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Introduction to Text Analytics with R: Overview
 
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The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – 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 About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – 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 Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz5_y0 Watch the latest video tutorials here: https://hubs.ly/H0hz61V0 See what our past attendees are saying here: https://hubs.ly/H0hz6-S0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 77852 Data Science Dojo
INTRODUCTION TO TEXT MINING IN HINDI
 
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find relevant notes at-https://viden.io/
Views: 10816 LearnEveryone
INTRODUCTION TO TEXT MINING
 
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INTRODUCTION TO TEXT MINING
Views: 502 LearnEveryone
Text Analytics With R | How to Connect Facebook with R | Analyzing Facebook in R
 
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In this text analytics with R tutorial, I have talked about how you can connect Facebook with R and then analyze the data related to your facebook account in R or analyze facebook page data in R. Facebook has millions of pages and getting emotions and text from these pages in R can help you understand the mood of people as a marketer. Text analytics with R,how to connect facebook with R,analyzing facebook in R,analyzing facebook with R,facebook text analytics in R,R facebook,facebook data in R,how to connect R with Facebook pages,facebook pages in R,facebook analytics in R,creating facebook dataset in R,process to connect facebook with R,facebook text mining in R,R connection with facebook,r tutorial for facebook connection,r tutorial for beginners,learn R online,R beginner tutorials,Rprg
8. Text Mining Webinar - Topic Detection
 
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This video from the recordings of the KNME Text Mining Webinar of October 30 2013 (https://www.youtube.com/edit?o=U&video_id=tY7vpTLYlIg). This part shows how to implement a topic detection with text mining and KNIME nodes.
Views: 2728 KNIMETV
Introduction to Text Analytics with R: TF-IDF
 
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TF-IDF includes specific coverage of: • Discussion of how the document-term frequency matrix representation can be improved: – How to deal with documents of unequal lengths. – What to do about terms that are very common across documents. •Introduction of the mighty term frequency-inverse document frequency (TF-IDF) to implement these improvements: -TF for dealing with documents of unequal lengths. -IDF for dealing with terms that appear frequently across documents. • Implementation of TF-IDF using R functions and applying TF-IDF to document-term frequency matrices. • Data cleaning of matrices post TF-IDF weighting/transformation. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – 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 The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hD4l40 Watch the latest video tutorials here: https://hubs.ly/H0hD4lb0 See what our past attendees are saying here: https://hubs.ly/H0hD3R-0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 21222 Data Science Dojo
Social Media Analytics - Twitter Analysis in R (Example @realDonaldTrump)
 
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Case Study: Donald Trump Twitter (@realDonaldTrump) Analysis Click here to see how to link to Twitter database: https://www.youtube.com/watch?v=ebutXE4MJ3Y (UPDATED) Twitter Analytics in R codes Powerpoint can be downloaded at https://drive.google.com/open?id=0Bz9Gf6y-6XtTNDE5a2V0dXBjWVU How to process tweets with emojis in R? What if there is a gsub utf-8 invalid error? (Example Solution) 1. Use gsub to replace the emojis (utf-8 coding) codes. 2. See slide 7 in the Powerpoint file above.
Views: 7403 The Data Science Show
Whatsapp chat sentiment analysis in R | Sudharsan
 
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Whatsapp Chat Sentiment analysis using R programming! Subscribe to my channel for new and cool tutorials. You can also reach out to me on twitter: https://twitter.com/sudharsan1396 Code for this video: https://github.com/sudharsan13296/Whatsapp-analytics
Sentiment Analysis in R | Sentiment Analysis of Twitter Data | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Sentiment Analysis Tutorial shall give you a clear understanding as to how a Sentiment Analysis machine learning algorithm works in R. Towards the end, we will be streaming data from Twitter and will do a comparison between two football teams - Barcelona and Real Madrid (El Clasico Sentiment Analysis) Below are the topics covered in this tutorial: 1) What is Machine Learning? 2) Why Sentiment Analysis? 3) What is Sentiment Analysis? 4) How Sentiment Analysis works? 5) Sentiment Analysis - El Clasico Demo 6) Sentiment Analysis - Use Cases Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #SentimentAnalysis #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 34526 edureka!
What is Text Mining?
 
