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Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
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** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For Natural Language Processing Training call us at US: +18336900808 (Toll Free) or India: +918861301699 , Or, write back to us at [email protected]
Views: 4997 edureka!
Python Text Mining with nltk
 
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Link to our course :  http://rshankar.com/courses/autolayoutyt7/ In this course, we have been looking at Regular expressions, a tool that helps us mine text but in this video i wish to give you a flavor of a Python package called nltk. Since this course is about finding patterns in text, it is only fair that you know about another package that offers a lot of help in this direction. Reference: https://www.nltk.org/ https://en.wikipedia.org/wiki/Text_mining https://www.deviantart.com/sirenscall/art/The-Highwayman-26312892 https://www.deviantart.com/enricogalli/art/Moby-Dick-303519647 Images courtesy: Designed by Freepik from www.flaticon.com Script: If you look at jobs advertised for data analysts or data scientists, you will often come across the term - text mining It is the process of deriving useful information from text. Text mining is in itself a fascinating subject and involves tasks such as text classification, text clustering, sentiment analysis and much more. The goal of text mining is to turn text into data for analysis. In this course, we have been looking at Regular expressions, a tool that helps us mine text but in this video i wish to give you a flavor of a Python package called nltk. Since this course is about finding patterns in text, it is only fair that you know about another package that offers a lot of help in this direction. nltk stands for the natural language toolkit and is an open source community driven project. nltk helps us build Python programs to work with human language data. So for example if you wish to create a spam detection program, or movie review program, nltk offers a lot of helper functions. The goal of this video to inform you that such a package exists and show you some basic functionality. If you like what you see, do let me know and I will add more videos on this subject. So we will start with a new Jupyter notebook. I already have the nltk package . If you do not, you will need to get it, please. nltk comes with some example books. We can import these books or corpora as follows. Perhaps some of these titles may be familiar to you. So lets take Moby Dick. Its data is stored in a Text object. Can we find how many words the book contains? Ok, now how about unique words? Hmm. Less than 10 percent of the total words. An interesting thing we may wish to do is examine the frequency of words. This is often done with speeches of various politicians. So for example you may wish to see the most frequent words spoken by a politician before an election and the frequency after elections. So lets import FreqDist and assign to it the text of Moby Dick. So the keys of this object are all the words and we can see the values which are the frequency of the words. Moby Dick is a story of a whale. Lets see how many times this word figures in the book. The keys are case sensitive of course. Let us now focus on popular words in the book. But not words such as ‘has’ or ‘the’ So lets say we want to find the words of length greater than 6 which appear more than 100 times in the book. And lets sort these words for good measure. Interesting set of words. Some such as Captain would be expected i guess. Lets come back to a topic we have seen before - Word tokenization. So we have our sentence like so. And we want to break this sentence into various tokens or words. Earlier we used the function split() so lets do that again. As you can see, the output in this case bundles the full stop with a word. Also what about the word shouldn’t. Is it one token or 2? nltk provides a function that is more language syntax aware. Lets use it. I will leave you to evaluate the differences. One last thing. Here we have a slice of a wonderful poem called the HighwayMan. Now we wish to break this text into its sentences. Can we do it? Regular expressions can help but why use Regex when we have a solution. nltk offers a sent_tokenize function. Lets use it. Isn’t this poem beautiful.. Ok guys thats it for now. If you want more videos on this subject do let me know. Take care.
Views: 110 funza Academy
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 393824 sentdex
Projects In Machine Learning | NLP for Text Classification with NLTK & Scikit-learn | Eduonix
 
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In this tutorial, we will cover Natural Language Processing for Text Classification with NLTK & Scikit-learn. Remember the last Natural Language Processing project we did? (http://bit.ly/2Ittrop) We will be using all that information to create a Spam filter. This tutorial will also cover Feature Engineering and ensemble NLP in text classification. This project will use Jupiter Notebook running Python 2.7. Let's get started! You will find the source code to this project here: https://github.com/eduonix/nlptextclassification Check out our other Machine Learning Projects here: http://bit.ly/2HIXvvV Don't forget to check our new project on Data Science Foundational Program on Kickstarter. This program incorporates everything from beginner-level concepts to real-world implementation along with 4 courses, 2 e-books, Interview preparation guide, multiple labs, numerous practice tests and much more. Read more - https://kck.st/2CuIkay Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. http://bit.ly/2ITJDQb Follow Eduonix on other social networks: ■ Facebook: https://goo.gl/ZqRVjS ■ Twitter: https://goo.gl/oRDaji ■ Google+: https://goo.gl/mfPaxx ■ Instagram: https://goo.gl/7f5DUC | @eduonix ■ Linkedin: https://goo.gl/9LLmmJ ■ Pinterest: https://goo.gl/PczPjp
Best books on Data Mining
 
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Best books on Data Mining
Views: 266 Books Magazines
Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka
 
