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Annotation Query Language(AQL) Basic Features - Chapter 2
 
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Text Mining and Analytics Annotation Query Language(AQL) Basic Features - Chapter 2 This video tutorials cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications. analytics | analytics tools | analytics software | data analysis programs | data mining tools | data mining | text analytics | strucutred data | unstructured data |text mining | what is text mining | text mining techniques | AQL | Annotation Query Language More Articles, Scripts and How-To Papers on http://www.aodba.com
Views: 226 AO DBA
Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka
 
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** Natural Language Processing Using Python: https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry. NLP Tutorial : https://www.youtube.com/watch?v=05ONoGfmKvA Subscribe to our channel to get video updates. Hit the subscribe button above. ------------------------------------------------------------------------------------------------------- #NLPin10minutes #NLPtutorial #NLPtraining #Edureka Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ ------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learned content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 56573 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: 472601 sentdex
SQLizer: Query Synthesis from Natural Language
 
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Navid Yaghmazadeh, Yuepeng Wang, Isil Dillig, Thomas Dillig This paper presents a new technique for automatically synthesizing SQL queries from natural language (NL). At the core of our technique is a new NL-based program synthesis methodology that combines semantic parsing techniques from the NLP community with type-directed program synthesis and automated program repair. Starting with a program sketch obtained using standard parsing techniques, our approach involves an iterative refinement loop that alternates between probabilistic type inhabitation and automated sketch repair. We use the proposed idea to build an end-to-end system called SQLIZER that can synthesize SQL queries from natural language. Our method is fully automated, works for any database without requiring additional customization, and does not require users to know the underlying database schema. We evaluate our approach on over 450 natural language queries concerning three different databases, namely MAS, IMDB, and YELP. Our experiments show that the desired query is ranked within the top 5 candidates in close to 90% of the cases and that SQLIZER outperforms NALIR, a state-of-the-art tool that won a best paper award at VLDB'14.
CQL 1: Complex corpus searches with the Corpus Query Language
 
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The Corpus Query Language is designed for complex corpus searches focussing on grammatical and lexical structures. It can exploit part-of-speech tags, lemmas and other attributes and also unspecific or optional conditions. Learn CQL from our web: http://ske.li/cql Quick Start Guide: https://www.sketchengine.eu/quick-start-guide/ attend training: https://www.sketchengine.eu/user-guide/sketch-engine-training/ supported by the ELEXIS project
Views: 2682 Sketch Engine
Azure Log Analytics: Deep dive into the Azure Kusto query language - THR3115
 
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The key factor for a successful transition to Microsoft Azure is governance, and management is one pillar of it. Azure provides a powerful service for that: Azure Log Analytics, which collects telemetry and various data from hybrid architectures (Azure/on-premises/other clouds). With Keyword Query Language (KQL), it's easy to construct log requests and view. In this demo-based session see how to use this language in order to create useful requests and visualize results in dashboards.
Views: 3318 Microsoft Ignite
Text analytics extract key phrases using Power BI and Microsoft Cognitive Services
 
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Download the PDF to keep as reference http://theexcelclub.com/extract-key-phrases-from-text/ FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Or on Android App https://play.google.com/store/apps/details?id=com.PBI.trainigapp Carry out a text analytics like the big brand...only for free with Power BI and Microsoft Cognitive Services. this video will cover Obtain a Text Analytics API Key from Microsoft Cognitive Services Power BI – Setting up the Text Data Setting up the Parameter in Power BI Setting up the Custom function Query(with code to copy) Grouping the text Running the Key Phrase Extraction by calling the custom function. Extracting the key phrases from the returned Json file. Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y Join the online Excel and PowerBI community https://plus.google.com/u/0/communities/110804786414261269900
Views: 5168 Paula Guilfoyle
Einstein Analytics - Natural Language Query
 
