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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 Natural Language Processing Training call us at US: +18336900808 (Toll Free) or India: +918861301699 , Or, write back to us at [email protected]dureka.co
Views: 4997 edureka!
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 136007 Siraj Raval
Python Text Mining with nltk
 
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Link to our course :  http://rshankar.com/courses/autolayoutyt7/ In this course, we have been looking at Regular expressions, a tool that helps us mine text but in this video i wish to give you a flavor of a Python package called nltk. Since this course is about finding patterns in text, it is only fair that you know about another package that offers a lot of help in this direction. Reference: https://www.nltk.org/ https://en.wikipedia.org/wiki/Text_mining https://www.deviantart.com/sirenscall/art/The-Highwayman-26312892 https://www.deviantart.com/enricogalli/art/Moby-Dick-303519647 Images courtesy: Designed by Freepik from www.flaticon.com Script: If you look at jobs advertised for data analysts or data scientists, you will often come across the term - text mining It is the process of deriving useful information from text. Text mining is in itself a fascinating subject and involves tasks such as text classification, text clustering, sentiment analysis and much more. The goal of text mining is to turn text into data for analysis. In this course, we have been looking at Regular expressions, a tool that helps us mine text but in this video i wish to give you a flavor of a Python package called nltk. Since this course is about finding patterns in text, it is only fair that you know about another package that offers a lot of help in this direction. nltk stands for the natural language toolkit and is an open source community driven project. nltk helps us build Python programs to work with human language data. So for example if you wish to create a spam detection program, or movie review program, nltk offers a lot of helper functions. The goal of this video to inform you that such a package exists and show you some basic functionality. If you like what you see, do let me know and I will add more videos on this subject. So we will start with a new Jupyter notebook. I already have the nltk package . If you do not, you will need to get it, please. nltk comes with some example books. We can import these books or corpora as follows. Perhaps some of these titles may be familiar to you. So lets take Moby Dick. Its data is stored in a Text object. Can we find how many words the book contains? Ok, now how about unique words? Hmm. Less than 10 percent of the total words. An interesting thing we may wish to do is examine the frequency of words. This is often done with speeches of various politicians. So for example you may wish to see the most frequent words spoken by a politician before an election and the frequency after elections. So lets import FreqDist and assign to it the text of Moby Dick. So the keys of this object are all the words and we can see the values which are the frequency of the words. Moby Dick is a story of a whale. Lets see how many times this word figures in the book. The keys are case sensitive of course. Let us now focus on popular words in the book. But not words such as ‘has’ or ‘the’ So lets say we want to find the words of length greater than 6 which appear more than 100 times in the book. And lets sort these words for good measure. Interesting set of words. Some such as Captain would be expected i guess. Lets come back to a topic we have seen before - Word tokenization. So we have our sentence like so. And we want to break this sentence into various tokens or words. Earlier we used the function split() so lets do that again. As you can see, the output in this case bundles the full stop with a word. Also what about the word shouldn’t. Is it one token or 2? nltk provides a function that is more language syntax aware. Lets use it. I will leave you to evaluate the differences. One last thing. Here we have a slice of a wonderful poem called the HighwayMan. Now we wish to break this text into its sentences. Can we do it? Regular expressions can help but why use Regex when we have a solution. nltk offers a sent_tokenize function. Lets use it. Isn’t this poem beautiful.. Ok guys thats it for now. If you want more videos on this subject do let me know. Take care.
Views: 110 funza Academy
Manage All Unstructured Data with SAS® Text Analytics
 
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http://www.sas.com/en_us/software/analytics/text-miner.html Learn how text analytics can help you tap into the power of your unstructured data and provide more complete fact-based decisions. SAS TEXT MINER Get faster, deeper insight from unstructured data. Why limit yourself to analyzing legacy data? Our text mining software lets you easily analyze text data from the web, comment fields, books and other text sources. Discover new information, topics and term relationships that deepen your understanding. And add what you learn to your models to improve lift and performance. Benefits: * Improve model performance. * Add subject-matter expertise. * Automatically know more. * Determine what's hot and what's not. LEARN MORE ABOUT SAS TEXT MINER http://www.sas.com/en_us/software/analytics/text-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 29344 SAS Software
Number 1 Recommendation When Starting To Learn NLP
 
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Learn The Number 1 Recommendation When Starting To Learn NLP The Check out https://goo.gl/jusPUm to enjoy more great free video training.
Views: 11746 NLPTimes
Best books on Data Mining
 
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Best books on Data Mining
Views: 266 Books Magazines
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: 77283 Data School
Digital Text Mining
 
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Matthew Jockers, University of Nebraska-Lincoln assistant professor of English, combines computer programming with digital text-mining to produce deep thematic, stylistic analyses of literary works throughout history -- an intensely data-driven process he calls macroanalysis. It's opening up new methods for literary theorists to study literature. http://research.unl.edu/annualreport/2013/pioneering-new-era-for-literary-scholarship/ http://research.unl.edu/
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: 231950 Siraj Raval
Introduction to Text Analytics with R: Overview
 
