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Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
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** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 36376 edureka!
Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka
 
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** Natural Language Processing Using Python: https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry. NLP Tutorial : https://www.youtube.com/watch?v=05ONoGfmKvA Subscribe to our channel to get video updates. Hit the subscribe button above. ------------------------------------------------------------------------------------------------------- #NLPin10minutes #NLPtutorial #NLPtraining #Edureka Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ ------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learned content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 38643 edureka!
Strategies for effective learning outcomes with students new to text mining and text analysis
 
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Kelvin Smith Library 2014 Digital Scholarship Colloquium Strategies for effective learning outcomes with students new to text mining and text analysis Mace Mentch, Consultant, Instructional Design and Technology 11/6/2014 Depending on the source, it has been estimated that 80% of existing data is in the form of unstructured text. The processes and methods used to transform unstructured textual data into structured data through turning the text into numbers and then back into text to discover relationships and create knowledge is complex. This presentation will cover methods derived from instructional systems design that serve to effectively facilitate student learning outcomes for the text mining and text analysis process. Colloquium website: http://library.case.edu/ksl/freedmancenter/colloquium/2014colloquium/
Views: 79 case
Tricks, tips and topics in Text Analysis - Bhargav Srinivasa Desikan
 
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PyData Amsterdam 2018 There is an abundance of easily mineable text data (Whatsapp, twitter, and even our own e-mails!), and we have no excuse to not analyse it. In this workshop, we will learn some tips and tricks to deal with messy text data, before moving on to some lesser looked at text analysis techniques, such as text summarisation, working with distance metrics, and an old personal favorite - topic models. Slides: https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial -- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1329 PyData
Introduction to Text Analysis
 
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Presented by Tassie Gniady and David Kloster. Abstract: This workshop covers the basics of text analysis from cleaning text from different corpora (Star Trek scripts, social media, & scientific abstracts) to generating simple visualizations such as wordclouds. 2019 Supercomputing for Everyone: Services for Digital Humanities and Creative Activities workshop series at Indiana University. Hosted by Tassie Gniady, University Information Technology Services Research Technologies. This informational series will show you all the services UITS has to offer, from 3D object acquisition to virtual reality for teaching and art education, presented by a host of experts from the UITS Advanced Visualization Lab and Cyberinfrastructure for Digital Humanities & Creative Activities group. The format of the weekly series at IU Bloomington includes hands-on introductions to topics, lectures by people doing cutting-edge work at IU, and open sessions for exploring your questions and approaches. The Supercomputing for Everyone Series (S4ES) of training workshops aims to bring more users into the realm of advanced computing, whether it be visualization, computation, analytics, storage, or any related discipline. Filmed Jan 17, 2019. For more information about services for digital humanities and creative activities, visit https://rt.iu.edu
Views: 12 IU_PTI
How good are humans at text analysis?
 
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Accuracy is a measure of the capability of technology to interpret a text correctly. Some companies claim the accuracy of their technology is 99%, is that even possible? In today's video, we are going to refute some myths about text analysis, and we are going to discover the best way to achieve a high level of accuracy. Please let me know what you think by leaving a comment on this video. Thank you for watching and subscribe to receive a notification when I post a new one. #TextAnalysis #DataAnalysis #Consumer Centricity ======================================================= Subscribe to my channel here * https://bit.ly/2VSoXiY Visit our company website * https://www.wonderflow.co/ You can also find me on LinkedIn * https://www.linkedin.com/in/riccardoosti/
Views: 232 Riccardo Osti
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 53907 Well Academy
Why You Should Do Text Analysis in Python (Even if You Don't Want to) - Bhargav Srinivasa Desikan
 
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PyData LA 2018 The explosion in Artificial Intelligence and Machine Learning is unprecedented now - and text analysis is likely the most easily accessible and understandable part of this. And with python, it is crazy easy to do this - python has been used as a parsing language forever, and with the rich set of NLP, ML and Computational Linguistic tools, it's worth doing text analysis even if you don't want to. --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 962 PyData
basic text an video tutorial
 
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Basic text analysis shiny app - how to run instructions, for my business and analytics students.
Views: 183 Sudhir Voleti
Chicago Summit 2018 - Create Advanced Text Analytics Solutions with NLP
 