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Looking for the latest and greatest of the Salesforce Einstein Analytics platform? Check out this new series of videos! Einstein Analytics connects your sales, service, or any other team with intelligent insights prebuilt, right in Salesforce. Whether you're a developer, admin, or business user, you'll get to see how Einstein Analytics can help you supercharge customer interactions. To test drive it yourself visit: https://www.salesforce.com/analytics-playground/
Views: 1296 Salesforce
Twitter data analysis with IBM Watson Explorer: Unstructured Text Analysis
 
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This video provides an overview of IBM Watson Explorer used to analyze and gain insights from unstructured data, such as Twitter data. For more information about Watson Explorer, please visit: https://www.ibm.com/us-en/marketplace/content-analytics
Views: 3900 IBM Analytics
MLSA - Multi Language Sentiment Analysis
 
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JHU Information Retrieval class project. Performing sentiment analysis on ranked documents retrieved per user query on multiple languages.
Views: 44 Jorge M Ramirez
Data Mining-Query optimization using Document Frequency
 
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Query optimization using Document Frequency
Views: 534 John Paul
Amazing Things NLP Can Do!
 
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In this video I want to highlight a few of the awesome things that we can do with Natural Language Processing or NLP. NLP basically means getting a computer to understand text and help you with analysis. Some of the major tasks that are a part of NLP include: · Automatic summarization · Coreference resolution · Discourse analysis · Machine translation · Morphological segmentation · Named entity recognition (NER) · Natural language generation · Natural language understanding · Optical character recognition (OCR) · Part-of-speech tagging · Parsing · Question answering · Relationship extraction · Sentence breaking (also known as sentence boundary disambiguation) · Sentiment analysis · Speech recognition · Speech segmentation · Topic segmentation and recognition · Word segmentation · Word sense disambiguation · Lemmatization · Native-language identification · Stemming · Text simplification · Text-to-speech · Text-proofing · Natural language search · Query expansion · Automated essay scoring · Truecasing Let’s discuss some of the cool things NLP helps us with in life 1. Spam Filters – nobody wants to receive spam emails, NLP is here to help fight span and reduce the number of spam emails you receive. No it is not yet perfect and I’m sure we still all still receive some spam emails but imagine how many you’d get without NLP! 2. Bridging Language Barriers – when you come across a phrase or even an entire website in another language, NLP is there to help you translate it into something you can understand. 3. Investment Decisions – NLP has the power to help you make decisions for financial investing. It can read large amounts of text (such as news articles, press releases, etc) and can pull in the key data that will help make buy/hold/sell decisions. For example, it can let you know if there is an acquisition that is planned or has happened – which has large implications on the value of your investment 4. Insights – humans simply can’t read everything that is available to us. NLP helps us summarize the data we have and pull out meaningful information. An example of this is a computer reading through thousands of customer reviews to identify issues or conduct sentiment analysis. I’ve personally used NLP for getting insights from data. At work, we conducted an in depth interview which included several open ended response type questions. As a result we received thousands of paragraphs of data to analyze. It is very time consuming to read through every single answer so I created an algorithm that will categorize the responses into one of 6 categories using key terms for each category. This is a great time saver and turned out to be very accurate. Please subscribe to the YouTube channel to be notified of future content! Thanks! https://en.wikipedia.org/wiki/Natural_language_processing https://www.lifewire.com/applications-of-natural-language-processing-technology-2495544
Views: 6989 Story by Data
1. Introduction to SQL (Hindi)
 
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Here's how to Introduction to SQL Structured Query Language Complete Playlist: https://goo.gl/vkpS3h Database Management System (DBMS) Complete Playlist: https://goo.gl/WKHDVx Check Out Our Other Playlists: https://www.youtube.com/user/GeekyShow1/playlists SUBSCRIBE to Learn Programming Language ! http://goo.gl/glkZMr Learn more about subject: http://www.geekyshows.com/ __________________________________________________________ If you found this video valuable, give it a like. If you know someone who needs to see it, share it. If you have questions ask below in comment section. Add it to a playlist if you want to watch it later. ___________________________________________________________ T A L K W I T H M E ! Business Email: [email protected] Youtube Channel: https://www.youtube.com/c/geekyshow1 Facebook: https://www.facebook.com/GeekyShow Twitter: https://twitter.com/Geekyshow1 Google Plus: https://plus.google.com/+Geekyshowsgeek Website: http://www.geekyshows.com/ ___________________________________________________________ Make sure you LIKE, SUBSCRIBE, COMMENT, and REQUEST A VIDEO! :) ___________________________________________________________
Views: 339462 Geeky Shows
Cognos Analytics: Creating Query Calculations
 