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** Natural Language Processing Using Python: https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry. NLP Tutorial : https://www.youtube.com/watch?v=05ONoGfmKvA Subscribe to our channel to get video updates. Hit the subscribe button above. ------------------------------------------------------------------------------------------------------- #NLPin10minutes #NLPtutorial #NLPtraining #Edureka Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ ------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learned content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For Natural Language Processing Training call us at US: +18336900808 (Toll Free) or India: +918861301699 , Or, write back to us at [email protected]
Views: 7242 edureka!
Manage All Unstructured Data with SAS® Text Analytics
 
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http://www.sas.com/en_us/software/analytics/text-miner.html Learn how text analytics can help you tap into the power of your unstructured data and provide more complete fact-based decisions. SAS TEXT MINER Get faster, deeper insight from unstructured data. Why limit yourself to analyzing legacy data? Our text mining software lets you easily analyze text data from the web, comment fields, books and other text sources. Discover new information, topics and term relationships that deepen your understanding. And add what you learn to your models to improve lift and performance. Benefits: * Improve model performance. * Add subject-matter expertise. * Automatically know more. * Determine what's hot and what's not. LEARN MORE ABOUT SAS TEXT MINER http://www.sas.com/en_us/software/analytics/text-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 29344 SAS Software
Number 1 Recommendation When Starting To Learn NLP
 
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Learn The Number 1 Recommendation When Starting To Learn NLP The Check out https://goo.gl/jusPUm to enjoy more great free video training.
Views: 11746 NLPTimes
SAS® Text Analytics Software Demo
 
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http://www.sas.com/en_us/software/analytics/text-miner.html SAS Text Analytics help companies address big data issues that arise from unstructured content by applying linguistic rules and statistical methods. SAS TEXT MINER Get faster, deeper insight from unstructured data. Why limit yourself to analyzing legacy data? Our text mining software lets you easily analyze text data from the web, comment fields, books and other text sources. Discover new information, topics and term relationships that deepen your understanding. And add what you learn to your models to improve lift and performance. Benefits: * Improve model performance. * Add subject-matter expertise. * Automatically know more. * Determine what's hot and what's not. LEARN MORE ABOUT SAS TEXT MINER http://www.sas.com/en_us/software/analytics/text-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss To learn more about SAS Text Analytics, visit http://www.sas.com/textanalytics
Views: 23716 SAS Software
REVIEW: The Bestseller Code - An Algorithm Chooses The Best Book Ever
 
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Buy the Bestseller Code by Jodie Archer & Matthew L Jockers on Amazon: http://amzn.to/2el5aa0 Buy The Circle by Dave Eggers on Amazon: http://amzn.to/2eM4etH --- Visit us on social media too! Twitter: www.twitter.com/pagetopixels Facebook: www.facebook.com/pagetopixels BACK IN SUMMER by Nicolai Heidlas Music https://soundcloud.com/nicolai-heidlas Creative Commons — Attribution 3.0 Unported— CC BY 3.0 http://creativecommons.org/licenses/b... Music provided by Audio Library https://youtu.be/sGsC98vR4Q4
Views: 342 Page To Pixels
Statistical Text Analysis for Social Science
 
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What can text analysis tell us about society? Corpora of news, books, and social media encode human beliefs and culture. But it is impossible for a researcher to read all of today's rapidly growing text archives. My research develops statistical text analysis methods that measure social phenomena from textual content, especially in news and social media data. For example: How do changes to public opinion appear in microblogs? What topics get censored in the Chinese Internet? What character archetypes recur in movie plots? How do geography and ethnicity affect the diffusion of new language? In order to answer these questions effectively, we must apply and develop scientific methods in statistics, computation, and linguistics. In this talk I will illustrate these methods in a project that analyzes events in international politics. Political scientists are interested in studying international relations through *event data*: time series records of who did what to whom, as described in news articles. To address this event extraction problem, we develop an unsupervised Bayesian model of semantic event classes, which learns the verbs and textual descriptions that correspond to types of diplomatic and military interactions between countries. The model uses dynamic logistic normal priors to drive the learning of semantic classes; but unlike a topic model, it leverages deeper linguistic analysis of syntactic argument structure. Using a corpus of several million news articles over 15 years, we quantitatively evaluate how well its event types match ones defined by experts in previous work, and how well its inferences about countries correspond to real-world conflict. The method also supports exploratory analysis; for example, of the recent history of Israeli-Palestinian relations.
Views: 978 Microsoft Research
Natural Language Processing (NLP)- Part 1
 
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Natural language processing is a very important part of machine learning. Many of you are doing your final year thesis on NLP. But in traditional books and tutorials these thing are theoretically explained, whereas application based lessons are much needed to complete projects. I hope you like these videos. What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. What is Artificial Intelligence? (AI) Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g/videos?sub_confirmation=1 Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] #machinelearning #bigdata #ML #DataScience #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #BLE #DataProcessing #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials
Views: 174 Planeter
mining text data projects
 
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Views: 68 PHD Projects
Text Analytics with R | Analyzing Sentiments with BoxPlot Chart | Data Science Tutorial
 
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In this data science text analytics with R tutorial, I have talked about how you can analyze the sentiments from text using box plot chart in R. It helps us comparing sentiments of multiple texts or speeches or books to better analyze the sentiments from it. Text mining in R is done with help of sentimentr package and tm package. Text analytics with R,analyzing sentiments with boxplot chart,data science tutorial,boxplot chart,plotting sentiments,sentiment analysis in R,sentiment analysis with R,how to analyzing text in R,text processing in R,natural languge processing,NLP,nlp in R,r nlp,nlp anlaysis in R,what is text mining,how to do text mining in R,how to do NLP in R,NLP processing in R,process nlp in R,R tutorial for beginners,beginners tutorial for R,learn NLP using R
Python Text Analysis -  Find Protagonist in a Book!!
 
