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Data Mining & Business Intelligence | Tutorial #3 | Issues in Data Mining
 
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This video addresses the issues which are there involved in Data Mining system. Watch now !
Views: 1198 Ranji Raj
Data mining issues and challenges. By game fan
 
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Describe what is data mining and its issues step by step Game fan Gamefan
Views: 2101 K game fan
Data Mining - Foundations of Learning to Rank: Needs & Challenges | Lectures On-Demand
 
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Ambuj Tewari - EECS at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Views: 3291 Michigan Engineering
Wireless Network, Wireless Sensor Network and Web Mining Research Centres - Sona College
 
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SonaNET (Sona Network Computing Research Centre) focus on Network computing research challenges in the field of Computer Science. SonaNET is dedicated to provide research solutions in the following research domains • Wireless Sensor Network • Wireless Network • Cloud Computing • Cloud Security SONA MINE is to develop pioneering tools and applications applied to database and data mining. To obtain patents for the framework developed. SONA MINE focuses on data mining and addresses that research issues in the following domain • Clinical Analysis • Web Mining • Opinion Mining • Privacy Preservation • Sentiment Analysis Sona College of Technology, Salem, Tamil Nadu, India.
Views: 291 Sona College
Challenges and Issues in various types of Data Mining
 
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Challenges and Issues in various types of Data Mining
15 Hot Trending PHD Research Topics in Data Mining 2018
 
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15 Hot Trending Data Mining Research Topics 2018 1. Medical Data Mining 2. Education Data Mining 3. Data Mining with Cloud Computing 4. Efficiency of Data Mining Algorithms 5. Signal Processing 6. Social Media Analytics 7. Data Mining in Medical Science 8. Government Domain 9. Financial Data Analysis 10. Financial Accounting Fraud Detection 11. Customer Analysis 12. Financial Growth Analysis using Data Mining 13. Data Mining and IOT 14. Data Mining for Counter-Terrorism Key Research Application Fields: • Crisp-DM • Oracle Data mining • Web Mining • Open NN • Data Warehousing • Text Mining WHY YOU NEED TO OUTSOURCE TO PhD Assistance: a) Unlimited revisions b) 24/7 Admin Support c) Plagiarism Generate d) Best Possible Turnaround time e) Access to High qualified technical coordinators and expertise f) Support: Skype, Live Chat, Phone, Email Contact us: India: +91 8754446690 UK: +44-1143520021 Email: [email protected] Visit Webpage: https://goo.gl/HwJgqQ Visit Website: http://www.phdassistance.com
Views: 2408 PhD Assistance
PHD RESEARCH TOPIC IN WEB MINING
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-dependable-secure-computing/
Views: 605 PHD Projects
FDP on Data Mining - Tools and Research Issues by Dr A V KrishnaPrasad
 
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FDP on Data Mining - Tools and Research Issues by Dr A V KrishnaPrasad
Text Mining Problems
 
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I would like to thank Lauren Briggs (Durban, South Africa) and Sean Pethybridge (Surf City, New Jersey) for giving voices to Laura, Saundra and Markus.
Views: 181 Fabio Stella
research paper topics in data mining
 
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Visit Our Website: https://goo.gl/TIo1T2?58204
PHD RESEARCH TOPIC IN DATA MINING
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mobile-networking/
Views: 3915 PHD Projects
Data Mining Trends and Research Frontiers - Kelompok Bo Cuan Gpp
 
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Video Presentasi Data Mining Trends and Research Frontiers Kelompok Bo Cuan Gpp
Views: 607 Ria Liuswani
[OREILLY] Social Web Mining - Github -Conclusion
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 15 Freemium Courses
[OREILLY] Social Web Mining - Github - Constructing Interest Graphs   Part 1
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 12 Freemium Courses
Mining Online Data Across Social Networks
 