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Step-by-step instructions with demos on how to create a report with a number of query calculations in Cognos Analytics (v11).
Views: 8129 Senturus
Intro to Power BI Q&A natural language queries (4-3)
 
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This video is part of the Analyzing and Visualizing Data with Power BI course available on EdX. To sign up for the course, visit: http://aka.ms/pbicourse. To read more: Power BI service https://aka.ms/pbis_gs Power BI Desktop https://aka.ms/pbid_gs Power BI basic concepts tutorial: https://aka.ms/power-bi-tutorial To submit questions and comments about Power BI, please visit community.powerbi.com.
Views: 45872 Microsoft Power BI
Facebook text analysis on R
 
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For more information, please visit http://web.ics.purdue.edu/~jinsuh/.
Views: 12795 Jinsuh Lee
Lecture 8 — Text Similarity (Introduction) - Natural Language Processing | Michigan
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Working With AQL on Text - Chapter 3
 
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Text Mining and Analytics Working With AQL on Text - Chapter 3 This video tutorials cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications. analytics | analytics tools | analytics software | data analysis programs | data mining tools | data mining | text analytics | strucutred data | unstructured data |text mining | what is text mining | text mining techniques | AQL | Annotation Query Language More Articles, Scripts and How-To Papers on http://www.aodba.com
Views: 41 AO DBA
Claire Kelley, Sarah Kelley - Automatic Citation generation with Natural Language Processing
 
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Description Can citations write themselves? Using a topic modeling approach we model similarity between patents from the US patent database via cosine similarity. Mining the text of millions of patents for similarities allows us to algorithmically recommend possible citations for new patents. We use python, pyspark and big query to parallelize complex operations over millions of rows of data. Abstract In this project we use natural language processing to investigate ways to generate recommendations for citations for new patents. Using a topic modeling approach and cosign similarity we attempt to recommend patents similar to the text of a potential new patent. This would allow inventors and engineers to quickly reference patents similar to their own and easily cite them. From an architectural stand point we use big query to handle data ingestion and pre-processing. The data management functionality of big query is key because there are millions of patents some of which may contain hundreds of pages of text. After pre processing in big query the bulk of our analytic work is done in spark through the python wrapper which allows for easy parallelization of the analytical portion. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 536 PyData
Advanced Analytics with R and SQL
 
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R is the lingua franca of Analytics. SQL is the world’s most popular database language. What magic can you make happen by combining the power of R and SQL for Data Science and Advanced Analytics? Imagine the power of exploring, transforming, modeling, and scoring data at scale from the comfort of your favorite R environment. Now, imagine operationalizing the models you create directly in SQL Server, allowing your applications to use them from T-SQL, executed right where your data resides. Come learn how to build and deploy intelligent applications that combine the power of R, SQL Server, thousands of open source R extension packages, and high-performance implementations of the most popular machine learning algorithms at scale.
Natural Language Processing in Artificial Intelligence in Hindi | NLP with Demo and Examples
 
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Views: 2638 Gate Smashers
Basic Searching in Splunk
 
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In this video we demonstrate how to perform basic searches, use the timeline and time range picker, and use fields in the Splunk Search & Reporting app.
Views: 96383 Splunk How-To
COUNTIFS in Power Query? Excel Magic Trick 1549
 