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Learn more about Amazon Comprehend at - https://amzn.to/2Lcbrjz. About 80% of the data an organization holds is unstructured, which makes it difficult to analyze and use. Examples of unstructured data include emails, social media feeds, news articles, and customer feedback. NLP and ML can help. Amazon Comprehend is an NLP service that uses ML to find insights and relationships in text. In this session, we learned how to easily process, analyze, and visualize data by combining Amazon Comprehend with Amazon RDS, Amazon Elasticsearch Service, and Amazon Neptune. Also see real-world examples of how customers have built advanced text analytics solutions with Amazon Comprehend.
Views: 339 Amazon Web Services
Information extraction made easy by Text Mining Solutions
 
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Information extraction brought to you by Text Mining Solutions we explain the process of text mining in 3 easy to understand steps. 1. Organise your input documents. 2. Processing your documents. 3. Analyse your results. This video is perfect for anyone new to Text Mining. For more information go to http://www.textminingsolutions.co.uk Follow Text Mining Solutions on: Facebook: https://www.facebook.com/TextMiningSolutions?fref=ts Twitter: https://twitter.com/Txt_Mining LinkedIn: https://www.linkedin.com/company/text-mining-solutions Music: http://www.purple-planet.com
Views: 497 TxtMining
Tourist behavior analysis using social media and text analytics
 
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Tool created in order to analyze tourist behavior using social media. With this tool it is possible to correctly identify where tourists came from and where they go, also if they liked the foreign country using text analytics. It is also possible to get tourist spending patterns (how much they spend).
Views: 1097 João Ladeira
4. Text Mining Webinar - Transformation
 
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This is the fourth part of the text Mining Webinar recorded on October 30 2013 (https://www.youtube.com/edit?o=U&video_id=tY7vpTLYlIg). This part is about transformation of a document into a list of terms for further data analytics and text analytics tasks.
Views: 1325 KNIMETV
Text mining for ontology learning and matching
 
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http://togotv.dbcls.jp/20141117.html NBDC / DBCLS BioHackathon 2014 was held in Tohoku Medical Megabank in Sendai and Taikanso in Matsushima, Miyagi, Japan. Main focus of this BioHackathon is the standardization and utilization of human genome information with Semantic Web technologies in addition to our previous efforts on semantic interoperability and standardization of bioinformatics data and Web services. (read more about the past hackathons...) On the first day of the BioHackathon (Nov. 9), public symposium of the BioHackathon 2014 was held at Tohoku Medical Megabank in Sendai. In this talk, Jung-Jae Kim (Nanyang Technological University, Singapore) makes a presentation entitled "Text mining for ontology learning and matching". (16:09)
Views: 1978 togotv
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: 4580 PHD Projects
Analyzing Text using BigInsights Text Analytics Web Tooling Skill Builder
 
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This video will demonstrate how to use the text analytics module in BigInsights to extract contextual information from text documents. Course Links: BD085EN - Text Analytics with AQL programming (Big Data University): http://bigdatauniversity.com/bdu-wp/bdu-course/text-analytics-essentials/ DW653 - BigInsights Analytics for Programmers: http://www.ibm.com/services/learning/ites.wss/zz/en?pageType=course_description&courseCode=DW653G&cc= Other Links: Training Paths: http://www.ibm.com/services/learning/ites.wss/zz/en?pageType=page&c=a0003096 BigInsights Training Path: http://www.ibm.com/services/learning/ites.wss/zz/en?pageType=page&c=P869090H78414O98
Views: 1560 IBM Analytics Skills
INTRODUCTION TO TEXT MINING
 
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INTRODUCTION TO TEXT MINING
Views: 447 LearnEveryone
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 111305 LearnEveryone
Performing Sentiment Analysis
 
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Ever wonder how to analyze people’s words to gauge sentiment? In this video, we’ll show you how! You’ll learn: - How to gather sentiment data - How to clean and structure it - How to perform the analysis in Tableau
Views: 9001 Tableau Software
SPS2017: Educational Data Mining Software
 
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The video is giving details about research software developed using WEKA (Open source Data Mining tool) and JAVA (Programming Language). The first version is developed in 2017. Anyone having the link can download this software and directly use this software without any installation. All the instructions are given in 'README.txt' file in a downloaded zip folder. The link to download the setup will be provided on request. Any suggestions and questions are invited in the comment section below. Feel free to add below. Developer: Er. Prabhjot Kaur Music Credits: Youtube Audio Library
Views: 86 Prabhjot Kaur
Machine Learning with Text in scikit-learn (PyCon 2016)
 