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Capturing Data, Modeling Patterns, Predicting Behavior. Capturing Data, Modeling Patterns, Predicting Behavior - Based on collecting more than 20 million blog posts and news media articles per day, Professor Jure Leskovec discusses how to mine such data to capture and model temporal patterns in the news over a daily time-scale --in particular, the succession of story lines that evolve and compete for attention. He discusses models to quantify the influence of individual media sites on the popularity of news stories and algorithms for inferring hidden networks of information flow. Learn more: http://scpd.stanford.edu/
Views: 19865 stanfordonline
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 19881 Red Apple Tutorials
Data Mining and Text Mining with John Elder
 
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Analytics 2014 Conference Keynote Conference John Elder of Elder Research explains the top three challenges of data mining and text mining, and how to solve them. Learn more about Analytics 2014 at http://www.sas.com/analyticsseries/us/
Views: 1149 SAS Software
Semantic Web Mining
 
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Semantic Web Mining by Dr. S Yasodha
Views: 399 Krish eClasses
Data Mining - Three Big Problems in Fundraising | Lectures On-Demand
 
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Ashutosh R. Nandeshwar, Associate Director of Analytics, University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Data Mining - Advanced Research Computing at U-M | Lectures On-Demand
 
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Brock Palen, Senior HPC Systems Administrator - CoE, University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
[OREILLY] Social Web Mining - Github - Constructing Interest Graphs   Part 2
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 6 Freemium Courses
The internet of things and data deluge
 
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Big data is a buzzword for the past few years. A huge amount of the data comes from so-called Internet of Things (IoT) applications which are gathering momentum across many industrial sectors worldwide. Many research problems arise from IoT data, e.g. reliable wireless communication solutions for data collection and delivery, efficient data processing techniques for different industrial applications, quick decision making based on data mining and machine learning, security and privacy. In this lecture, I will discuss some of these issues based on my previous and current research work, with application examples in areas such as smart energy, e-health, and smart city. I will finish by addressing some of the research challenges in our exciting new project called SEND at Keele.
Views: 294 Keele University
[OREILLY] Social Web Mining - Github - Welcome To The Course
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 73 Freemium Courses
Searching and Mining Open Source Code from the Web
 
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Google Tech Talks June, 4 2008 ABSTRACT Various data mining techniques have been applied to mine source code repositories. However, relying only on one or several local source code repositories may not provide sufficient, relevant data samples (e.g., usage of a certain API call) for mining tasks such as code reuse and defect detection. The recent availability of code search engines allows the mining scope to be scaled to billions of lines of open source code available from the Web, and thus increases the chance of getting sufficient, relevant data samples for mining. This talk will discuss the mining opportunities and challenges based on searching open source code from the Web and present new approaches that mine open source code searched from the Web to assist code reuse and defect detection Speaker: Tao Xie Tao Xie is an Assistant Professor in the Department of Computer Science at North Carolina State University. He received his Ph.D. in Computer Science from the University of Washington in 2005. He leads the Automated Software Engineering Research Group at North Carolina State University. His research centers around two major themes: automated software testing and mining software engineering data. He has served on a number of conference program committees including ISSTA 2008/2009, ASE 2006/2007(Expert-Review Panel)/2008, ICST 2008, AOSD 2007, and ICSM 2007/2008. Besides doing research, he has contributed to understanding the software engineering research community by building community webs such as Software Engineering Academic Genealogy and Software Engineering Conference Map.
Views: 5797 GoogleTechTalks
What is Web Mining
 
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Views: 12665 TechGig
The Logic of Data Mining in Social Research
 
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This video is a brief introduction for undergraduates to the logic (not the nitty-gritty details) of data mining in social science research. Four orienting tips for getting started and placing data mining in the broader context of social research are included.
Views: 322 James Cook
PhD research topic in Image Mining
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mobile-cloud-computing/
Views: 788 PHD Projects
[OREILLY] Social Web Mining - Github - Using The GitHub API
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 14 Freemium Courses
Healthcare Data Mining with Matrix Models (Part 1)
 