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GET CODE HERE: http://robotix.com.au/#/videos/123 Python text analysis using the TextBlob modue available here: http://textblob.readthedocs.io/en/dev/ Using the code above you can read entire books imported to python as text files from Project Gutenberg SOCIAL: Twitter: https://twitter.com/SanjinDedic Facebook Page: https://www.facebook.com/RobotixAu/ LinkedIn: https://au.linkedin.com/in/sanjin-dedic-a028b9113 MINDS: https://www.minds.com/SanjinDedic WEBSITES Techxellent.com.au Robotix.com.au -~-~~-~~~-~~-~- Latest and Best Arduino Playlist in Collaboratio with DFRobot: https://www.youtube.com/playlist?list=PL_92WMXSLe_86NTWf0nchm-EmQIwccEye -~-~~-~~~-~~-~-
Views: 429 Robotix
6 Books for Improving Your English: Advanced English Lesson
 
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More book recommendations: https://www.speakenglishwithvanessa.com/6-book-recommendations-english-learners/ Download my free e-book: "5 Steps To Becoming A Confident English Speaker" http://www.speakenglishwithvanessa.com/free-ebook --------------------------------------------------------------------- Subscribe and follow on social media! I'd love to meet you! YouTube: http://www.youtube.com/subscription_center?add_user=theteachervanessa Facebook: http://www.facebook.com/speakenglishwithvanessa Google+: https://plus.google.com/+TeacherVanessa/ Send us a postcard from your country: Vanessa Prothe PO Box 104 Asheville, NC 28802 USA --------------------------------------------------------------------- Buy the books in this video! Support yourself and support me! Fantastic Mr. Fox: http://amzn.to/2zcUV0b The Curios Incident of the Dog in the Night: http://amzn.to/2ytEsRO Diary of a Young Girl: http://amzn.to/2AILYxW Harry Potter: http://amzn.to/2ytf9iE Hunger Games: http://amzn.to/2AJKUK4 Chronicles of Narnia: http://amzn.to/2AHddZY Kite Runner: http://amzn.to/2ytjAdr A Thousand Splendid Suns: http://amzn.to/2AZ3taf How to Win Friends and Influence People: http://amzn.to/2ytm4Z0 --------------------------------------------------------------------- Speak English With Vanessa helps English learners to speak American English fluently, naturally, and confidently. To become a fluent English speaker and have English conversations with a native English speaker, go to http://www.speakenglishwithvanessa.com
Digital Text Mining
 
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Matthew Jockers, University of Nebraska-Lincoln assistant professor of English, combines computer programming with digital text-mining to produce deep thematic, stylistic analyses of literary works throughout history -- an intensely data-driven process he calls macroanalysis. It's opening up new methods for literary theorists to study literature. http://research.unl.edu/annualreport/2013/pioneering-new-era-for-literary-scholarship/ http://research.unl.edu/
How to Succeed in any Programming Interview 2018
 
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I'll show you the 5 steps to succeed in any technical interview. We'll go over what a great study plan looks like, resources to help you find jobs, and how you should conduct yourself during the interview. Please Subscribe! That is the one thing you could do that would make me happiest. Links from the video below My Code School (Intro to Data Structures): https://www.youtube.com/watch?v=92S4zgXN17o&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=1 MIT Open Courseware (Intro to Algorithms): https://www.youtube.com/watch?v=HtSuA80QTyo&index=1&list=PLSX2U_ZE4Huk19DPn34oZlygPbsig380X HackerEarth and HackerRank: https://www.hackerearth.com/ https://www.hackerrank.com/ Programming Interview Exposed: http://books.lihui.org/cs2/Wiley%20-%20Programming%20Interviews%20Exposed_Secrets%20to%20Landing%20Your%20Next%20Job%20(2000).pdf Cracking the Coding Interview: https://github.com/yuanhui-yang/Cracking-the-Coding-Interview/blob/master/Cracking%20the%20Coding%20Interview%20-%204th%20Edition.pdf How to Conduct a Mock Interview: http://web.stanford.edu/dept/CTL/Oralcomm/Microsoft%20Word%20-%20How%20to%20Conduct%20Mock%20Interviews.pdf Angellist: https://angel.co/ HackerNews Who's Hiring: https://news.ycombinator.com/item?id=13541679 Making a great resume: https://medium.com/@order_group/job-interview-and-good-resume-cv-tips-for-programmers-from-our-experts-3aa626c825ab#.ssdw5a2th Passing the Interview: http://blog.triplebyte.com/how-to-pass-a-programming-interview Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 311627 Siraj Raval
Data Science Essentials in Python, the Book
 