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Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn. (Presented at PyCon on May 28, 2016.) GitHub repository: https://github.com/justmarkham/pycon-2016-tutorial Enroll in my online course: http://www.dataschool.io/learn/ == OTHER RESOURCES == My scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A My pandas video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y == LET'S CONNECT! == Newsletter: https://www.dataschool.io/subscribe/ Twitter: https://twitter.com/justmarkham Facebook: https://www.facebook.com/DataScienceSchool/ LinkedIn: https://www.linkedin.com/in/justmarkham/ YouTube: https://www.youtube.com/user/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool
Views: 86573 Data School
Mining Twitter with Python : 6 - Analyzing tweets - text analysis
 
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This tutorial will focus on unstructured data, that is, the raw text of the text. We'll discuss aspects of text analysis such as text preprocessing and normalization. We will also perform some statistical analysis on the tweets. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 1605 Sukhvinder Singh
Text Mining and Analytics | DelftX on edX | Course About Video
 
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Take this course on edX: https://www.edx.org/course/text-mining-analytics-delftx-txt1x#! ↓ More info below. ↓ Follow on Facebook: https://www.facebook.com/edX Follow on Twitter: https://www.twitter.com/edxonline Follow on YouTube: https://www.youtube.com/user/edxonline About this course The knowledge base of the world is rapidly expanding, and much of this information is being put online as textual data. Understanding how to parse and analyze this growing amount of data is essential for any organization that would like to extract valuable insights and gain competitive advantage. This course will demonstrate how text mining can answer business related questions, with a focus on technological innovation. This is a highly modular course, based on data science principles and methodologies. We will look into technological innovation through mining articles and patents. We will also utilize other available sources of competitive intelligence, such as the gray literature and knowledge bases of companies, news databases, social media feeds and search engine outputs. Text mining will be carried out using Python, and could be easily followed by running the provided iPython notebooks that execute the code. FAQ Who is this course for? The course is intended for data scientists of all levels as well as domain experts on a managerial level. Data scientists will receive a variety of different toolsets, expanding knowledge and capability in the area of qualitative and semantic data analyses. Managers will receive hands-on oversight to a high-growth field filled with business promise, and will be able to spot opportunities for their own organization. You are encouraged to bring your data sources and business questions, and develop a professional portfolio of your work to share with others. The discussion forums of the course will be the place where professionals from around the world share insights and discuss data challenges. How will the course be taught? The first week of the course describes a range of business opportunities and solutions centered around the use of text. Subsequent weeks identify sources of competitive intelligence, in text, and provide solutions for parsing and storing incoming knowledge. Using real-world case studies, the course provides examples of the most useful statistical and machine learning techniques for handling text, semantic, and social data. We then describe how and what you can infer from the data, and discuss useful techniques for visualizing and communicating the results to decision-makers. What types of certificates does DelftX offer? Upon successful completion of this course, learners will be awarded a DelftX Professional Education Certificate. Can I receive Continuing Education Units? The TU Delft Extension School offers Continuing Education Units for this course. Participants of TXT1x who successfully complete the course requirements will earn a Certificate of Completion and are eligible to receive 2.0 Continuing Education Units (2.0 CEUs) How do I receive my certificate and CEUs? Upon successful completion of the course, your certificate can be printed from your dashboard. The CEUs are awarded separately by the TU Delft Extension School. ------- LICENSE The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.
Views: 2996 edX
Mining Social Media Data for Understanding Students’ Learning Experiences
 
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Abstract—Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences—opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi-label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from about 35,000 tweets streamed at the geo-location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences. Index Terms—Education, computers and education, social networking, web text analysis
Finding What to Read  Visual Text Analytics Tools and Techniques - Christopher Collins
 
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Abstract: Text is one of the most prominent forms of open data available, from social media to legal cases. Text visualizations are often critiqued for not being useful, for being unstructured and presenting data out of context (think: word clouds). In this talk, Chris will argue that we should not expect them to be a replacement for reading. He will briefly discuss the close/distant reading debate then focus on where text visualization can be useful: hypothesis generation and guiding investigation. Text visualization can help someone form questions about a large text collection, then drill down to investigate through targeted reading of the underlying source texts. Chris will also discuss the design challenges which, while common across visualization, are particularly important with text (e.g. legibility, label fitting, finding appropriate levels of ‘zoom’) as well as what are interesting open challenges in this field. About our Speaker: Christopher Collins is the Canada Research Chair in Linguistic Information Visualization and an Assistant Professor of Computer Science at the University of Ontario Institute of Technology (UOIT). His research focus is interdisciplinary, combining information visualization and human-computer interaction with natural language processing to address the challenges of information management and the problems of information overload. His work has been published in many venues including IEEE Transactions on Visualization and Computer Graphics, and has been featured in popular media such as the Toronto Star and the New York Times Magazine. Collins received his Ph.D. in Computer Science from the University of Toronto. More about Chris: http://vialab.science.uoit.ca/
Views: 614 BocoupLLC
Predictive Analytics in Higher Education
 