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Authors: Joel Dudley, Icahn School of Medicine at Mount Sinai Ping Zhang, IBM Thomas J. Watson Research Center Fei Wang, Department of Healthcare Policy and Research, Cornell University Abstract: In the last decade, advances in high-throughput technologies, growth of clinical data warehouses, and rapid accumulation of biomedical knowledge provided unprecedented opportunities and challenges to researchers in biomedical informatics. One distinct solution, to efficiently conduct big data analytics for biomedical problems, is the application of matrix computation and factorization methods such as non-negative matrix factorization, joint matrix factorization, tensor factorization. Compared to probabilistic and information theoretic approaches, matrix-based methods are fast, easy to understand and implement. In this tutorial, we provide a review of recent advances in algorithms and methods using matrix and their potential applications in biomedical informatics. We survey various related articles from data mining venues as well as from biomedical informatics venues to share with the audience key problems and trends in matrix computation research, with different novel applications such as drug repositioning, personalized medicine, and electronic phenotyping. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 646 KDD2016 video
Data Repositories and Web Tools for Data Mining
 
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2015 Network Analysis Short Course - Systems Biology Analysis Methods for Genomic Data Speaker: Giovanni Coppola, UCLA The goal of the network analysis workshop is to familiarize researchers with network methods and software for integrating genomic data sets with complex phenotype data. Students will learn how to integrate disparate data sets (genetic variation, gene expression, epigenetic, protein interaction networks, complex phenotypes, gene ontology information) and use networks for identifying disease genes, pathways and key regulators.
[OREILLY] Social Web Mining - Github - Visualizing Interest Graphs
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 21 Freemium Courses
Algorithmic Bias: From Discrimination Discovery to Fairness-Aware Data Mining (Part 3)
 
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Authors: Carlos Castillo, EURECAT, Technology Centre of Catalonia Francesco Bonchi, ISI Foundation Abstract: Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [Datta et al., 2015]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [Sweeney, 2013]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data. These considerations call for the development of data mining systems which are discrimination-conscious by-design. This is a novel and challenging research area for the data mining community. The aim of this tutorial is to survey algorithmic bias, presenting its most common variants, with an emphasis on the algorithmic techniques and key ideas developed to derive efficient solutions. The tutorial covers two main complementary approaches: algorithms for discrimination discovery and discrimination prevention by means of fairness-aware data mining. We conclude by summarizing promising paths for future research. More on http://www.kdd.org/kdd2016/ KDD2016 conference is published on http://videolectures.net/
Views: 580 KDD2016 video
Web Mining and Social Analytics - Dr. Jaideep Srivastava
 
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With the mass adoption of the Internet in our daily lives, and the ability to capture high resolution data on its use, we are at the threshold of a fundamental shift not only in our understanding of the social and behavioral sciences (i.e. psychology, sociology, and economics), but also the ways in which we study them. Massively Multiplayer Online Games (MMOGs) and Virtual Worlds (VWs) have become increasingly popular and have communities comprising tens of millions. They serve as unprecedented tools to theorize and empirically model the social and behavioral dynamics of individuals, groups, and networks within large communities. The preceding observation has led to a number of multi-disciplinary projects, involving teams of behavioral scientists and computational scientists, working together to develop novel methods and tools to explore the current limits of behavioral sciences. This talk consists of four parts. First, we describe findings from the Virtual World Exploratorium; a multi-institutional, multi-disciplinary project which uses data from commercial MMOGs and VWs to study many fields of social science, including sociology, social psychology, organization theory, group dynamics, macro-economics, etc. Results from investigations into various behavioral sciences will be presented. Second, we provide a survey of new approaches for behavioral informatics that are being developed by multi-disciplinary teams, and their successes. We will also introduce novel tools and techniques that are being used and/or developed as part of this research. Third, we will discuss some novel applications that are not yet there, but are just around the corner, and their associated research issues. Finally, we present commercial applications of Social Analytics research, based on our experiences with a start-up company that we've created.
Views: 379 MOTC QA
Data Mining in Systems Research, Dr. Abhishek Sharma, NEC Labs USA
 