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Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Get your copy of the book at https://pragprog.com/book/dzpyds/data-science-essentials-in-python
Views: 331 Dmitry Zinoviev
NLP: Understanding the N-gram language models
 
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Hi, everyone. You are very welcome to week two of our NLP course. And this week is about very core NLP tasks. So we are going to speak about language models first, and then about some models that work with sequences of words, for example, part-of-speech tagging or named-entity recognition. All those tasks are building blocks for NLP applications. And they're very, very useful. So first thing's first. Let's start with language models. Imagine you see some beginning of a sentence, like This is the. How would you continue it? Probably, as a human,you know that This is how sounds nice, or This is did sounds not nice. You have some intuition. So how do you know this? Well, you have written books. You have seen some texts. So that's obvious for you. Can I build similar intuition for computers? Well, we can try. So we can try to estimate probabilities of the next words, given the previous words. But to do this, first of all,we need some data. So let us get some toy corpus. This is a nice toy corpus about the house that Jack built. And let us try to use it to estimate the probability of house, given This is the. So there are four interesting fragments here. And only one of them is exactly what we need. This is the house. So it means that the probability will be one 1 of 4. By c here, I denote the count. So this the count of This is the house,or any other pieces of text. And these pieces of text are n-grams. n-gram is a sequence of n words. So we can speak about 4-grams here. We can also speak about unigrams, bigrams, trigrams, etc. And we can try to choose the best n,and we will speak about it later. But for now, what about bigrams? Can you imagine what happens for bigrams, for example, how to estimate probability of Jack,given built? Okay, so we can count all different bigrams here, like that Jack, that lay, etc., and say that only four of them are that Jack. It means that the probability should be 4 divided by 10. So what's next? We can count some probabilities. We can estimate them from data. Well, why do we need this? How can we use this? Actually, we need this everywhere. So to begin with,let's discuss this Smart Reply technology. This is a technology by Google. You can get some email, and it tries to suggest some automatic reply. So for example, it can suggest that you should say thank you. How does this happen? Well, this is some text generation, right? This is some language model. And we will speak about this later,in many, many details, during week four. So also, there are some other applications, like machine translation or speech recognition. In all of these applications, you try to generate some text from some other data. It means that you want to evaluate probabilities of text, probabilities of long sequences. Like here, can we evaluate the probability of This is the house, or the probability of a long,long sequence of 100 words? Well, it can be complicated because maybe the whole sequence never occurs in the data. So we can count something, but we need somehow to deal with small pieces of this sequence, right? So let's do some math to understand how to deal with small pieces of this sequence. So here, this is our sequence of keywords. And we would like to estimate this probability. And we can apply chain rule,which means that we take the probability of the first word, and then condition the next word on this word, and so on. So that's already better. But what about this last term here? It's still kind of complicated because the prefix, the condition, there is too long. So can we get rid of it? Yes, we can. So actually, Markov assumption says you shouldn't care about all the history. You should just forget it. You should just take the last n terms and condition on them, or to be correct, last n-1 terms. So this is where they introduce assumption, because not everything in the text is connected. And this is definitely very helpful for us because now we have some chance to estimate these probabilities. So here, what happens for n = 2, for bigram model? You can recognize that we already know how to estimate all those small probabilities in the right-hand side,which means we can solve our task. So for a toy corpus again,we can estimate the probabilities. And that's what we get. Is it clear for now? I hope it is. But I want you to think about if everything is nice here. Are we done?
Views: 387 Machine Learning TV
Text-Mining Universtity Maastricht - Game of Thrones
 
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Another great project from the Text-Mining course of the Department of Data Science and Knowledge Engineering from the University of Maastricht by Rik Claessens. Automatic detection of persons, locations and travel patterns from the free text of the books.
Introduction to Text and Data Mining
 
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Heard about Text and Data Mining (TDM) and wondering if it might be a good fit for your research? Find out what text and data mining is and how it can usefully be applied in a research context. Also learn about data sources for text and data mining projects and support, tools, and resources for learning more.
Views: 25 UniSydneyLibrary
▶ 5 Most Used Data Mining Software || Data Mining Tools -- Famous Data Mining Tools
 
