer to perform maintenance tasks correctly and completely in the presence of clones. Proper performance of these tasks requires a deep and complete understanding of the relationships among the clones in a system. Thus, new techniques and tools that will assist developers in the analysis of large numbers of clones are a critical need. In this talk I will describe preliminary work on code clone categorization that I am leading at The University of Alabama. In particular, I will describe the development of techniques and tools for categorization of code clones using structural and semantic properties of the clones. Specific research outcomes that we are working towards include: (1) a suite of metrics for measuring the congruence and complementarity of a number of static program representations that capture structural properties of the clones, (2) a process to categorize code clones based on these metrics, and (3) serial and integrated processes that combine structural categorization of code clones and semantic categorization of code clones. Bio: Nicholas A. Kraft is an assistant professor in the Department of Computer Science at The University of Alabama. He received his Ph.D. in computer science from the School of Computing at Clemson University. His research interests are in software engineering and languages, particularly source-code based reverse engineering techniques and tools for software understanding and maintenance. He has published on these topics in IEEE Transactions on Software Engineering, Science of Computer Programming, Information and Software Technology, and the Journal of Systems and Software. His current work is supported by four grants from the National Science Foundation. He has served on the program committees of conferences such as the International Conference on Program Comprehension and the International Conference on Software Language Engineering.
Views: 4680 GoogleTechTalks
Complete Data Science Course | What is Data Science? | 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" 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 move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video 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 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML 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 Naรฏve 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, Naรฏve 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: 50199 edureka!
Glenn Roe: Distant Readings
 
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The challenge of โ€˜Big Dataโ€™ in the humanities has led in recent years to a host of innovative technological and algorithmic approaches to the growing digital human record. These techniquesโ€”from data mining to distant readingโ€”can offer students and scholars new perspectives on the exploration and visualisation of increasingly intractable data sets in both the human and social sciences; perspectives that would have previously been unimaginable. The danger, however, in these kinds of โ€˜macro-analysesโ€™, is that researchers find themselves ever more disconnected from the raw materials of their research, engaging with massive collections of texts in ways that are neither intuitive nor transparent, and that provide few opportunities to apply traditional modes of close reading to these new resources. In this talk, I will outline some of my previous work using data mining and machine learning techniques to explore large-scale data sets drawn primarily from the French Enlightenment period. And, building upon these past experiences, I will then present several of my current research projects, which use sequence alignment algorithms to identify intertextual relationships between authors and texts in the 18th-century โ€˜Republic of Lettersโ€™. By reintroducing the notion of (inter)textuality into algorithmic and data-driven methods of analysis we can move towards bridging the gap between distant and close readings, by way of an intermediary mode of scholarship I term โ€˜directedโ€™ or โ€˜scalableโ€™ reading. Glenn's current research agenda is primarily located at the intersection of new computational approaches with traditional literary and historical research questions. Drawing from diverse domains such as intellectual history, the history of ideas, literary theory, book history, and digital humanities, Glenn is chiefly interested in the idea of โ€˜intertextualityโ€™ as it pertains to various editorial, authorial, and critical practices over the longue durรฉe.
Talks@12: Data Science & Medicine
 
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Innovations in ways to compile, assess and act on the ever-increasing quantities of health data are changing the practice and police of medicine. Statisticians Laura Hatfield and Sherri Rose will discuss recent methodological advances and the impact of big data on human health. Speakers: Laura Hatfield, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Sherri Rose, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Like Harvard Medical School on Facebook: https://goo.gl/4dwXyZ Follow on Twitter: https://goo.gl/GbrmQM Follow on Instagram: https://goo.gl/s1w4up Follow on LinkedIn: https://goo.gl/04vRgY Website: https://hms.harvard.edu/
Andreas Weigend interviewed at Strata Jumpstart 2011
 
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Andreas Weigend (PhD Physics 1991, Stanford University; Diplom Physik, Philosophie 1986, Universitaet Bonn) is an Associate Professor of Information Systems at the Stern School of Business, New York University. He came to Stern from the University of Colorado at Boulder where he had founded the Time Series Analysis Group, after working at Xerox PARC (Palo Alto Research Center) on knowledge discovery. At the Santa Fe Institute, he co-directed the Time Series Prediction Competition that led to the volume Time Series Prediction: Forecasting the Future and Understanding the Past (1994, Addison Wesley). His research focuses on basic methodologies for modeling and extracting knowledge from data and their application across different disciplines. He develops and integrates ideas and analytical tools from statistics and information theory with neural networks and other machine learning paradigms. His approach, basic science on real problems, emphasizes the importance of rigorous evaluations of new methods in data mining. His recent work uses computational intelligence to extract and understand hidden states in financial markets, and to exploit this information to improve density predictions. He has published about one hundred articles in scientific journals, books and conference proceedings. He co-edited four books including Decision Technologies for Financial Engineering (1998, World Scientific). Prof. Weigend received a Research Initiation Award by the National Science Foundation (NSF), a major grant by the Air Force Office of Scientific Research (AFOSR), a Junior Faculty Development Award by the University of Colorado and a NYU Curricular Development Challenge Grant for his innovative course Data Mining in Finance. This course covers the foundations of knowledge discovery, data mining, prediction and nonlinear modeling, as well as specific techniques including neural networks, graphical models, evolutionary programming and clustering techniques. It develops solutions to current problems in finance and includes integrated in-depth projects with major Wall Street firms. Prof. Weigend organized the sixth international conference Computational Finance that took place at Stern on January 6-8, 1999, drawing more than 300 attendees. He has given tutorials and short executive courses on time series analysis, volatility prediction, nonlinear modeling, risk management and decision making under uncertainty and consulted for a broad spectrum of firms ranging from financial boutiques to Goldman Sachs, J. P. Morgan, Morgan Stanley and Nikko Securities.
Views: 495 O'Reilly
Social Media Analysis Using Optimized K-Means Clustering
 