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This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data is far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 1 of this video series provides an introduction to the video series and includes specific coverage: - Overview of the spam dataset used throughout the series - Loading the data and initial data cleaning - Some initial data analysis, feature engineering, and data visualization Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam... The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/tu... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 56382 Data Science Dojo
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 Natural Language Processing Training call us at US: +18336900808 (Toll Free) or India: +918861301699 , Or, write back to us at [email protected]
Views: 7242 edureka!
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 393824 sentdex
Statistical Text Analysis for Social Science
 
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What can text analysis tell us about society? Corpora of news, books, and social media encode human beliefs and culture. But it is impossible for a researcher to read all of today's rapidly growing text archives. My research develops statistical text analysis methods that measure social phenomena from textual content, especially in news and social media data. For example: How do changes to public opinion appear in microblogs? What topics get censored in the Chinese Internet? What character archetypes recur in movie plots? How do geography and ethnicity affect the diffusion of new language? In order to answer these questions effectively, we must apply and develop scientific methods in statistics, computation, and linguistics. In this talk I will illustrate these methods in a project that analyzes events in international politics. Political scientists are interested in studying international relations through *event data*: time series records of who did what to whom, as described in news articles. To address this event extraction problem, we develop an unsupervised Bayesian model of semantic event classes, which learns the verbs and textual descriptions that correspond to types of diplomatic and military interactions between countries. The model uses dynamic logistic normal priors to drive the learning of semantic classes; but unlike a topic model, it leverages deeper linguistic analysis of syntactic argument structure. Using a corpus of several million news articles over 15 years, we quantitatively evaluate how well its event types match ones defined by experts in previous work, and how well its inferences about countries correspond to real-world conflict. The method also supports exploratory analysis; for example, of the recent history of Israeli-Palestinian relations.
Views: 978 Microsoft Research
Text Mining (part 1)  -  Import Text into R (single document)
 
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Text Mining with R. Import a single document into R.
Views: 14984 Jalayer Academy
SAS® Text Analytics Software Demo
 
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http://www.sas.com/en_us/software/analytics/text-miner.html SAS Text Analytics help companies address big data issues that arise from unstructured content by applying linguistic rules and statistical methods. SAS TEXT MINER Get faster, deeper insight from unstructured data. Why limit yourself to analyzing legacy data? Our text mining software lets you easily analyze text data from the web, comment fields, books and other text sources. Discover new information, topics and term relationships that deepen your understanding. And add what you learn to your models to improve lift and performance. Benefits: * Improve model performance. * Add subject-matter expertise. * Automatically know more. * Determine what's hot and what's not. LEARN MORE ABOUT SAS TEXT MINER http://www.sas.com/en_us/software/analytics/text-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss To learn more about SAS Text Analytics, visit http://www.sas.com/textanalytics
Views: 23716 SAS Software
4/6 Ask the Expert:  Adventures in Text Analytics
 
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Confession: I hate categories. I hate that they rarely work. I hate that almost every year companies deploy projects to rebuild them. So how happy we are that Performance Analytics new Text Analytic feature can do nearly all the work you used to expect from Categories. Not only that, but the money you previously burned doing new Category overhaul projects and user training can be put to something more useful (like other PA projects). In this session, we demonstrate the Text Analytics feature and prove it's every bit the equal of category trees with a fraction of the effort. If you're not measuring performance on ServiceNow applications, then why are you measuring at all? Join us on the discussion to watch the presentation and post your questions. https://community.servicenow.com/community?id=community_question&sys_id=c728470adbf853403882fb651f9619c8 ------- Next Steps ------- Book a FREE consultation with Whitespace Studios: https://thewhitespace.io/services/performance/ If you're a ServiceNow CUSTOMER and our outcome based approach to the platform inspires you, lets work together: [email protected] If you're a ServiceNow PARTNER, and you want the best Performance Analytics, Integration, and Custom app building talent at your fingertips, we're happy to white-label under your brand: [email protected] If you're in ServiceNow Sales, and you need passionate motivated solvers to close PA deals or ensure customer satisfaction with the product, let's talk: [email protected]
Views: 288 NOWCommunity
PDF Data Extraction and Automation 3.1
 
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Learn how to read and extract PDF data. Whether in native text format or scanned images, UiPath allows you to navigate, identify and use PDF data however you need. Read PDF. Read PDF with OCR.
Views: 104570 UiPath
SAS TextMining Introduction
 
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Introduction to Text Mining
Views: 1158 Dothang Truong
Text Analytics Future
 
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Text Analytics Future. #IIEX Interview with Anderson Analytics - OdinText founder Tom H. C. Anderson
Views: 387 OdinText
Data Science Tutorial | Text Analytics in R  - Creating a Stunning Word Cloud in R - Part 1
 