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»See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on Data Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner Here We're Going to Learn Which Software is best to use in Data Mining Field R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science. আধুনিক প্রযুক্তির ব্যবহার বৃদ্ধির সাথে অতি দ্রুত ডেটা উৎপন্ন হচ্ছে। টেক জায়ান্ট আইবিএম জানায় ইন্টারনেটে যত ডেটা আছে তার ৯০ ভাগ উৎপন্ন হয়েছে গত তিন বছরে। এ ডেটা উৎপন্নের হার দিনকে দিন বেড়েই চলছে। বিশেষজ্ঞদের ধারনা ২০২০ সাল নাগাদ প্রায় ৪০ জেটাবাইট ডেটা জেনারেট হবে। যা ২০১১ তুলনায় প্রায় ৫০ গুন বেশি। বিশাল পরিমাণ এই ডেটা প্রক্রিয়াজাতের মাধ্যমে বিজ্ঞান, গবেষণা, চিকিৎসা, শিক্ষা ও ব্যবসায় ব্যপক ভুমিকা রাখা যেতে পারে। তাই বলা হচ্ছে “ বিগ ডেটা ইজ বিগ ইমপ্যাক্ট।” Data Mining,big data,data analysis,data mining tutorial,book , Bangla tutorials,data mining software,Data Mining,What is data mining, bookbd, data analysis,data mining tutorial,data science,big data,business tutorial,data mining Bangla tutorial,how to,how to mine data,knowledge discovery,Artificial Intelligence,Deep learning,machine learning,Python tutorials,
Views: 4924 BookBd
HOW TO ANALYZE PEOPLE ON SIGHT - FULL AudioBook - Human Analysis, Psychology, Body Language
 
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How To Analyze People On Sight | GreatestAudioBooks 🌟SPECIAL OFFERS: ► Free 30 day Audible Trial & Get 2 Free Audiobooks: https://amzn.to/2Iu08SE ...OR: 🌟 try Audiobooks.com 🎧for FREE! : http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8 ► Shop for books & gifts: https://www.amazon.com/shop/GreatestAudioBooks How To Analyze People On Sight | GreatestAudioBooks by Elsie Lincoln Benedict & Ralph Pain Benedict - Human Analysis, Psychology, Body Language - In this popular American book from the 1920s, "self-help" author Elsie Lincoln Benedict makes pseudo-scientific claims of Human Analysis, proposing that all humans fit into specific five sub-types. Supposedly based on evolutionary theory, it is claimed that distinctive traits can be foretold through analysis of outward appearance. While not considered to be a serious work by the scientific community, "How To Analyze People On Sight" makes for an entertaining read. . ► Follow Us On TWITTER: https://www.twitter.com/GAudioBooks ► Friend Us On FACEBOOK: http://www.Facebook.com/GreatestAudioBooks ► For FREE SPECIAL AUDIOBOOK OFFERS & MORE: http://www.GreatestAudioBooks.com ► SUBSCRIBE to Greatest Audio Books: http://www.youtube.com/GreatestAudioBooks ► BUY T-SHIRTS & MORE: http://bit.ly/1akteBP ► Visit our WEBSITE: http://www.GreatestAudioBooks.com READ along by clicking (CC) for Caption Transcript LISTEN to the entire book for free! Chapter and Chapter & START TIMES: 01 - Front matter -- - 00:00 02 - Human Analysis - 04:24 03 - Chapter 1, part 1 The Alimentive Type - 46:00 04 - Chapter 1, part 2 The Alimentive Type - 1:08:20 05 - Chapter 2, part 1 The Thoracic Type - 1:38:44 06 - Chapter 2, part 2 The Thoracic Type - 2:10:52 07 - Chapter 3, part 1 The Muscular type - 2:39:24 08 - Chapter 3, part 2 The Muscular type - 3:00:01 09 - Chapter 4, part 1 The Osseous Type - 3:22:01 10 - Chapter 4, part 2 The Osseous Type - 3:43:50 11 - Chapter 5, part 1 The Cerebral Type - 4:06:11 12 - Chapter 5, part 2 The Cerebral Type - 4:27:09 13 - Chapter 6, part 1 Types That Should and Should Not Marry Each Other - 4:53:15 14 - Chapter 6, part 2 Types That Should and Should Not Marry Each Other - 5:17:29 15 - Chapter 7, part 1 Vocations For Each Type - 5:48:43 16 - Chapter 7, part 2 Vocations For Each Type - 6:15:29 #audiobook #audiobooks #freeaudiobooks #greatestaudiobooks #book #books #free #top #best #psychology # This video: Copyright 2012. Greatest Audio Books. All Rights Reserved. Audio content is a Librivox recording. All Librivox recordings are in the public domain. For more information or to volunteer visit librivox.org. Disclaimer: As an Amazon Associate we earn from qualifying purchases. Your purchases through Amazon affiliate links generate revenue for this channel. Thank you for your support.
Views: 2045275 Greatest AudioBooks
Recurrent Neural Networks (RNN / LSTM )with Keras - Python
 
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In this tutorial, we learn about Recurrent Neural Networks (LSTM and RNN). Recurrent neural Networks or RNNs have been very successful and popular in time series data predictions. There are several applications of RNN. It can be used for stock market predictions , weather predictions , word suggestions etc. SimpleRNN , LSTM , GRU are some classes in keras which can be used to implement these RNNs. The backend can be Theano as well as TensorFlow. Find the codes here GitHub : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Good Reads : http://karpathy.github.io/ Recommended book for Deep Learning : http://amzn.to/2nXweQS
Views: 55063 The SemiColon
Social Media Analytics Book Trailer feat. Marshall Sponder
 