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Social Media Analysis Using Optimized K-Means Clustering -IEEE PROJECTS 2017-2018 MICANS INFOTECH PVT LTD, CHENNAI ,PONDICHERRY http://www.micansinfotech.com http://www.finalyearprojectsonline.com http://www.softwareprojectscode.com +91 90036 28940 ; +91 94435 11725 ; [email protected] Download [email protected] http://www.micansinfotech.com/VIDEOS.html Abstract: The increasing influence of social media and enormous participation of users creates new opportunities to study human social behavior along with the capability to analyze large amount of data streams. One of the interesting problems is to distinguish between different kinds of users, for example users who are leaders and introduce new issues and discussions on social media. Furthermore, positive or negative attitudes can also be inferred from those discussions. Such problems require a formal interpretation of social media logs and unit of information that can spread from person to person through the social network. Once the social media data such as user messages are parsed and network relationships are identified, data mining techniques can be applied to group different types of communities. However, the appropriate granularity of user communities and their behavior is hardly captured by existing methods. In this paper, we present a framework for the novel task of detecting communities by clustering messages from large streams of social data. Our framework uses K-Means clustering algorithm along with Genetic algorithm and Optimized Cluster Distance (OCD) method to cluster data. The goal of our proposed framework is twofold that is to overcome the problem of general K-Means for choosing best initial centroids using Genetic algorithm, as well as to maximize the distance between clusters by pairwise clustering using OCD to get an accurate clusters. We used various cluster validation metrics to evaluate the performance of our algorithm. The analysis shows that the proposed method gives better clustering results and provides a novel use-case of grouping user communities based on their activities. Our approach is optimized and scalable for real-time clustering of social media data.
BigDataX: Random graph and scale-free graph models
 
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Big Data Fundamentals is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn how big data is driving organisational change and essential analytical tools and techniques including data mining and PageRank algorithms. Enrol now! http://bit.ly/2rg1TuF
Visual Exploration of Market Basket Analysis with JMP
 
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Association rules are a popular data mining technique for exploring relationships in databases. These rules use a variety of algorithms and attempt to identify strong rules or associations among variables. One example is the classic market basket case, which finds that when bread and cheese are purchased, wine is more often purchased. Rules can also easily serve as supervised learning algorithms by directing that one element be a target variable of interest. JMP does not include association rule methods -- but does offer connectivity and flexibility, in addition to great interactive visualization tools. This presentation, by Matthew Flynn, PhD, Marketing Manager at Aetna, demonstrates that strength by connecting JMP to other software tools -- such as SASยฎ Enterprise Minerโ„ข, open-source R, Weka and (now with JMP 11) MATLAB -- to access association rules methods and enliven them by visually exploring the generated rule results in JMP. This presentation was recorded at Discovery Summit 2013 in San Antonio, Texas.
Views: 2786 JMPSoftwareFromSAS
Scalable Learning of Graphical Models (Part 1)
 
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Authors: Geoff Webb, Faculty of Information Technology, Monash University Franรงois Petitjean, Faculty of Information Technology, Monash University Abstract: From understanding the structure of data, to classification and topic modeling, graphical models are core tools in machine learning and data mining. They combine probability and graph theories to form a compact representation of probability distributions. In the last decade, as data stores became larger and higher-dimensional, traditional algorithms for learning graphical models from data, with their lack of scalability, became less and less usable, thus directly decreasing the potential benefits of this core technology. To scale graphical modeling techniques to the size and dimensionality of most modern data stores, data science researchers and practitioners now have to meld the most recent advances in numerous specialized fields including graph theory, statistics, pattern mining and graphical modeling. This tutorial covers the core building blocks that are necessary to build and use scalable graphical modeling technologies on large and high-dimensional data. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 220 KDD2016 video
Analyzing Social Networks with Python
 