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Dr. Abhishek Sharma tells us how data mining and machine learning are changing the way we analyze the performance of computing systems. He shares the lessons he has learnt doing research and building products that use data mining and machine learning for server log analysis. The video also includes pointers to must-read papers and techniques for researchers interested in this area. www.researchdiaries.org
Views: 432 Omprakash Gnawali
Data Mining - Sociology of Science & Organizations | Lectures On-Demand
 
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Jason Owen Smith The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Mining electronic health records and the web for drug repurposing,  Kira Radinsky (eBay | Technion)
 
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Check out all the Strata Data Conference keynotes, sessions, and tutorials here: https://www.safaribooksonline.com/library/view/strata-data-conference/9781491985373/ Researchers decide on exploratory targets for drug repurposing—the process of applying known drugs in new ways to treat diseases—based on trends in research and observations on small numbers of cases, leading to potentially costly biases of focus and neglect. Kira Radinsky offers an overview of a system that jointly mines 10 years of nationwide medical records of more than 1.5 million people and extracts medical knowledge from Wikipedia to help reduce spurious correlations and provide guidance about drug repurposing. The resulting system seeks to identify potential biological processes to justify potential influences between medications and target diseases via links on a graph constructed from Wikipedia data. Kira shares results of the system on two studies on drug repurposing for hypertension and diabetes. In both cases, the algorithm identified drug families that were previously unknown, and clinical opinion by experts in the field and clinical trials on those drug families suggest that these drugs show promise. Subscribe to O'Reilly on YouTube: http://goo.gl/n3QSYi Follow O'Reilly on: Twitter: http://twitter.com/oreillymedia Facebook: http://facebook.com/OReilly Instagram: https://www.instagram.com/oreillymedia LinkedIn: https://www.linkedin.com/company-beta/8459/
Views: 2888 O'Reilly
[OREILLY] Social Web Mining - Github - Why Mine GitHub
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 19 Freemium Courses
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 233173 Siraj Raval
Ethical Challenges of Using Social Media Data In Research
 
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A talk on the ethical challenges of using social media data in academic research delivered as part of the Bite Size Guide to Research in the 21st Century on the 24th of January, Sheffield, ScHARR. Thanks very much to Andy Tattersall for organising this very great event, and to Dan Smith for editing this video. Read my blog: https://wasimahmed.org/ Follow me on Twitter: https://twitter.com/was3210 Follow me on LinkedIn: https://uk.linkedin.com/in/was3210
Views: 476 Wasim Ahmed
Data Mining - Analysis of Information Needs on Twitter | Lectures On-Demand
 
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Zhe Zhao Student, Computer Science and Engineering - University of Michigan Faculty, School of information - University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Data Mining - Predicting Scientific Impact | lectures On-Demand
 
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Avishay Livne - Graduate Student, Computer Science and Engineering at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Scalability and Efficiency on Data Mining Applied to Internet Applications
 
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Google Tech Talks August 16, 2007 ABSTRACT The Internet went well beyond a technology artefact, increasingly becoming a social interaction tool. These interactions are usually complex and hard to analyze automatically, demanding the research and development of novel data mining techniques that handle the individual characteristics of each application scenario. Notice that these data mining techniques, similarly to other machine learning techniques, are intensive in terms of both computation and I/O, motivating the development of new paradigms, programming environments, and parallel algorithms that support scalable and efficient applications. In this talk we present some results that justify not only the need for developing these new techniques, as well as their parallelization. Wagner Meira Jr. obtained his PhD from the University of Rochester in 1997 and is currently Associate Professor at the Computer Science Department at Universidade Federal de Minas Gerais, Brazil. His research focuses on scalability and efficiency of large scale parallel and distributed systems, from massively parallel to Internet-based platforms, and on data mining algorithms, their parallelization, and application to areas such as information retrieval, bioinformatics, and e-governance. Google engEDU Speaker: Wagner Meira Jr
Views: 384 GoogleTalksArchive
Predicting Stock Prices - Learn Python for Data Science #4
 