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The volume, velocity and variety of data Higher Education Institutions generate is rapidly increasing. SEAtS harnesses and analyses this data to provide powerful insights.
Views: 138 SEAtS Software
Natural Language Processing in Artificial Intelligence in Hindi | NLP Easy Explanation
 
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Hello Friends Welcome to Well Academy In this video i am Explaining Natural Language Processing in Artificial Intelligence in Hindi and Natural Language Processing in Artificial Intelligence is explained using an Practical Example which will be very easy for you to understand. Artificial Intelligence lectures or you can say tutorials are explained by Abdul Sattar Another Channel Link for Interesting Videos : https://www.youtube.com/channel/UCnKlI8bIoRdgzrPUNvxqflQ Google Duplex video : https://www.youtube.com/watch?v=RPOAz48uEc0 Sample Notes Link : https://goo.gl/KY9g2e For Full Notes Contact us through Whatsapp : +91-7016189342 Form For Artificial Intelligence Topics Request : https://goo.gl/forms/suL3639o2TG8aKkG3 Artificial Intelligence Full Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fug7z_YlD9M0x8gdZ7ziXen DBMS Gate Lectures Full Course FREE Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fs6JyD7FFw9Ou1u601tev2D Computer Network GATE Lectures FREE playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fsO-ui9r_pmuDC3d2Oh9wWy Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 59694 Well Academy
Sentiment Analysis
 
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Welcome to Data Lit! This 3-month course is an intro to data science for beginners. In this video, I'll explain how a popular data science technique called sentiment analysis works using a real-world scenario. We'll play the role of a data scientist working at a startup making a personal healthcare device. Using sentiment analysis, we'll understand how consumers feel about a competitors product. That'll help us make decisions on how to promote our own product, and what feature we can focus on the most. Using Python, Twitter, and Google Colab, anyone can do this process in just a few minutes. Enjoy! Code for this video: https://github.com/llSourcell/Sentiment_Analysis Please Subscribe! And Like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Join us at the School of AI: https://theschool.ai/ More learning resources: https://towardsdatascience.com/sentiment-analysis-with-python-part-1-5ce197074184 https://www.geeksforgeeks.org/twitter-sentiment-analysis-using-python/ https://www.datacamp.com/community/tutorials/simplifying-sentiment-analysis-python https://www.kaggle.com/ngyptr/python-nltk-sentiment-analysis https://pythonspot.com/python-sentiment-analysis/ https://www.analyticsvidhya.com/blog/2018/07/hands-on-sentiment-analysis-dataset-python/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w #DataLit #SchoolOfAI #SirajRaval Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 41074 Siraj Raval
Applying the four step "Embed, Encode, Attend, Predict" framework to predict document similarity
 
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Description This presentation will demonstrate Matthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build Deep Neural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep Learning Library. Abstract A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 6402 PyData
Lev Konstantinovskiy - Text similiarity with the next generation of word embeddings in Gensim
 
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Description What is the closest word to "king"? Is it "Canute" or is it "crowned"? There are many ways to define "similar words" and "similar texts". Depending on your definition you should choose a word embedding to use. There is a new generation of word embeddings added to Gensim open source NLP package using morphological information and learning-to-rank: Facebook's FastText, VarEmbed and WordRank. Abstract There are many ways to find similar words/docs with an open-source Natural Language processing library Gensim that I maintain. I will give an overview of modern word embeddings like Google's Word2vec, Facebook's FastText, GloVe, WordRank, VarEmbed and discuss what business tasks fit them best. What is the most similar word to "king"? It depends on what you mean by similar. "King" can be interchanged with "Canute", but it's attribute is "crown". We will discuss how to achieve these two kinds of similarity from word embeddings. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 7790 PyData
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
 
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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1554187 ExcelIsFun
Mining data on Facebook with Python: 1- Setting up our app for mining data on Facebook
 
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In this tutorial we will set up our app to mine data from Facebook. We will be introduces to the Facebook API Graph and setting up user token access. Let's connect out app to communicate with the Graph API to get started mining data on this huge platform. ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 17689 Sukhvinder Singh
Analytics in higher education
 