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In this data science tutorial video I’ve talked about text analytics in R and using the text analytics in R how you can create the stunning word cloud that will help your understand the gist of the entire book or speech or long corporate emails. Wordcloud is a very simple yet very helpful tool to have it in your pocket to really get to know how your leaders are thinking and may take decision in future. In this video I’ve shown you basic functioning of creating wordcloud in R and then how you can tune the wordcloud parameter for a stunning wordcloud in action.
Big Data Analytics using Python and Apache Spark | Machine Learning Tutorial
 
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Apache Spark is the most active Apache project, and it is pushing back Map Reduce. It is fast, general purpose and supports multiple programming languages, data sources and management systems. More and more organizations are adapting Apache Spark to build big data solutions through batch, interactive and stream processing paradigms. The demand for trained professionals in Spark is going through the roof. Being a new technology, there aren't enough training sources to provide easy guidance on building end-to-end solutions. Section 1: Introduction Lecture 1 About the course 08:42 Lecture 2 About V2 Maestros 01:39 Lecture 3 Resource Bundle Article Section 2: Overview Lecture 4 Hadoop Overview 10:06 Lecture 5 HDFS Architecture 14:46 Lecture 6 Map Reduce - How it works 17:24 Lecture 7 Map Reduce - Example 16:46 Lecture 8 Hadoop Stack 06:27 Lecture 9 What is Spark? 14:03 Lecture 10 Spark Architecture - Part 1 13:23 Lecture 11 Spark Architecture - Part 2 13:25 Lecture 12 Installing Spark and Setting up for Python 12:05 Quiz 1 Hadoop and Spark Architecture 5 questions Section 3: Programming with Spark Lecture 13 Spark Transformations 11:33 Lecture 14 Spark Actions 15:04 Lecture 15 Advanced Spark Programming 10:10 Lecture 16 Python - Spark Programming examples 1 16:11 Lecture 17 Python - Spark Programming Examples 2 17:18 Quiz 2 Data Engineering with Spark 5 questions Lecture 18 PRACTICE Exercise : Spark Operations Article Section 4: Spark SQL Lecture 19 Spark SQL Overview 10:03 Lecture 20 Python - Spark SQL Examples 16:16 Quiz 3 Spark SQL 2 questions Lecture 21 PRACTICE Exercise : Spark SQL Article Section 5: Spark Streaming Lecture 22 Streaming with Apache Spark 15:53 Lecture 23 Python - Spark Streaming examples 17:47 Quiz 4 Spark Streaming 3 questions Section 6: Real time Data Science Lecture 24 Basic Elements of Data Science 11:51 Lecture 25 The Dataset 10:44 Lecture 26 Learning from relationships 12:55 Lecture 27 Modeling and Prediction 09:31 Lecture 28 Data Science Use Cases 07:47 Lecture 29 Types of Analytics 12:08 Lecture 30 Types of Learning 17:16 Lecture 31 Doing Data Science in real time with Spark 07:39 Quiz 5 Spark Data Science 5 questions Section 7: Machine Learning with Spark Lecture 32 Spark Machine Learning 12:18 Lecture 33 Analyzing Results and Errors 13:46 Lecture 34 Linear Regression 19:00 Lecture 35 Spark Use Case : Linear Regression 18:33 Lecture 36 Decision Trees 10:42 Lecture 37 Spark Use Case : Decision Trees Classification 14:58 Lecture 38 Principal Component Analysis 07:28 Lecture 39 Random Forests Classification 10:31 Lecture 40 Python Use Case : Random Forests & PCA 13:16 Lecture 41 Text Preprocessing with TF-IDF 14:53 Lecture 42 Naive Bayes Classification 19:21 Lecture 43 Spark Use Case : Naive Bayes & TF-IDF 07:26 Lecture 44 K-Means Clustering 11:53 Lecture 45 Spark Use Case : K-Means 14:26 Lecture 46 Recommendation Engines 11:55 Lecture 47 Spark Use Case : Collaborative Filtering 06:34 Lecture 48 Real Time Twitter Data Sentiment Analysis 10:11 Quiz 6 Spark Machine Learning Algorithms 4 questions Lecture 49 PRACTICE Exercise : Spark Clustering Article Lecture 50 PRACTICE Exercise : Spark Classification Article Section 8: Conclusion Lecture 51 Closing Remarks 01:56 Lecture 52 BONUS Lecture : Other courses you should check out Article
Text Mining and Analytics Made Easy with DSTK Text Explorer
 
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DSTK - Data Science Toolkit offers Data Science softwares to help users in data mining and text mining tasks. DSTK follows closely to CRISP DM model. DSTK offers data understanding using statistical and text analysis, data preparation using normalization and text processing, modeling and evaluation for machine learning and statistical learning algorithms. DSTK Text Explorer helps user to do text mining and text analytics task easily. It allows text processing using stopwords, stemming, uppercase, lowercase and etc. It also has features in sentiment analysis, text link analysis, name entity, pos tagging, text classification using stanford nlp classifier. It allows data scraping from images, videos, and webscraping from websites. For more information, visit: http://dstk.tech
Views: 3624 SVBook
Introduction - Learn Python for Data Science #1
 