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Download Excel Start Files: https://people.highline.edu/mgirvin/YouTubeExcelIsFun/EMT1549-1550.xlsx Entire page with all Excel Files for All Videos: http://people.highline.edu/mgirvin/excelisfun.htm In this video learn how to use the Power Query Group By feature to simulate the COUNTIFS function. See how to count with one condition, See how to Count with Two Conditions
Views: 4544 ExcelIsFun
Basic Excel Business Analytics #34: Power Query: Import & Merge Multiple Sources Access, Text, Excel
 
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Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn about how to use Power Query to Import & Merge Multiple File Types (Access, Text, and Excel) into one Proper data Set using the Append feature. Power Query Tab, Get External Data. Excel 2016: Data Tab, Get and Transform group. Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
Views: 7575 ExcelIsFun
Jiwon Seo - SociaLite Python integrated Query Lamguage for Big Data Analysis
 
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PyData SV 2014 SociaLite is a Python-integrated query language for big data analysis. It makes big data analysis simple, yet achieves fast performance with its compiler optimizations, often more than three orders of magnitude faster than Hadoop MapReduce programs. For example, PageRank algorithm can be implemented in just 2 lines of SociaLite query, which runs nearly as fast as an optimal C implementation. High-level abstractions in SociaLite help implement distributed data analysis algorithms. For example, its distributed in-memory tables allow large data to be stored across multiple machines, and with minimal user annotations, fast distributed join operations can be performed. Moreover, its Python integration makes SociaLite very powerful. We support embedding and extending, where embedding supports using SociaLite queries directly in Python code, and extending supports using Python functions in SociaLite queries. To support embedding, we apply source code rewriting that transforms SociaLite queries to invoke SociaLite runtime interfaces. For extending, we keep track of functions defined in Python interpreter and make them accesible from SociaLite. The integration makes it easy to implement various data mining algorithms in SociaLite and Python. I will demonstrate in the talk a few well-known algorithms implemented in SociaLite, including PageRank, k-means, and logistic regression. The high-level queries can achieve fast performance with various optimizations. The queries are compiled into Java bytecode with compiler optimizations applied, such as prioritizations or pipelined evaluation. Also, the runtime system gives its best effort to achieve full utilizations of multi-core processors as well as network bandwidths. With the compiler optimizations and the runtime system we achieve very fast performance that is often close to optimal C implementations.
Views: 412 PyData
Excel Power Query #03: Import Multiple Text Files in 1 Step, and Amazing Pivot Chart For Grade Data
 
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Download file: http://people.highline.edu/mgirvin/excelisfun.htm See how to import three text files (Tab Delimited) that contain Student Grade Data for a 35 year period using the Powery Query 1 Step Method, and then make an amazing Pivot Chart with Dynamic Labels: 1. (00:08 minute mark) Problem Setup 2. (02:45 minute mark) Import multiple Text Files (Tab Delimited) from a single Folder 3. (06:43 minute mark) Build 3 PivotTables for 3 Class Grade 4. (09:22 minute mark) Chart with Custom Labels 5. (09:58 minute mark) Create Labels for chart that are connected to the PivotTable and Cells. Text formulas with TEXT and CHAR functions. Miguel Escobar and Ken Puls are doing online workshops on Power Query www.powerquery.training . This training is not by excelisfun.
Views: 43517 ExcelIsFun
Common SQL Queries converted for the Firebase Database - The Firebase Database For SQL Developers #4
 
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Check out our old, but still good blog post: https://goo.gl/YzkWsC Welcome to the fourth video in the Firebase Database for SQL Developers series! This video translates 8 common SQL queries to Firebase Database queries. Watch more videos from this series: https://goo.gl/ZDcO0a Subscribe to the Firebase Channel: https://goo.gl/9giPHG
Views: 101088 Firebase
DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 3 Query Likelihood Retrieval Function
 
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https://www.coursera.org/learn/text-retrieval
Views: 164 Ryo Eng
Webtrekk Analytics Bot / AskBy.ai
 
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AskBy.ai translates natural language into query language.
Views: 601 Sajagan Thirugnanam
Running Queries: Wildcards
 