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An introduction to the basics of text and data mining. To learn more about text mining, view the video "How does Text Mining Work?" here: https://youtu.be/xxqrIZyKKuk
Views: 58828 Elsevier
Text Analytics with R | Sentiment Analysis on Twitter Data | How to analyze tweets in R
 
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In this text analytics with R video, I've talked about how you can analyze twitter data for doing sentiment analysis. Here I've taken an example of US President Donald Trump and analyze the tweets that general public is tweeting about him and then categorize the tweets in positive and negative tweets and create a wordcloud of it to better visualize the data. Text analytics with R,Sentiment Analysis on twitter data,how to analyze tweets in R,r sentiment anlaysis,sentiment analysis in r,r twitter data analysis,analyzing twitter data in R,twitter sentiment analysis,analyzing sentiments from tweets,example of sentiment analysis in r,r sentiment analysis tutorial,r twitter tutorial,sentiment analysis of twitter data in R,how to analyze sentiments of twitter data,R Text analytics tutorial,step by step text analytics in R
Text Mining for Beginners
 
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This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 79539 Linguamatics
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: 66493 edureka!
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
 
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Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. To learn more about Quantopian, visit http://www.quantopian.com. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 2108 Quantopian
Text Mining of PubMed Abstracts
 
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Presentation based on Zaremba et al, Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens. BMC Bioinformatics 2009 10:177 http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-177
Views: 973 Jeff Shaul
Social Network Analysis with R | Examples
 
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Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 26057 Bharatendra Rai
Image Analysis and Processing with R
 
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Link for R file: https://goo.gl/BXEf7M To install EBimage package, you can run following 2 lines; install.packages("BiocManager") BiocManager::install("EBImage") Provides image or picture analysis and processing with r, and includes, - reading and writing picture file - intensity histogram - combining images - merging images into one picture - image manipulation (brightness, contrast, gamma correction, cropping, color change, flip, flop, rotate, & resize ) - low-pass and high pass filter R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 18234 Bharatendra Rai
Rattle for Data Mining - Using R without programming (CRAN)
 
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www.learnanalytics.in demostrates use of an free and open source platform to build sophisticated predictive models. We demonstrate using R package Rattle to do data analysis without writing a line of r code. We cover hypothesis testing, descriptive statistics, linear and logistic regression with a flavor of machine learning (Random Forest, SVM etc.). Also using graphs such as ROC curves and Area under curves (AUC) to compare various models. To download the dataset and follow on your own follow http://www.learnanalytics.in/datasets/Credit_Scoring.zip
Views: 44393 Learn Analytics
Datamining project using R progamming part1
 
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code in R programming and ppt . Project:Stock predictor for pharmacy(Tablets). Data mining in R Studio
Views: 11591 Saiprasad Shettar
wordcloud R
 
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Wordcloud in R. Make sure you download the wordcloud package. install.package ("wordcloud ")
Views: 3398 The Math Student
jstor: An R package for Analysing Scientific Articles
 
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The interest in the (quantitative) analysis of textual data has increased considerably over the last few years. For researchers investigating the scholarly literature the full text archive of JSTOR (http://www.jstor.org) offers a rich and diverse set of journal articles and other texts. Through its service Data for Research (http://www.jstor.org/dfr/), JSTOR gives researchers the opportunity to analyse this data, by delivering metadata, n-grams and, upon special request, full-text materials. jstor (https://tklebel.github.io/jstor/) enables researchers to easily import the supplied metadata to R. These metadata can either be analysed on their own, or be used in conjunction with n-grams or full-text-data. The presentation will show how jstor supports investigations of scholarly literature, covering the analysis of n-grams and citation analysis. Besides introducing possible applications, the paper will also discuss limitations regarding data quality and possible solutions thereof.
Views: 435 R Consortium
Create A Word Cloud In Microsoft Word
 
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Word clouds are very popular and can form an interesting and attractive image within a document fairly easily - and with no copyright or license to worry about if you've created it yourself. In this quick video I show you how easy it is now to create beautiful word clouds right within Microsoft Word itself - no switching to websites or other applications. Create professional looking word clouds from your own documents in seconds!
Views: 337787 The Tech Train
A Fun Introduction to Text and Data Mining - Federico Nanni
 
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Video recorded at the Workshop On mining Scientific Publications, 19th-23rd June at The University of Toronto, as a part of JCDL 2017 (Joint Conference on Digital Libraries).
Views: 165 OpenMinTeD
#DIS2016 - Jacques Vanginderdeuren - Presenting the Text Mining Hackathon from Euroclear
 