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Welcome to the 1st Episode of Learn Python for Data Science! This series will teach you Python and Data Science at the same time! In this video we install Python and our text editor (Sublime Text), then build a gender classifier using the sci-kit learn library in just about 10 lines of code. Please subscribe & share this video if you liked it! The code for this video is here: https://github.com/llSourcell/gender_classification_challenge I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Download Python here: https://www.python.org/downloads/ Download Sublime Text here: https://www.sublimetext.com/3 Some Great simple sci-kit learn examples here: https://github.com/chribsen/simple-machine-learning-examples and the official scikit website: http://scikit-learn.org/ Highly recommend this online book as supplementary reading material: https://learnpythonthehardway.org/book/ Wondering when to use which model? This chart helps, but keep in mind deep neural nets outperform pretty much any model given enough data and computing power. so use these when you don't have access to loads of data and compute: http://scikit-learn.org/stable/tutorial/machine_learning_map/ Thank you guys for watching! Subscribe, like, and comment! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 424817 Siraj Raval
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 63540 deltaDNA
Text Analytics with R | Analyzing Sentiments with BoxPlot Chart | Data Science Tutorial
 
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In this data science text analytics with R tutorial, I have talked about how you can analyze the sentiments from text using box plot chart in R. It helps us comparing sentiments of multiple texts or speeches or books to better analyze the sentiments from it. Text mining in R is done with help of sentimentr package and tm package. Text analytics with R,analyzing sentiments with boxplot chart,data science tutorial,boxplot chart,plotting sentiments,sentiment analysis in R,sentiment analysis with R,how to analyzing text in R,text processing in R,natural languge processing,NLP,nlp in R,r nlp,nlp anlaysis in R,what is text mining,how to do text mining in R,how to do NLP in R,NLP processing in R,process nlp in R,R tutorial for beginners,beginners tutorial for R,learn NLP using R
Python Text Analysis -  Find Protagonist in a Book!!
 
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GET CODE HERE: http://robotix.com.au/#/videos/123 Python text analysis using the TextBlob modue available here: http://textblob.readthedocs.io/en/dev/ Using the code above you can read entire books imported to python as text files from Project Gutenberg SOCIAL: Twitter: https://twitter.com/SanjinDedic Facebook Page: https://www.facebook.com/RobotixAu/ LinkedIn: https://au.linkedin.com/in/sanjin-dedic-a028b9113 MINDS: https://www.minds.com/SanjinDedic WEBSITES Techxellent.com.au Robotix.com.au -~-~~-~~~-~~-~- Latest and Best Arduino Playlist in Collaboratio with DFRobot: https://www.youtube.com/playlist?list=PL_92WMXSLe_86NTWf0nchm-EmQIwccEye -~-~~-~~~-~~-~-
Views: 429 Robotix
Data Science Essentials in Python, the Book
 
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Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Get your copy of the book at https://pragprog.com/book/dzpyds/data-science-essentials-in-python
Views: 331 Dmitry Zinoviev
Text mining Lecture 7
 
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Text Mining Lecture 7 Topic: Natural Language Processing in Accounting, Auditing and Finance: A synthesis of the Literature with a Roadmap for Future Research 01:33 Major Contribution of the Paper 02:56 Introduction 03:47 Objective 04:17 Literature Selection & Assessment 08:43 Analysis of Sample size N 14:11 NLP in Accounting , Auditing and Finance 16:48 Knowledge Organization, Categorization, and Retrieval 17:49 Taxonomy & Thesauri Generation 18:30 Information Retrieval 20:23 Fraud Prediction and Detection 21:57 Predicting Stock Prices and Market Activity 23:36 Firm- Specific Predicitions 24:23 Predictive Value of Annual Reports and Disclosures 25:27 Predictive of Web Content 29:56 Natural Language Processing & Readability Studies Topic: Detecting deceptive discussion in conference calls 36:29 Motivation 38:47 Literature review on linguistic features 44:29 Development of word lists to measure deception 1:02:53 Data 1:04:30 Parsing method for conference calls 1:10:29 Results for CFO 1:13:01 Similarities in Linguistic cues 1:15:01 Coding 1:23:02 Software Repository for Accounting and Finance
R Programming Tutorial-22-How to Read Txt Files ( हिन्दी)
 