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A comparison of the two wildcard symbols $ and *. This video explains how the $ can substitute 1 character of text while the * substitutes multiple characters of text. SQL examples are also provided.
Natural Language Processing (NLP) Tutorial | Data Science Tutorial | Simplilearn
 
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Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Python for Data Science Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Science-NLP-6WpnxmmkYys&utm_medium=SC&utm_source=youtube The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants. Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization. Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it's modeling, and implementation using SAS. As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis. Who should take this course? There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. Analytics professionals who want to work with Python 2. Software professionals looking for a career switch in the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in Analytics and Data Science 5. Experienced professionals who would like to harness data science in their fields 6. Anyone with a genuine interest in the field of Data Science For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 28143 Simplilearn
Replace Values and Column Data Types with Power BI (Power Query)
 
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A short updated video on how to replace values in Power BI (Power Query) and how to also replace column data types with a few simple functions. Old video: https://www.youtube.com/watch?v=ikzeQgdKA0Q
Views: 15028 The Power User
TF-IDF for Machine Learning
 
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Quick overview of TF-IDF Some references if you want to learn more: Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf Scikit's implementation: http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer Scikit's code example for feature extraction: http://scikit-learn.org/stable/modules/feature_extraction.html Stanford notes: http://nlp.stanford.edu/IR-book/html/htmledition/tf-idf-weighting-1.html
Views: 38581 RevMachineLearning
Convert Text to Time Values with Power Query (Data Cleansing Part 3)
 
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This is the third video in a series of solutions for our Data Cleansing Challenge. In this video I explain how to use Power Query to convert the time stored as text into numeric time values in Excel. Download the Excel file to follow along: Read the full article: https://www.excelcampus.com/powerquery/convert-text-to-time-values-power-query/ In a previous video (https://youtu.be/uhzLYTupl9I) I shared this challenge to convert time/duration stored as text into time values that can be used for calculations and analytics. Thanks to everyone that commented on the video and blog post with solutions. In this video we look at a solution posted by Walt. The solution uses Power Query to extract/split the time components (hours, minutes, seconds into separate columns. We then merge the columns and convert the data type to a duration. Checkout these articles if you are new to Power Query: Power Query Overview: An Introductions to Excel's Most Powerful Data Tool (https://www.excelcampus.com/powerquery/power-query-overview/) The Complete Guide to Installing Power Query (https://www.excelcampus.com/tables/excel-tables-tutorial-video/) Here are links to the other solution videos: Part 1 using Text Formulas: https://youtu.be/rqypEnQszPk Part 2 using SUMPRODUCT: https://youtu.be/TzEVSlguFso In the next video we look at how to use VBA UDF's (User Defined Functions) to solve the challenge (video coming soon).
Views: 1633 Excel Campus - Jon
Lecture 3 — Natural Language Content Analysis - Part 1
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
R Practice Session with RStudio and VMWare
 
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This course is designed to learn the foundations of data science. It covers fundamental tools and techniques such as Structured Query Language (SQL), Machine Learning and Data Mining algorithms, particularly prediction/classification methods and basic Map Reduce routine for Big Data. R programming language is chosen to perform all the exercises. This course presents real life applications of the techniques and introduces the intuition behind them. Throughout the course work, you solve several exercises to help understand the techniques as well as add them to your skill set. After completing this course, you will have all the necessary tools to implement, execute and explain your data exploration, predictive analysis and algorithms. Please subscribe this channel and visit http://datasciencefloor.com/ for more information. Send your queries at [email protected]
Views: 454 Data Science Floor
Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python
 
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This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines. - Natural Language Processing (Part 1): Introduction to NLP & Data Science - Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python - Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python - Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python - Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python - Natural Language Processing (Part 6): Text Generation with Markov Chains in Python All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial
Views: 1105 Alice Zhao
Basic Excel Business Analytics #30: Excel 2016 Power Query: Data Ribbon Tab, Get and Transform
 