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Maksim Tsvetovat, Alex Kouznetsov, Jacqueline Kazil Social Network data is not just Twitter and Facebook - networks permeate our world - yet we often don't know what to do with them. In this tutorial, we will introduce both theory and practice of Soc
Views: 1286 Next Day Video
Strict Schedule ll DBMS ll Explained with Examples in Hindi
 
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๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Nuclear density gauge
 
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A nuclear density gauge is a tool used in civil construction and the petroleum industry, as well as for mining and archaeology purposes. It consists of a radiation source that emits a directed beam of particles and a sensor that counts the received particles that are either reflected by the test material or pass through it. By calculating the percentage of particles that return to the sensor, the gauge can be calibrated to measure the density and inner structure of the test material. Different variants are used for different purposes. For density analysis of very shallow objects such as roads or walls, a gamma source emitter such as 137Cesium is used to produce gamma radiation. These isotopes are effective in analyzing the top 10 inches with high accuracy. 226Radium is used for depths of 328 yards. Such instruments can help find underground caves or identify locations with lower density that would make tunnel construction hazardous. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 2254 Audiopedia
Blink - an online 3D graph visualization tool and database for brain networks
 
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Blink (http://blink.neuromia.org) is an online 3D graph visualization tool and database for brain networks (functional and structural). Every brain network has its own identifier so that it can be easily shared with the scientific community. The visualization can be easily customized and also exported to a printable image. Networks can be uploaded using the web interface or by a provided desktop tool (for batch uploads). This is a preview version of Blink for the BRAIN-ART COMPETITION 2013 with one network and a demo mode. I hope you will like it. The full website will be online in late summer.
Views: 4897 Kai Schlamp
Dense Subgraph Discovery - Part 2
 
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Authors: Aristides Gionis, Charalampos E. Tsourakakis Abstract: Finding dense subgraphs is a fundamental graph-theoretic problem, that lies in the heart of numerous graph-mining applications, ranging from finding communities in social networks, to detecting regulatory motifs in DNA, and to identifying real-time stories in news. The problem of finding dense subgraphs has been studied extensively in theoretical computer science, and recently, due to the relevance of the problem in real-world applications, it has attracted considerable attention in the data-mining community. In this tutorial we aim to provide a comprehensive overview of (i) major algorithmic techniques for finding dense subgraphs in large graphs and (ii) graph mining applications that rely on dense subgraph extraction. We will present fundamental concepts and algorithms that date back to 80's, as well as the latest advances in the area, from theoretical and from practical point-of-view. We will motivate the problem of finding dense subgraphs by discussing how it can be used in real-world applications. We will discuss different density definitions and the complexity of the corresponding optimization problems. We will also present efficient algorithms for different density measures and under different computational models. Specifically, we will focus on scalable streaming, distributed and MapReduce algorithms. Finally we will discuss problem variants, extensions, and will provide pointers for future research directions. ACM DL: http://dl.acm.org/citation.cfm?id=2789987 DOI: http://dx.doi.org/10.1145/2783258.2789987
Basics Of Syntax Analysis ll Explained With Example in Hindi ll Compiler Design Course
 
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๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Views: 1488 5 Minutes Engineering
Finding Correlated Biclusters from Gene Expression Data
 
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Title: Finding correlated biclusters from gene expression data Domain: Matlab Description: Extracting biologically relevant information from DNA microarrays is a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been proposed for the analysis of gene expression data, but when analyzing the large and heterogeneous collections of gene expression data, conventional clustering algorithms often cannot produce a satisfactory solution. Biclustering algorithm has been presented as an alternative approach to standard clustering techniques to identify local structures from gene expression data set. These patterns may provide clues about the main biological processes associated with different physiological states. In this paper, different from existing bicluster patterns, we first introduce a more general pattern: correlated bicluster, which has intuitive biological interpretation. Then, we propose a novel transform technique based on singular value decomposition so that identifying correlated-bicluster problem from gene expression matrix is transformed into two global clustering problems. The Mixed-Clustering algorithm and the Lift algorithm are devised to efficiently produce-corBiclusters. The biclusters obtained using our method from gene expression data sets of multiple human organs and the yeastSaccharomyces cerevisiaedemonstrate clear biological meanings. We are providing โ€ข 1 Review PPT โ€ข 2nd Review PPT โ€ข Full Coding with described algorithm โ€ข Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Mat lab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Contact for more details: Ph:044-43548566 Mob:8110081181 Mail id:[email protected]
Views: 396 SHPINE TECHNOLOGIES
Visualizing Social Data
 
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This video is a class for COMM 2450. I used NetVizz (https://apps.facebook.com/netvizz/) to generate the data for the graph from Facebook, and used Gephi (https://gephi.org) to manipulate the graph visualization. The graph contains ~1,000 nodes and ~50,000 edges. The video is influenced by Christakis & Fowler's "Politically Connected", which shows various graph clustering visualizations, including the famous one of the political blogosphere of the United States.
Views: 637 Debarghya Das