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#DIS2016 - Jacques Vanginderdeuren - Presenting the Text Mining Hackathon from Euroclear - #Datascience #textmining #hackathon #Euroclear Join us on JUNE 17&18, register here: https://textmininghack.eventbrite.co.uk “Euroclear Text Mining & NLP Hackathon ”: Together with the DiHub, we are preparing a hackathon on text mining which will take place on June 17 and 18. Our hope is that the outcome of the hackathon will allow us to take our capabilities in text analytics to the next level, thereby supporting our ambition to develop text-based solutions to facilitate operational work and make it more efficient. Jacques Vanginderdeuren, Head of Advanced Analytics at Euroclear. I took over the so called “Big Data” team at Euroclear 7 months ago after leading the Banking Data Management Team for 10 years, supporting the needs in data of our Credit and Treasury departments and acting as an interface between IT and the business for data-related matters. His objective is to promote advanced analytics throughout the company and to contribute to our transformation into an insight driven organization, whether through use cases in the form of one-shot insights discovery exercises or the implementation of structural solutions with embedded analytics. Euroclear is also one of the innovation partners of the DiHub, representing the financial industry. Subscribing to this channel will help us promote more content. Thank your for supporting our initiative. https://goo.gl/s0cXtC The European Data Innovation Hub is a contributing actor in the data innovation ecosystem and supports data professionals throughout Belgium with networking activities, events, training and meeting facilities, e-learning platform, co-working space and mentorship. We foster grassroots community initiatives and take the burden out of organising them. As a facilitator and catalyst we support the plans and ambition of professionals, academics and government by helping them to connect, organise, share, learn and inspire. http://datasciencebe.com/ https://twitter.com/Datasciencebe http://www.datainnovationhub.eu/ https://twitter.com/Dataeu
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
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In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 172629 Well Academy
JMP Integration and Extensibility with SAS, R, Python (Oct 2017)
 
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In this webinar, we explore the many ways to extend core JMP functionality through its integration with SAS, R, Matlab, and even Python, C, and Javascript.
Views: 941 Julian Parris
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: 1591 IBM Analytics Skills
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 84210 edureka!
Crear nubes de palabras en R
 
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Este video muestra cómo crear nubes de palabras a partir de un corpus de texto en R utilizando la librería tm (text mining). El script utilizado se encuentra en http://www.rpubs.com/rgcmme/PLN-08
Views: 2156 Raúl García Castro
Real-Time Top-R Topic Detection on Twitter with Topic Hijack Filtering
 
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Authors: Kohei Hayashi, Takanori Maehara, Masashi Toyoda, Ken-ichi Kawarabayashi Abstract: Twitter is a ""what's-happening-right-now"" tool that enables interested parties to follow thoughts and commentary of individual users in nearly real-time. While it is a valuable source of information for real-time topic detection and tracking, Twitter data are not clean because of noisy messages and users, which significantly diminish the reliability of obtained results. In this paper, we integrate both the extraction of meaningful topics and the filtering of messages over the Twitter stream. We develop a streaming algorithm for a sequence of document-frequency tables; our algorithm enables real-time monitoring of the top-10 topics from approximately 25% of all Twitter messages, while automatically filtering noisy and meaningless topics. We apply our proposed streaming algorithm to the Japanese Twitter stream and successfully demonstrate that, compared with other online nonnegative matrix factorization methods, our framework both tracks real-world events with high accuracy in terms of the perplexity and simultaneously eliminates irrelevant topics. ACM DL: http://dl.acm.org/citation.cfm?id=2783402 DOI: http://dx.doi.org/10.1145/2783258.2783402
Luís Paulo Reis - Text Mining to Improve Qualitative Data Analysis of Large Document Collections
 
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Closing Conference // 3rd World Conference on Qualitative Research (17 to 19 October 2018 - Lisbon, Portugal) Text Mining to Improve Qualitative Data Analysis of Large Document Collections Text Mining is the process of deriving high-quality information from text using analytical and natural language processing methods. Text analysis involves among others, information retrieval, lexical analysis, pattern recognition, tagging/annotation, information extraction, data mining, visualization, and predictive analytics. The main goal is, essentially, to turn text into valid information for analysis. This Talk introduces the main processes and methodologies of text mining to support qualitative data analysis of large-scale document collections. It aims to contribute to the steadily growing field of qualitative research facing the challenge to consolidate text analysis methods to be able to process vast amounts of text, in a semi-autonomous manner, using modern data and text mining algorithms. The talk illustrates with examples and results from recent LIACC projects on this area with emphasis on our projects on complaints analysis developed together with the Portuguese Government (Min. Economy/ASAE) and several projects developed together with large companies such as Twitómetro/TwitterEcho, VOXX, Time Machine, Financial Sentiment Analysis and Argumentation Mining.
Presenting our Research Paper on Text Analytics
 
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Title: Analyzing Expert Cyber-security Twitter Accounts by Using Thesaurus Methods for Text Analytics Completed under Prof Andreea Cotoranu and Prof Avery Leider at Pace University The link to code and ppt: https://github.com/averma74/Text-Analytics Presented our research paper in the Annual Research Day held in Pace University, PLV campus, New York
Views: 36 Aditee Verma
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With Python | Edureka
 