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Where's the Social Media ROI? Marshall Sponder shows you where it is in his new book, Social Media Analytics, featuring tools and best practices for measuring comprehensive data from social media—and aligning results with overall business strategy. The book shows how to track social media in organizations of various sizes and types (B2B, B2C, C2C, corporate, nonprofit, government, international) and answers the questions every social-media marketer is asking: How many customers did we get? How many products did we sell? What were our sales margins? Readers learn how to set up social-media audits, scorecards, and data asset valuation (finding out what data they have in their organization, where it is, what's in it, and what's missing), as well as align the information with business strategy.
Views: 308 McGrawHillPro
Link Tracking with Google Analytics
 
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How do you track Link Clicks with Google Analytics? There are two techniques that we are going to discover today in this video: Outbound Link Tracking with UTM Parameters Inbound LInk Tracking with Google Analytics Event Tracking Depending on what kind of Link you want to track theses are the methods that will let you accomplish Link Tracking. #Tracking #GoogleAnalytics #UTM 🔗 Links mentioned in the video: UTM Tool: https://measureschool.com/utmtool Manual Event Tracking: https://developers.google.com/analytics/devguides/collection/analyticsjs/events 🎓 Learn more from Measureschool: http://measureschool.com/products GTM Copy Paste https://chrome.google.com/webstore/detail/gtm-copy-paste/mhhidgiahbopjapanmbflpkcecpciffa 🚀Looking to kick-start your data journey? Hire us: https://measureschool.com/services/ 📚 Recommended Measure Books: https://kit.com/Measureschool/recommended-measure-books 📷 Gear we used to produce this video: https://kit.com/Measureschool/measureschool-youtube-gear 👍 FOLLOW US Facebook: http://www.facebook.com/measureschool Twitter: http://www.twitter.com/measureschool
Views: 34321 Measureschool
Jiawei Han
 
01:19:26
Views: 1621 SonicNU
SQL: Should beginners learn SQL (2018)?
 
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SQL is the best language for beginners to start with if they want to find a job in analytics. Beginners should start there for three main reasons; it is in demand, it is powerful, and it is rather easy. There are scores of free resources that beginners can use to learn SQL. There are interactive tutorials as well as .pdfs of text books. Whatever works best for you, it is available for free. There are paid resources available as well, but why pay when you can get it for free? I wrote an article on SQL where I outline how it works and include some learning resources that SQL beginners can use. The online SQL course linked in this article is the actual course I used to get started. https://www.theanalyticsdude.com/learn-sql/ I hope you enjoy the video and the article and join The Analytics Dude as we walk through the skills needed to get a job in analytics, the techniques that separate good analysts from ordinary ones, and how to present recommendations so that executives take action.
Views: 87 Eric Hulbert
DATA MINING: Predicting Tipping Points
 
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Tipping Points as evidenced in global events are, in many ways, influenced by media. DATA MINING for predicting and analyzing world events. This just released, ground-breaking book: DATA MINING: PREDICTING TIPPING POINTS by Dr. Philip Gordon, PhD, details three case studies which were selected on the basis of common Tipping Point Attributes: Each involved media contagiousness and stickiness during their development and, each arrived at a "dramatic moment in time", which could only be characterized by the phenomenon of Tipping Points. Three recent case studies explore the leading edge technologies of DATA MINING and the theory of TIPPING POINTS: The first case study, the 2008 Presidential Campaign of Barack Obama was chosen to examine a narrower scope and timeframe for the application of the analysis. In contrast to the second case study, the International Financial Crisis of 2007--2010, which involves a broader data study period to identify trends and more complex issues. The third study, Climate Change was included as consideration because the data mining research and analysis revealed critical relationships between Media Impact and Global Events. As the issue of Climate Change is still evolving, Dr. Gordon provides a Data Mining and Tipping Point Theory methodology for analyzing and predicting our planets' most pressing Global Tipping Points. Review Comments: "The genius of the formulation of DATA MINING: PREDICTING TIPPING POINTS is that it takes explicit account of the role of social media and the internet at facilitating bifurcations and promoting dynamical instability. In effect, we have trimmed a few feet of tail off the kite. As a reader, I was informed and educated as to the factors which conspire to influence stability / instability in complex social systems. ...the book does a good job of making sense of past bifurcations and dynamical instabilities, namely political instability, our perception of global climate change, and international economic crises...my compliments on a truly insightful Media Tipping Points." -Prof. Dr. (med.) Peter S. Geissler, A.B., B.S., M.S., M.Phil., Ph.D. (Yale) M.A., M.Eng., M.S., Ph.D., M.S., M.D., M.Phil.(Cantab) "A truly fascinating book that (teaches) a whole new way of thinking about major events and how the media can influence them. - Being a political junkie I was heavily into the media coverage of the 2008 Obama election and the global financial meltdown both via TV and the blogosphere. I now find myself looking for the tipping points and stickiness factors as other key events unfold. Usually, I have trouble reading theoretical books but this one was an easy read and if you want supporting data then the references are there. This could become a solid reference for those in the media who truly want to understand what they are reporting. Highly recommended and I look forward to Dr. Gordon's ongoing analysis of (future) events." -Dr. Ralph Moorhouse, Ph.D. Political junkie, Expert: natural polymers for industries "The application of Data Mining and Tipping Point Theory to media and global events, particularly the financial crisis and climate change, is a fascinating one." -Dr. Serge Besanger, PhD Expert, International Monetary Fund "...very interesting application (of the Tipping Point Theory)...potential opportunity for predicting other global events, i.e.: Egyptian crisis and perhaps, even terrorism activities." -Dr. Adam AJLANI, PhD Professor, Sciences Politic and Political Consultant, France TV1
Views: 82 BlueMatrixCatalog
Computer Science Curriculum
 