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Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn how to import multiple Text Files (.txt) from a folder into Excel using the new Excel 2016 Power Query: Data Ribbon Tab, Get and Transform group. Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
Views: 88626 ExcelIsFun
Reformat Reports with Power Query
 
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Transform ugly Excel reports in minutes with Power Query. Download the workbook and written step by step instructions here: http://www.myonlinetraininghub.com/reformat-excel-reports-with-power-query
Views: 7583 MyOnlineTrainingHub
What is an Ontology
 
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Description of an ontology and its benefits. Please contact [email protected] for more information.
Views: 152784 SpryKnowledge
BigDataX: Queries for data streams
 
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Big Data Fundamentals is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn how big data is driving organisational change and essential analytical tools and techniques including data mining and PageRank algorithms. Enrol now! http://bit.ly/2rg1TuF
Basic Excel Business Analytics #32: Power Query Import Multiple Excel Files with Multiple Sheets
 
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Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn how to import multiple Excel workbooks (each with the store name in the file name) with multiple sheets in each workbook (each sheet contains the Sales Rep name) and import the sales data into a proper data set, including a column for the sales rep name (data from the sheet tab names) and a column for the store name (data from file name): 1) (00:04) Download File Information 2) (00:23) Look at Excel Workbooks that need to be imported, including the names of each Sales Rep on each sheet tab. 3) (01:20) Power Query, From File, From Folder, to import files from a folder 4) (02:07) Remove Other Columns, being sure to keep file with File name, which contains the store name. 5) (02:24) Add Column and use Power Query Function called Excel.Workbook, so that we can extract the data from the Excel workbook. 6) (03:13) Use Replace Values feature in Power Query to extract the store name from the file name. 7) (03:47) Remove Content Column 8) (03:54) Expand data Column which will expose the Data Column (Data in Excel Workbook), Item Column (contains sheet name data), Kind Column (contains object information such as: Sheet, Table and Defined Names). 9) (04:12) Filter the Kind Column to remove Tables and Defined Names and keep Sheets only. 10) (04:39) Filter Item column to remove sheets that were not properly named (have default names such as Sheet1, Sheet2). We use the “Does Not Contain” Filter to keep sheets that do not contain the text “sheet”; another way to think about it is: “Filter out sheet tabs that have the text ‘sheet’ in them” 11) (05:15) Amazing results: data from sheet tabs and file names is retained for proper data set. 12) (05:27) Remove columns that are not File Name (Store name), Item (Sales Rep Name) and Data. 13) (05:37) Expand data from Excel Workbooks. 14) (05:42) “Use First Rows As Headers” to promote the Field Names from the first sheet in the first workbook to Field Names. 15) (05:53) Filter out Field names from other Sheet tabs. 16) (06:48) Rename Columns 17) (07:15) Add correct Data Types before importing 18) (07:37) Close and Load To a Table on Existing Sheet 19) (08:11)Summary and Conclusion Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
Views: 8924 ExcelIsFun
Basic Excel Business Analytics #35: Power Query to Get Data From Web Site & Import into Excel.
 
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Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn about How to use Power Query to Get Data From the Web and Import into Excel. Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video. Power Query Tab, Get External Data, From Web. Excel 2016: Data Tab, Get and Transform group.
Views: 13530 ExcelIsFun
2014 IEEE DATA MINING Efficient Prediction of Difficult Keyword Queries over Databases
 
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - [email protected] Our Website: www.globalsofttechnologies.org
Learn Excel - Sentiment Analysis - Podcast 2062
 