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In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 486906 Siraj Raval
An Introduction to Temporal Databases
 
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Check out http://www.pgconf.us/2015/event/83/ for the full talk details. In the past manipulating temporal data was rather ad hoc and in the form of simple solutions. Today organizations strongly feel the need to support temporal data in a coherent way. Consequently, there is an increasing interest in temporal data and major database vendors recently provide tools for storing and manipulating temporal data. However, these tools are far from being complete in addressing the main issues in handling temporal data. The presentation uses the relational data model in addressing the subtle issues in managing temporal data: comparing database states at two different time points, capturing the periods for concurrent events and accessing to times beyond these periods, sequential semantics, handling multi-valued attributes, temporal grouping and coalescing, temporal integrity constraints, rolling the database to a past state and restructuring temporal data, etc. It also lays the foundation in managing temporal data in NoSQL databases as well. Having ranges as a data type PostgresSQL has a solid base in implementing a temporal database that can address many of these issues successfully. About the Speaker Abdullah Uz Tansel is professor of Computer Information Systems at the Zicklin School of Business at Baruch College and Computer Science PhD program at the Graduate Center. His research interests are database management systems, temporal databases, data mining, and semantic web. Dr. Tansel published many articles in the conferences and journals of ACM and IEEE. Dr. Tansel has a pending patent application on semantic web. Currently, he is researching temporality in RDF and OWL, which are semantic web languages. Dr. Tansel served in program committees of many conferences and headed the editorial board that published the first book on temporal databases in 1993. He is also one the editors of the forth coming book titled Recommendation and Search in Social Networks to be published by Springer. He received BS, MS and PhD degrees from the Middle East Technical University, Ankara Turkey. He also completed his MBA degree in the University of Southern California. Dr. Tansel is a member of ACM and IEEE Computer Society.
Views: 4446 Postgres Conference
PhD research topic in big data
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/network-security-research-topics/
Views: 5191 PHD Projects
Tessa Sullivan: Technical Challenges in Classification Analy
 
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Tessa Sullivan looks at the ACM digital library and the current classification scheme. She examines the user interface, changes in classification and grouping, and examines future possibilities for research and datamining. Specifically discussed are the technical challenges involved in getting a complete and useful dataset for research.
Views: 435 UNC-Chapel Hill
Web mining 1
 
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Views: 250 hardi rafat
Prof Ryan Baker, Data Science in Education, Part 2
 
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In this talk, Pof. Baker discusses how the methods of educational data mining draw from broader trends in data science, and some of the problems and methods more specific to education research. Throughout the talk, Baker discusses both the current state of the art in educational data mining, and some of the key research challenges and opportunities for data scientists working in this emerging area.
Views: 156 Shirin Mojarad
GTU Research Week Workshop by Dr. S Nickolas : Data Mining & its Applications
 
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GTU Research Week 2015 Workshop conducted by Dr. S Nickolas on Data Mining & its Applications at Gujarat Technological University, Chandkheda, Ahmedabad
Text/Data Mining Webinar: Supporting Researcher Needs
 
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This webinar explored the complex needs and interests of scholars engaged in text and data mining (TDM), and how librarians can meet those needs. What kinds of knowledge and skills are necessary to effectively support this rapidly growing type of research? How can researchers, eager to gain access to large bodies of primary text and data, benefit from library perspectives on the rights of content providers, available tools, and appropriate methodologies? Speakers: Robert Scott, head of the Digital Humanities Center at Columbia University Libraries Kalev Leetaru, currently the Yahoo! Fellow in Residence of International Values, Communications Technology & the Global Internet at the Institute for the Study of Diplomacy, Georgetown University Robert Scott: 4:48 Kalev Leetaru: 27:35 Q&A: 49:50 CRL News: 1:04:32
Views: 309 CRLdotEDU

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