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( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural-language-processing-course ** ) This video will provide you with a detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this video: 0:46 - Introduction to Big Data 1:45 - What is Text Mining? 2:09- What is NLP? 3:48 - Introduction to Stemming 8:37 - Introduction to Lemmatization 10:03 - Applications of Stemming & Lemmatization 11:04 - Difference between stemming & Lemmatization Subscribe to our channel to get video updates. Hit the subscribe button above https://goo.gl/6ohpTV ----------------------------------------------------------------------------------------------- Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka ----------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - 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: 6059 edureka!
Final Year Projects| An Ontology-Based Text-Mining Method to Cluster Proposals for Research
 
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Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1406 Clickmyproject
Sentiment Analysis: Feelings, not Facts
 
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Brief explanation of Sentiment Analysis along with a few basic examples
Views: 14069 Michael Herman
Crear nubes de n-gramas en R
 
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Este video muestra cómo crear nubes de n-gramas a partir de un corpus de texto en R utilizando la librería tm (text mining). El script utilizado se encuentra en http://www.rpubs.com/rgcmme/PLN-09
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 76701 edureka!
BIGDATA in TELUGU
 
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Follow us on https://t.me/Learnerspage Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. BIGDATA in TELUGU https://youtu.be/jdPhsYZU_5E?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. Data science & Big data in Telugu https://youtu.be/5XQ3lmPVV8M?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD). Tableau in Telugu https://youtu.be/iPvwRyeAGYA?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX In 2020 the world will generate 50 times the amount of data as in 2011. And 75 times the number of information sources (IDC, 2011). Within these data are huge, unparalleled opportunities for human advancement. But to turn opportunities into reality, people need the power of data at their fingertips. Tableau is building software to deliver exactly that. Big Data Tool R Installation in Telugu https://youtu.be/hdTLyC-KL_I?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX https://cran.r-project.org/bin/windows/base/ https://www.rstudio.com/products/rstudio/download/ R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Tableau in Telugu:How to Creat Groups in Charts https://youtu.be/i1z1lGJvQQU?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Data Warehouse in Telugu https://youtu.be/xFLE1_V7u6M Business Intelligence is a technology based on customer and profit-oriented models that reduce operating costs and provide increased profitability by improving productivity, sales, service and helps to make decision-making capabilities at no time. Business Intelligence Models are based on multidimensional analysis and key performance indicators (KPI) of an enterprise R Programming in Telugu:How to Write CVS files and Extract Data from data.[Lesson-3] https://youtu.be/oeh9fyru9-o?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX This video is about: How to write CSV file in R . How to remove the columns from data set or data frame in R R Programming Tutorial in Telugu: How to Read data in R [Lesson2] https://youtu.be/CL0RG4NTuq4?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX In This R Tutorial you will find clear way, how to read the CVS files in R Studio. How to use commands SETWD(), READ.CSV(), HEAD(), TAIL(),VIEW() FETCH DATA FROM SQL TO EXCEL https://youtu.be/IqukX_hKEnE?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Tableau in Telugu: Tableau Colors https://youtu.be/fHvg0irp1ds?list=PLZdcIlxTKvf4pkxc78BW1LSdnT1gWCSQX Pareto Chart Analysis https://youtu.be/TPZaIX4S1TU Pareto Analysis is a statistical technique in decision-making used for the selection of a limited number of tasks that produce significant overall effect. It uses the Pareto Principle (also known as the 80/20 rule) the idea that by doing 20% of the work you can generate 80% of the benefit of doing the entire job. Population Pyramid Chart https://youtu.be/poWV5VsideI Download the file in below link https://drive.google.com/file/d/1eWu8zXxh1QRFQj4OJAkG_S7AuHIDqY04/view A population pyramid also called an "age pyramid" is a graphical illustration that shows the distribution of various age groups in a population
Views: 21065 Learners Page
Bilevel Feature Extraction Based Text Mining
 
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Bi-level Feature Extraction-Based Text Mining
Aspect Based Sentiment Analysis.
 
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This is a Project built as a part of Information Retrieval and Extraction Course at IIIT-Hyderabad. Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e.g., laptops, restaurants) and their aspects (e.g., battery, screen; food, service). By contrast, this task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect. The project is built in python using stanford coreNLP and NLTK as 3rd party tools. github link:-https://github.com/SaujanyaReddy/Aspect-Based-Sentiment-Analysis-IRE-Major-Project dropbox link to ppt and report:-https://www.dropbox.com/sh/krpv30cwdakgr90/AAC-cQ-Vgkm1OpWaokZIEZlba?dl=0 slideshare link to ppt:-http://www.slideshare.net/IndranilMukherjee20/absa-project-60961283
Views: 6311 Indranil Mukherjee
Downloading Data from Google Trends And Analyzing It With R
 
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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/ MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 23828 Manuel Amunategui
text-analysis
 
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What is text analysis? A brief introduction
Views: 67 Shawn Graham
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 465107 Thales Sehn Körting