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Programming in Hindi ( हिन्दी) R is the language of big data—a statistical programming language that helps describe, mine, and test relationships between large amounts of data.. This course will teach you to program the R language from the ground up. You will learn everything from the very fundamentals of programming right through to the complexities of charts...... Learn Shiny:- https://www.youtube.com/playlist?list=PLgPJX9sVy92yImPWgBojfdTx8pkU03Q2H Best Book For R 1..https://dl.flipkart.com/dl/beginning-r-statistical-programming-language-1/p/itmegmt3qxdzfwgz?iid=95e191f0-6122-42d6-b4a4-c944b7edb0fe.9788126541201.SEARCH&srno=s_1_4&lid=LSTBOK9788126541201BZ19LR&fm=SEARCH&qH=abbc816787956bfb&pid=9788126541201&affid=vijaymanr 2... https://dl.flipkart.com/dl/beginning-r-introduction-statistical-programming/p/itmefqfarxq6ybke?iid=f04ae3d3-05f3-4336-b31a-496861cfad22.9781430245544.SEARCH&srno=s_1_8&lid=LSTBOK9781430245544ZT0WTF&fm=SEARCH&qH=abbc816787956bfb&pid=9781430245544&affid=vijaymanr Please support us by Paytm:--9634533596 Learning MySQL - (https://dl.flipkart.com/dl//learning-mysql/p/itmdz6zetdmpxe8g?pid=9780596008642&affid=vijaymanr) Best XHTML And CSS Tutorials:--https://www.youtube.com/playlist?list=PLgPJX9sVy92w1pmbv9S1G6jdyCuMDFVek Best android app development Tutorials:--https://www.youtube.com/playlist?list=PLgPJX9sVy92zmA9YedYtbnOfLP8DH6Ihd Best Python tutorial:-https://www.youtube.com/playlist?list=PLgPJX9sVy92xVxrM7YJRZ2TUXPgWYyfVr Best Java tutorial:----https://www.youtube.com/playlist?list=PLgPJX9sVy92zxE2XRZenJ-rjHnt_Vqr2f Best C++ tutorial :-----https://www.youtube.com/playlist?list=PLgPJX9sVy92wA4SkNpy8-3vcPg9zpLthG Best C tutorial:---------https://www.youtube.com/playlist?list=PLgPJX9sVy92xk-c6iNqobhPqEokLiCGnp Please support us by Paytm:--9634533596 Learn MongoDB :---- https://www.youtube.com/playlist?list=PLgPJX9sVy92xUxpTFgAOSBHdBwIdxav39 Learn Magento 2 :----- https://www.youtube.com/playlist?list=PLgPJX9sVy92x0IfsB1iIXt286LSn1kQ_j Learn Laravel 5.4 :----https://www.youtube.com/playlist?list=PLgPJX9sVy92y5riB65d_Os5iZIs2wk_T3 Learning PHP:--https://www.youtube.com/playlist?list=PLgPJX9sVy92yA5dP9pSHzuQpyEwBRuvU8 ---------------------------------------------------------------------- Laptop I used :--- http://fkrt.it/Gq49vLuuuN Mic I used :--- http://fkrt.it/Gi2DyLuuuN ----------------------------------------------------------------------
Views: 2688 CS Geeks
Text Analytics of Bible vs. Quran
 
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Text Analytics of Bible vs. Quran using Next Generation Text Analytics Software-as-a-Service OdinText. Visit http://odintext.com/blog/ for more information
Views: 14919 OdinText
How to extract text from an image in python | pytesseract | Image to text processing
 
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In this tutorial, we shall demonstrate you how to extract texts from any image in python. So we shall write a program in python using the module pytesseract that will extract text from any image like .jpg, .jpeg, .png etc. Please subscribe to my youtube channel for such tutorials Watch the same tutorial on how to extract text from an image in Linux below: https://youtu.be/gLUQ8uaaw8A Please watch the split a file by line number here: https://youtu.be/ADRmbu3puCg Split utility in Linux/Unix : to break huge file into small pieces https://www.youtube.com/watch?v=ADRmbu3puCg How to keep sessions alive in terminal/putty infinitely in linux/unix : Useful tips https://www.youtube.com/watch?v=ARIgHdpxaU8 Random value generator and shuffling in python https://www.youtube.com/watch?v=AKwnQQ8TBBM Intro to class in python https://www.youtube.com/watch?v=E6kKZXHS5hM Lists, tuples, dictionary in python https://www.youtube.com/watch?v=Axea1CSewzc Python basic tutorial for beginners https://www.youtube.com/watch?v=_JyjbZc0euY Python basics tutorial for beginners part 2 -variables in python https://www.youtube.com/watch?v=ZlsptvP69NU Vi editor basic to advance part 1 https://www.youtube.com/watch?v=vqxQx-NNyFM Vi editor basic to advance part 2 https://www.youtube.com/watch?v=OWKp2DLaFyY Keyboard remapping in linux, switching keys as per your own choice https://www.youtube.com/watch?v=kJz7uKDyZjs How to install/open an on sceen keyboard in Linux/Unix system https://www.youtube.com/watch?v=d71i9SZX6ck Python IDE for windows , linux and mac OS https://www.youtube.com/watch?v=-tG54yoDs68 How to record screen or sessions in Linux/Unix https://www.youtube.com/watch?v=cx59c15-c8s How to download and install PAGE GUI builder for python https://www.youtube.com/watch?v=dim725Px2hM Create a basic Login page in python using GUI builder PAGE https://www.youtube.com/watch?v=oCAWWUhwEUQ Working with RadioButton in python in PAGE builder https://www.youtube.com/watch?v=YJbQvpzJDr4 Basic program on Multithreading in python using thread module https://www.youtube.com/watch?v=RGm3989ekAc
Views: 15554 LinuxUnixAix
Natural Language Processing (NLP)- Part 1
 