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Are you too busy to dedicate 4 years of your life to a traditional Computer Science Major? I've created a 5 month accelerated Computer Science curriculum to help you get a broad overview of the field, covering the most important topics in sequential order using the free resources of the Internet. I've listed learning tips, Computer Scientists to follow, and a path in this video. I hope you find it useful, this is the kind of learning path I'd design for myself but I'm open sourcing it. Enjoy! Curriculum for this video: https://github.com/llSourcell/Learn_Computer_Science_in_5_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval People to follow on Twitter: Jeff Dean Paul Allen Tim Berners-Lee Linus Torvalds Brendan Eich John Carmack Curriculum: Week 1-2 (Learn Python) - https://automatetheboringstuff.com/ - https://www.codecademy.com/learn/learn-python Week 3-4 (Data Structures) - https://www.edx.org/course/data-structures-fundamentals-uc-san-diegox-algs201x Week 5-6 (Algorithms) - https://courses.csail.mit.edu/6.006/fall11/notes.shtml Week 7 (Databases) - https://www.coursera.org/learn/python-databases Week 8 (Networking) - https://www.coursera.org/learn/computer-networking Week 9-10 (Web Development) - https://www.youtube.com/watch?v=1u2qu-EmIRc&list=PLhQjrBD2T382hIW-IsOVuXP1uMzEvmcE5 - https://github.com/melanierichards/just-build-websites Week 11-12 (Mobile Development) - https://developer.apple.com/library/content/referencelibrary/GettingStarted/DevelopiOSAppsSwift/ - https://developer.android.com/training/basics/firstapp/index.html Week 13-14 (Data Science) - https://www.edx.org/course/python-for-data-science Week 15-16 (Computer Vision) - https://www.coursera.org/learn/python-text-mining Week 17-18 (Natural Language Processing) - https://www.udacity.com/course/introduction-to-computer-vision--ud810 Week 19 (Software Engineering Practices) - https://www.coursera.org/learn/software-processes Week 20 (Blockchain) - https://www.youtube.com/watch?v=cjbHqvr4ffo&list=PL2-dafEMk2A7jW7CYUJsBu58JH27bqaNL Sign up for the next course at The School of AI: http://www.theschool.ai Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 74935 Siraj Raval
Proactive Learning and Structural Transfer Learning: Building Blocks of Cognitive Systems
 
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Dr. Jaime Carbonell is an expert in machine learning, scalable data mining (“big data”), text mining, machine translation, and computational proteomics. He invented Proactive Machine Learning, including its underlying decision-theoretic framework, and new Transfer Learning methods. He is also known for the Maximal Marginal Relevance principle in information retrieval. Dr. Carbonell has published some 350 papers and books and supervised 65 Ph.D. dissertations. He has served on multiple governmental advisory committees, including the Human Genome Committee of the National Institutes of Health, and is Director of the Language Technologies Institute. At CMU, Dr. Carbonell has designed degree programs and courses in language technologies, machine learning, data sciences, and electronic commerce. He received his Ph.D. from Yale University. For more, read the white paper, "Computing, cognition, and the future of knowing" https://ibm.biz/BdHErb
Views: 1724 IBM Research
How to build Interactive Excel Dashboards
 
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Download file used in the video with step by step instructions and links to more tutorials: https://www.myonlinetraininghub.com/workbook-downloads In this video you will learn how to create an interactive dashboard from scratch using the built in Excel tools. No add-ins or VBA/Macros. Just plain Excel. Applies to Excel 2007 onward for Windows & Excel 2016 onward for Mac. Subscribe to my free newsletter and get my 100 Tips & Tricks eBook here: https://www.myonlinetraininghub.com/sign-up-for-100-excel-tips-and-tricks
Views: 1442369 MyOnlineTrainingHub
Best R Programming Tutorials
 