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Find out how analytics can help you make sense of data and stay one step ahead. From attracting more quality applications, improving graduation results and becoming a centre of research excellence to increasing revenue year on year, data – and intelligent analytics from that data - will give you the insight you need to make a difference. Watch our video to discover more. Visit : http://www.caci.co.uk/technology-solutions/higher-education or call +442076026000
Views: 1083 CACI
Sentiment Analysis
 
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This video shows how minute survey and text classification programs (sentiment analysis) can detect service problems within an educational program. This analysis relies on data taken from the web and is focused on health administration programs. These data were analyzed as part of course on health information systems at Georgetown University. This course was taught by Farrokh Alemi, Ph.D.
Views: 241 rachaelpiltch1
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
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There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
Automatic tagging of short texts with scikit-learn and NLTK - Gilbert François Duivesteijn
 
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PyData London 2018 Automatic tagging of short text messages with NLTK and scikit-learn, applicable to all kind of short messages, like email subjects, tweets, or as demonstrated in this hands-on tutorial, Slack messages. The tutorial will show step by step how to do automated tagging of short texts, enabling the analyst to structure the data and get meaningful statistics. --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1993 PyData
TF/IDF
 
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Full course: https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/?couponCode=DATATUBE We'll introduce the concept of TF-IDF (Term Frequency / Inverse Document Frequency) and how it applies to search problems, in preparation for using it with MLLib.
SME and Mining: Navigating the Global Waters
 
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Mining is a rapidly changing field, meaning we have the opportunity right now to define the mine of the future. Sensors, robotic technologies, vast amounts of data, a growing emphasis on safety and sustainability, plus a multi-generational change in the workforce make this an exciting time to be in one of the world's oldest industries. Society for Mining, Metallurgy & Exploration (SME) provides a platform for a robust and diverse discussion about this evolving field and its associated careers. Youth, technology and education are coming together to define the future of mining - which in turn will define the future of our society. Learn what SME is doing to support that future at http://www.SMEnet.org and find our free educational materials to teach others the importance of mining at http://www.MineralsEducationCoalition.org.
Views: 1718 SMESocietyForMining
APPLICATION OF BIG DATA IN EDUCATION DATA MINING
 
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APPLICATION OF BIG DATA IN EDUCATION DATA MINING
Views: 312 Chennai Sunday
Text Mining: NGram Word Frequency in R
 
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Using R, you can see what how often words occur in an aggregated data set. It is often used in business for text mining of notes in tickets as well as customer surveys. Using a Corpus and TermDocumentMatrix in R we can organize the data accordingly to extract the most common word combos. Direct File: https://github.com/ProfessorPitch/ProfessorPitch/blob/master/R/NGram%20Wordcloud.R Software Versions: R 3.3.3 Java = jre1.8.0_171 (64 bit) R Packages: library(NLP) library(tm) library(RColorBrewer) library(wordcloud) library(ggplot2) library(data.table) library(rJava) library(RWeka) library(SnowballC)
Views: 5902 ProfessorPitch
Recognizing products from raw text descriptions using... - Tymoteusz Wołodźko, Tomasz Płomiński
 
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PyData Warsaw 2018 Recognizing products from raw text descriptions using “shallow” and “deep” machine learning - We will compare “shallow” and “deep” machine learning approaches to solving a natural language processing problem. Pros, cons and consequences of both choices will be discussed. === www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 313 PyData
Mining Social Media Data for Understanding Students’ Learning Experiences
 
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Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences—opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from tweets streamed at the geo-location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences.
My Journey with Text Analytics – From R to Analyttica TreasureHunt (ATH)
 
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Text Analytics, also known as Text Mining, is a technique used to derive insights from text data. The field has picked up some traction in review and customer analysis, and provides businesses with deeper insights regarding their customers. Everyone who has used an e-commerce site understands how consumer reviews, or lack thereof, can have a direct impact on new consumers, and thus, the business. An understanding of basic concepts and techniques used in text mining can help an analyst better understand text data, and derive useful information from a source where others just see “words”. Similarly, it can help an organization better understand the consumer’s view of the organization, and thus help in their decision making, to drive growth.
Views: 338 Analyttica Datalab
Sentiment Analysis Using Machine Learning | Python | Sklearn | Beginner Tutorial
 