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Natural language processing is a very important part of machine learning. Many of you are doing your final year thesis on NLP. But in traditional books and tutorials these thing are theoretically explained, whereas application based lessons are much needed to complete projects. I hope you like these videos. What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. What is Artificial Intelligence? (AI) Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g/videos?sub_confirmation=1 Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] #machinelearning #bigdata #ML #DataScience #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #BLE #DataProcessing #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials
Views: 174 Planeter
Text Analytics with R | How to find correlation between words - Data Science Tutorial
 
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In this text analytics with R video I've talked abou how you can find correlation between. words and understand the context behind the entire text and the motive of speaker or writer. This helps understand how one specific important word is related to other words in the entire text and we can limit the correlation also to look at only those words which has either high or low correlation. Text analytics with R,how to find correlation between words in R,data science tutorial,finding correlation between words,finding most frequent terms in the entire text,Finding most frequent words in R,word correlation in R,r Word correlation,Learn Text analytics in R,R Text mining,introduction to text analytics with R,most frequent words script in R,R script to find most frequent words,R script to find correlation between words,R script for Text mining
Reducing High Dimensional Data with PCA and prcomp: ML with R
 
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Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUOKu In this R video, we'll see how PCA can reduce a 1000+ variable data set into 10 variables and barely lose accuracy! Walkthrough & code: http://amunategui.github.io/high-demensions-pca/ Note: data source url in the video no longer works, see the walkthrough for new source: http://amunategui.github.io/high-demensions-pca/ Note: for those that can't use xgboost - I added an alternative script using GBM in the walkthrough: http://amunategui.github.io/high-demensions-pca/ Top of the page under resources look for link: "Alternative GBM Source Code - for those that can't use xgboost" This has been re-designed as 'Reducing High Dimensional Data in R' on Udemy.com, $19 COUPON!!!: https://www.udemy.com/practical-data-science-reducing-high-dimensional-data-in-r/?couponCode=1111 Check out my other in-depth classes on Udemy.com (discounts and specials) at http://amunategui.github.io/udemy/ Machine Learning with R. If you liked this video - give me a thumbs up! Thx
Views: 38555 Manuel Amunategui
Projects In Machine Learning | NLP for Text Classification with NLTK & Scikit-learn | Eduonix
 
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In this tutorial, we will cover Natural Language Processing for Text Classification with NLTK & Scikit-learn. Remember the last Natural Language Processing project we did? (http://bit.ly/2Ittrop) We will be using all that information to create a Spam filter. This tutorial will also cover Feature Engineering and ensemble NLP in text classification. This project will use Jupiter Notebook running Python 2.7. Let's get started! You will find the source code to this project here: https://github.com/eduonix/nlptextclassification Check out our other Machine Learning Projects here: http://bit.ly/2HIXvvV Don't forget to check our new project on Data Science Foundational Program on Kickstarter. This program incorporates everything from beginner-level concepts to real-world implementation along with 4 courses, 2 e-books, Interview preparation guide, multiple labs, numerous practice tests and much more. Read more - https://kck.st/2CuIkay Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. http://bit.ly/2ITJDQb Follow Eduonix on other social networks: ■ Facebook: https://goo.gl/ZqRVjS ■ Twitter: https://goo.gl/oRDaji ■ Google+: https://goo.gl/mfPaxx ■ Instagram: https://goo.gl/7f5DUC | @eduonix ■ Linkedin: https://goo.gl/9LLmmJ ■ Pinterest: https://goo.gl/PczPjp
CrossRef Metadata and Text and Data Mining
 
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CrossRef European Workshop in Vilnius 11 June 2015, the Lithuanian Academy of Sciences, Lithuania The workshop will consider good practice publishing for journals and books, how CrossRef services can improve digital journal and book quality, and ways to promote ethical publishing. It will also demonstrate how CrossRef services can enable digital journals and books to meet international publishing standards. The workshop has been developed from the highly successful annual meetings held in the US and UK and is being held in Vilnius to allow those unable to travel to the UK or US to participate. More information: http://serials.lt/crossref-european-workshop-in-vilnius/
What does data mining mean?
 