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It is easy to quantify survey data when it is multiple choice You can use a pivot table to figure out what percentage each answer has But what about free-form text answers? These are hard to process if you have hundreds or thousands of them. Sentiment Analysis is a machine-based method for predicting if an answer is positive or negative. Microsoft offers a tool that does Sentiment Analysis in Excel - Azure Machine Learning. Traditional sentiment analysis requires a human to analyze and categorize 5% of the statements. Traditional sentiment analysis is not flexible - you will rebuild the dictionary for each industry. Excel uses MPQA Subjectivity Lexicon (read about that at http://bit. ly/1SRNevt) This generic dictionary includes 5,097 negative and 2,533 positive words Each word is assigned a strong or weak polarity This works great for short sentences, such as Tweets or Facebook posts It can get fooled by double-negatives To install, go to Insert, Excel Store, search for Azure Machine Learning Specify an input range and two blank columns for the output range. The heading for the input range has to match the schema: tweet_text Companion article at: http://sfmagazine.com/post-entry/may-2016-excel-sentiment-analysis/
Views: 5551 MrExcel.com
Perform a Lookup with Power Query
 
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Check out my Blog: http://exceltraining101.blogspot.com This video show how to use Power Query to perform lookups. For small data sets you are better off with Excel's other lookup function, but if you are dealing with large data sets (over half a million) you may be better off using Power Query. This method also would make it easier if you do this on a recurring basis (daily, weekly, etc) and didn't/couldn't write macros. P.S. Feel free to provide a comment or share it with a friend! --------------------- #exceltips #exceltipsandtricks #exceltutorial #doughexcel --------------------- Excel Training: https://www.exceltraining101.com/p/training.html Excel Books: https://www.amazon.com/shop/dough
Views: 71187 Doug H
Natural Language Query
 
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Ability to simply ask questions about data in Salesforce
Views: 305 NuikuInc
Topic Extraction with MeaningCloud and Graphileon (Neo4j Online Meetup #47)
 
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Topic extraction is very often a crucial step in Natural Language Processing. It results in lists of entities, concepts, phrases, numbers that often are the input for further processes, used for Competitive Intelligence, Social Media Analysis and Search and Content Recommendation. The items that are extracted are very diverse. Often they are also densely connected. These characteristics are in the sweet spot of graph databases, and more precisely in graph databases that use a property graph model, because these offer the possibility to add other that results from post-processing, like similarity coefficients, sentiment scores and user-ratings. During the meetup , Tom Zeppenfeldt will illustrate how the Graphileon platform can be used to connect various webservices ([NewsApi](https://newsapi.org/) , [Lateral Article Extractor](https://lateral.io/docs/article-extractor) and [MeaningCloud](tps://www.meaningcloud.com) ) to process articles and use Neo4j's Cypher and graph algorithms to find similarities. ----------------------------- ABOUT THE SPEAKER ----------------------------- Tom Zeppenfeldt is founder of Graphileon, supplier of a graph-based application development platform. After a career in international development, a sector in which information requirements change rapidly, he started developing tools that allowed non-developers to explore data sets and build flexible applications. ----------------------------- ONLINE DISCUSSIONS ----------------------------- We'll be taking questions live during the session, but if you have any questions before or after be sure to post them in the project's thread in the Neo4j Community Site (https://community.neo4j.com/t/topic-extraction-with-meaning-cloud-and-graphileon-online-meetup/4332). ----------------------------- TIME ----------------------------- 08:00 PST (UTC - 8 hours) 11:00 EST (UTC - 5 hours) 16:00 UTC 16:00 GMT (UTC + 00 hours) 17:00 CET (UTC + 1 hours) ---------------------------------------------------------------------------------------- WANT TO BE FEATURED IN OUR NEXT NEO4J ONLINE MEETUP? ---------------------------------------------------------------------------------------- We select talks from our Neo4j Community site! community.neo4j.com To submit your talk, post in in the #projects (if including a link to github or website) or #content (if linking to a blog post, slideshow, video, or article) categories. ------------------------------------------------------------------------- VOTE FOR THE PRESENTATIONS YOU'D LIKE TO SEE! ------------------------------------------------------------------------- 'VOTE' for the projects and content you'd like to see! Browse the projects and content categories in our community site and 'heart' the ones you're interested in seeing! community.neo4j.com Have a suggestion or request for a certain talk? Email us at [email protected]
Views: 1519 Neo4j