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Source Code: https://goo.gl/Q3Gt5m References: https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/ http://www.inf.ed.ac.uk/teaching/courses/inf2b/learnnotes/inf2b-learn-note07-2up.pdf https://data.world/datasets/twitter In this video I explain how you can use machine learning algorithms on text data, using the example of twitter sentiment analysis. I have got the dataset of trump related tweets. It is there in the above mentioned website. This code looks though all the data and then figures out if a tweet is a positive tweet or a negative tweet. After the classification(positive sentiment/negative sentiment) it saves the data in a file. Code work offers you a variety of educational videos to enhance your programming skills. At times I create videos without prior preparations so that I can show you the mistakes I am making so that you don't repeat those mistakes yourself. It's humanly to make errors, so if you find some errors in my videos please leave a comment below and I will address them or you can email me at [email protected] stating the problem. I shall try to address all of you . Finally please hit hike . . . and do subscribe so that you get to know at once when some video is being released. Happy coding . .. Epic pen: http://epic-pen.com Screen Recorder: https://obsproject.com/ Facebook https://www.facebook.com/Coding-algorithms-datastructure-Codeworks-1520910904866937/ google plus https://plus.google.com/118085047343771284166 My Website: http://www.the-tinker-project.co.in/blog/
Views: 5158 code works
Learning Analytics - How to Make Students ICT Usage Transparent - DR Michael Cejnar, CEO, edQuire.
 
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LEARNING ANALYTICS - HOW TO MAKE STUDENTS ICT USAGE TRANSPARENT Speaker: Dr Michael Cejnar, CEO FIC Technology Australia has the world's highest use of classroom 1:1 laptops, but the nature and effectiveness of usability is not well understood. Laptop use in classrooms can make student work opaque to teachers, but on the other hand, the laptop can equally be used to effectively and in real time evaluate and display student's engagement, learning process and skills set. Such understanding of student engagement and distractibility patterns, may be a powerful tool for educators to be able to switch distracted students back to engagement, by providing them with activities and resources adapted to meet their learning styles. Discover how classroom learning analytics is shaping the future of measuring, managing and reporting students' learning and ICT skills. Dr Michael Cejnar - edQuire CEO, FiC Technology Dr Michael Cejnar, is a Sydney cardiologist and biomedical engineering entrepreneur, who founded the cross-disciplinary FIC Technology in 2011 with the goal of developing practical classroom tools for evidence-based teaching and learning in K- 12 education. FIC Technology is using cloud-based Learning Analytics of laptop use in classrooms to provide teachers with real-time indication of context-based on-taskness, ICT skills and diagnostics of learning processes. Analysis of this data has enabled an understanding of usage patterns of classroom ICT and student's skills sets, necessary for effective and efficient use of ICT in K-12
Data Mining in Education:  Tool or Profit Fracker
 
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When students use educational technology sometimes their metadata is stored. Is this a tool that allows ed tech firms to develop tools or is it an invasive profit-fracking tool? This is a project for an Information Policy at Syracuse University. I am a School Media Specialist.
Views: 24 Juan Rivera
Data & Analytics in the Law - NYC - Sep 27 2017
 
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Today Artificial Intelligence (AI), Blockchain, smart contracts, machine learning are top of mind and the legal profession is no exception. Law firms are hiring Chief Data Scientists; in-house departments are pressured to do more with less; and there have been two legal blockchain projects recently announced. By crunching data and using automation, lawyers can improving efficiency and accuracy and delivering better services to clients. Hear from experts on how your organization can harness information; produce analytics; and benefit from innovation in the law. 00:27 Welcome – Mary Juetten, Evolve Law 03:27 Darwin Talk – AI: An Historical Perspective – Dean Sonderegger, Wolters Kluwer 13:05 Expert Panel: Data & Analytics in the Law Moderator – Mary Juetten, Evolve Law Bennett Collen, Cognate Houman Shadab – New York Law School, Clause.io Susan Chazin, Wolters Kluwer Aaron Wright, Cardozo Law VENUE SPONSOR Cardozo Law - https://www.cardozo.yu.edu/ SPONSOR Wolters Kluwer - http://wolterskluwer.com/ ABOUT EVOLVE LAW Evolve Law brings together legal tech companies, attorneys, in-house counsel, entrepreneurs, and law schools for events centered around product demos, education, and discussion around the future of law. http://evolvelawnow.com #evolvelawlive #3539
Views: 494 Evolve the Law
10 Data Preparation
 
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Download the sample tutorial files at http://static.rapidminer.com/education/getting_started/Follow-along-Files.zip
Views: 8382 RapidMiner, Inc.