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What does data mining mean? A spoken definition of data mining. Intro Sound: Typewriter - Tamskp Licensed under CC:BA 3.0 Outro Music: Groove Groove - Kevin MacLeod (incompetech.com) Licensed under CC:BA 3.0 Intro/Outro Photo: The best days are not planned - Marcus Hansson Licensed under CC-BY-2.0 Book Image: Open Book template PSD - DougitDesign Licensed under CC:BA 3.0 Text derived from: http://en.wiktionary.org/wiki/data_mining Text to Speech powered by TTS-API.COM
Introduction to Text Analysis with NVivo 11 for Windows
 
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It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share. http://www.qsrinternational.com
Views: 114340 NVivo by QSR
How to build Interactive Excel Dashboards
 
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Download file used in the video with step by step instructions and links to more tutorials: https://www.myonlinetraininghub.com/workbook-downloads In this video you will learn how to create an interactive dashboard from scratch using the built in Excel tools. No add-ins or VBA/Macros. Just plain Excel. Applies to Excel 2007 onward for Windows & Excel 2016 onward for Mac. Subscribe to my free newsletter and get my 100 Tips & Tricks eBook here: https://www.myonlinetraininghub.com/sign-up-for-100-excel-tips-and-tricks
Views: 1442368 MyOnlineTrainingHub
Big Data and Hadoop Developer 2018 | Big Data as Career Path | Introduction to Big Data and Hadoop
 
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https://acadgild.com/big-data/big-data-development-training-certification/ Big Data and Hadoop Developer 2018 | Big Data as Career Path | Introduction to Big Data and Hadoop Big Data is growing explosively bigger & bigger every day. Get to know what makes Big Data the next big thing. What is Big Data all about? Big Data has been described in multiple ways by the industry experts. Let’s have a look at what Wikipedia has to say. Big Data is a term for datasets that are so large or complex that traditional data processing applications are inadequate. To put it in simple words, Big Data is the large volumes of structured and unstructured data. Did You Know? • According to Wikibon and IDC, 2.4 quintillion bits are generated every day? • Did you know, the data from our digital world will grow from 4.4 trillion gigabytes in 2013 to 44 trillion gigabytes in 2020? • In addition to that, data from embedded systems will grow from 2% in 2013 to 10% in 2020 The sheer volume of the data generated these days has made it absolutely necessary to re-think how we handle them. And with growing implementation of Big Data in sectors like banking, logistics, retail, e-commerce and social media, the volume of data is expected to grow even higher. Other than its Sheer size, what else makes Big data so important? • Mountains of data that can be gathered and analyzed to discover insights ad make better decisions. • Using Big data, social media comments can be analyzed in a far timelier and relevant manner offering a richer data set. • With Big Data, banks can now use information to constantly monitor their client’s transaction behaviors in real time. • Big Data is used for trade analytics, pre-trade decision-support analytics, sentiment measurement, predictive Analytics etc. • Organizations in media and entertainment industry use Big Data to analyze customer data along with behavioral data to create detailed customer profiles. • In the manufacturing and natural resources industry, Big Data allows for predictive modeling to support decision making. What is Big Data and its importance in the near future Gartner analyst Doug Laney introduced the 3Vs concept in 2001. Since then, Big Data has been further classified, based on the 5Vs. • Volume – The vast amounts of data generated every second. • Variety – The different types of data which contribute to the problem, such as text, videos, Images, audio files etc. • Velocity – The speed at which new data is generated and moves around. • Value – Having an access to Big Data in no good unless we turn it into value • Veracity - Refers to the messiness or trustworthiness of the data. Why is Big Data considered as an excellent career path? According to IDC forecast, the Big Data market is predicted to be worth of $46.34 billion by 2018 and is expected to have a sturdy growth over the next five years. Big Data Salary: As per Indeed, the average salary for Big Data professionals is about 114,000 USD per annum, which is around 98% higher than average salaries for all job postings nationwide. And Glassdoor quotes the median salary for Big Data professionals to be 104,850 USD per annum. Big Data professionals get a high percentage of hike in salary and Data scientists get a very good hike up to 8.90% YOY. As the Big Data market grows, so does the demand for the skilled workforce? According to Wanted Analytics, the demand for Big Data skills is to increase by 118% over the previous year. Do you need more reasons to believe in the power of Big Data? EMC, IBM, Cisco, Oracle, Adobe, Amazon, Accenture are just a few of the top companies who are constantly looking for Big Data skills. With the right training and hands-on experience, you too can find your dream career in one of these top companies. The path to your dream job is no longer a mystery. Sign up now & get started with your dream career. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 47079 ACADGILD
How to engage your students while teaching Data Analytics
 
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Author of our new book - Data Analytics, Dr. Anil Maheshwari talking to the faculties on - How to engage students while teaching Data Analytics, the opportunities and challenges in today’s education in Data Science, Managing student attention and the unique joys of teaching data analytics. Know more and buy on www.mheducation.co.in This book fills the need for a concise and conversational book on the growing field of Data Analytics and Big Data. Easy to read and informative, this lucid book covers everything important, with concrete examples, and invites the reader to join this field. This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others attracted to the idea of discovering new insights and ideas from data will enjoy this book. The chapters in the book are organized for a typical one-semester course. Key Features: • Every chapter begins with insightful case-lets from real-world stories invigorating interest of reader • Provides a running case study across the chapters as exercises • Shows clear learning objectives, review questions, and objective type questions • Covers end-to-end data processing chain, from generation of data to the consumption of data • Covers data mining, web mining, text mining, social analytics, and more • Provides easy tutorials for R Programming & Weka; Primers on Statistics, and Data Modeling included in the book
Downloading Data from Google Trends And Analyzing It With R
 
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Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUOKu In this video, I introduce Google Trends by querying it directly through the web, downloading a comma-delimited file of the results, and analyzing it in R. Full walkthrough and code: http://amunategui.github.io/google-trends-walkthrough/ Support these videos, check out my in-depth classes on Udemy.com (discounts and specials) at http://amunategui.github.io/udemy/
Views: 20430 Manuel Amunategui
How Asteroid Mining Will Save Earth | Space Time
 
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Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: https://to.pbs.org/DonateSPACE Thanks to Audible for supporting PBSDS. To start your free 30 day trial and receive a free audiobook visit https://www.audible.com/spacetime or text spacetime to 500 500! The days of oil may be numbered, but there’s another natural resource that’s never been touched, Asteroids. You can further support us on Patreon at https://www.patreon.com/pbsspacetime Get your own Space Time t­-shirt at http://bit.ly/1QlzoBi Tweet at us! @pbsspacetime Facebook: facebook.com/pbsspacetime Email us! pbsspacetime [at] gmail [dot] com Comment on Reddit: http://www.reddit.com/r/pbsspacetime Help translate our videos! https://www.youtube.com/timedtext_cs_panel?tab=2&c=UC7_gcs09iThXybpVgjHZ_7g Previous Episode: The Black Hole Information Paradox https://www.youtube.com/watch?v=9XkHBmE-N34 The richest person in modern history, was John Davison Rockefeller, His net worth was three times greater than that of our richest tech billionaires, inflation adjusted. And the source of that fortune? Timely exploitation of a vast, then-untapped natural resource: oil. Well, the days of oil may be numbered, but there’s another natural resource that’s never been touched, is effectively inexhaustible, and has a dollar value large enough to disrupt entire economies. That resource? Asteroids, and the precious materials they contain. Astrophysicist Neil deGrasse Tyson predicts that the world’s first trillionaire will be an asteroid miner. The Rockefellers of the 21st century may be less like the internet and tech moguls of the 20th century and more like the old-school oil barons of the 19th century. Hosted by Matt O'Dowd Written by Drew Rosen and Matt O'Dowd Graphics by Grayson Blackmon Assistant Editing and Sound Design by Mike Petrow and Linda Huang Made by Kornhaber Brown (www.kornhaberbrown.com) Special thanks to our Patreon Big Bang, Quasar and Hypernova Supporters: Big Bang CoolAsCats David Nicklas Anton Lifshits Joey Redner Fabrice Eap Quasar Tambe Barsbay Mayank M. Mehrota Mars Yentur Mark Rosenthal Dean Fuqua Roman Pinchuk ColeslawPurdie Vinnie Falco Hypernova Donal Botkin Edmund Fokschaner Matthew O’Connor Eugene Lawson Barry Hatfield Martha Hunt Joseph Salomone Chuck Zegar Craig Peterson Jordan Young Ratfeast John Hofmann Thanks to our Patreon Gamma Ray Burst Supporters: James Hughes Fabian Olesen Kris Fernet Jane Meyers James Flowers Greg Allen Denys Ivanov Nick Virtue Alexey Eromenko Nicholas Rose Scott Gossett Mark Vasile Patrick Murray Sultan Alkhulaifi Alex Seto Michal-Peanut Karmi Erik Stein Kevin Warne JJ Bagnell Avi Goldfinger John Pettit Florian Stinglmayr Benoit Pagé-Guitard Nathan Leniz Brandon Labonte David Crane Greg Weiss Shannan Catalano Brandon Cook Malte Ubl
Views: 278362 PBS Space Time
Introduction to Text and Data Mining
 
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Heard about Text and Data Mining (TDM) and wondering if it might be a good fit for your research? Find out what text and data mining is and how it can usefully be applied in a research context. Also learn about data sources for text and data mining projects and support, tools, and resources for learning more.
Views: 25 UniSydneyLibrary
Text-Mining Universtity Maastricht - Game of Thrones
 
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Another great project from the Text-Mining course of the Department of Data Science and Knowledge Engineering from the University of Maastricht by Rik Claessens. Automatic detection of persons, locations and travel patterns from the free text of the books.
Proactive Learning and Structural Transfer Learning: Building Blocks of Cognitive Systems
 
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Dr. Jaime Carbonell is an expert in machine learning, scalable data mining (“big data”), text mining, machine translation, and computational proteomics. He invented Proactive Machine Learning, including its underlying decision-theoretic framework, and new Transfer Learning methods. He is also known for the Maximal Marginal Relevance principle in information retrieval. Dr. Carbonell has published some 350 papers and books and supervised 65 Ph.D. dissertations. He has served on multiple governmental advisory committees, including the Human Genome Committee of the National Institutes of Health, and is Director of the Language Technologies Institute. At CMU, Dr. Carbonell has designed degree programs and courses in language technologies, machine learning, data sciences, and electronic commerce. He received his Ph.D. from Yale University. For more, read the white paper, "Computing, cognition, and the future of knowing" https://ibm.biz/BdHErb
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