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What are some of the best R programming tutorials? Coursera has a free class on R programming and data analytics in general. Free is always good. You can try reading the information on John D. Cook’s blog. It isn’t a tutorial per se, but he has a lot of tips, tricks and simple advice. Simple advice is what I expect with Python and PHP, not a data analysis software and programming language. Try Inside-R.org article database for examples of how to use the language for different applications. And hopefully more than differential equations. They also have information on data analysis tools that use R, kind of like learning tools that use Mathcad as well as C++. I’d give C++ an F on usability. You could try the Revolution Analytics site; they have a good introduction to the language and the tools using it. They make tools that use R. At least they have a lot of content that is not interspersed with buy our books. It is more buy our software. The Inside-R.org site by Revolution Analytics has a calendar on R user groups, and you might find an in-person meet up or lecture from there. I’d like a more reliable resource than that. The ultimate source is the true source, the R-Project.org site. Reading a couple hundred pages on the programming language might teach me the syntax or grammar or other rules, but it does not help me utilize it. I’m sorry, there is no code combat or code kata equivalent for R. What does exist? DevCheatSheet.com has R cheat sheets for data mining, the standard commands, R with Matlab and R for regression analysis. I’d consider that pretty progressive. StatMethods.net has a number of quick lessons on statistical methods, doing time series, generating lattice graphs, finding correlations and somewhat simplified explanations of R data structures. As if anything in data analytics could be simple. StatMethods.net’s Quick-R section even has samples of code showing how to create your own value labels, something that is otherwise hard to do. What else can I do to make this less complicated? Try the RStudio.com webinars, videos and tutorials. They are one of the few sites I’ve seen with free R videos that are not half way through a solution with the final answer being hire me or buy my book. You have to admit, that type of pitch does tend to equal dollar signs.
Views: 513 Techy Help
OCR Basics: "Reading and Sorting Mail Automatically" 1970 US Postal Service
 
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USPS, Mail, Post Office, Stamps... playlist: https://www.youtube.com/playlist?list=PL_hX5wLdhf_IE6wB_qlBB_sLmUryf-4yJ more at http://quickfound.net "This 1970 film demonstrates the U.S. Postal Service's Optical Character Recognition (OCR) machines, which allow mail to be sorted automatically." Public domain film from the US National Archives, slightly cropped to remove uneven edges, with the aspect ratio corrected, and one-pass brightness-contrast-color correction & mild video noise reduction applied. The soundtrack was also processed with volume normalization, noise reduction, clipping reduction, and/or equalization (the resulting sound, though not perfect, is far less noisy than the original). http://creativecommons.org/licenses/by-sa/3.0/ https://en.wikipedia.org/wiki/Optical_character_recognition Optical character recognition (optical character reader) (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text. It is widely used as a form of data entry from printed paper data records, whether passport documents, invoices, bank statements, computerised receipts, business cards, mail, printouts of static-data, or any suitable documentation. It is a common method of digitising printed texts so that it can be electronically edited, searched, stored more compactly, displayed on-line, and used in machine processes such as machine translation, text-to-speech, key data and text mining. OCR is a field of research in pattern recognition, artificial intelligence and computer vision. Early versions needed to be trained with images of each character, and worked on one font at a time. Advanced systems capable of producing a high degree of recognition accuracy for most fonts are now common. Some systems are capable of reproducing formatted output that closely approximates the original page including images, columns, and other non-textual components... History Early optical character recognition may be traced to technologies involving telegraphy and creating reading devices for the blind. In 1914, Emanuel Goldberg developed a machine that read characters and converted them into standard telegraph code.[citation needed] Concurrently, Edmund Fournier d'Albe developed the Optophone, a handheld scanner that when moved across a printed page, produced tones that corresponded to specific letters or characters. In the late 1920s and into the 1930s Emanuel Goldberg developed what he called a "Statistical Machine" for searching microfilm archives using an optical code recognition system. In 1931 he was granted USA Patent number 1,838,389 for the invention. The patent was acquired by IBM. Blind and visually impaired users In 1974, Ray Kurzweil started the company Kurzweil Computer Products, Inc. and continued development of omni-font OCR, which could recognize text printed in virtually any font (Kurzweil is often credited with inventing omni-font OCR, but it was in use by companies, including CompuScan, in the late 1960s and 1970s). Kurzweil decided that the best application of this technology would be to create a reading machine for the blind, which would allow blind people to have a computer read text to them out loud. This device required the invention of two enabling technologies – the CCD flatbed scanner and the text-to-speech synthesiser. On January 13, 1976, the successful finished product was unveiled during a widely reported news conference headed by Kurzweil and the leaders of the National Federation of the Blind.[citation needed] In 1978, Kurzweil Computer Products began selling a commercial version of the optical character recognition computer program. LexisNexis was one of the first customers, and bought the program to upload legal paper and news documents onto its nascent online databases. Two years later, Kurzweil sold his company to Xerox, which had an interest in further commercialising paper-to-computer text conversion. Xerox eventually spun it off as Scansoft, which merged with Nuance Communications.[citation needed] The research group headed by A. G. Ramakrishnan at the Medical intelligence and language engineering lab, Indian Institute of Science, has developed PrintToBraille tool, an open source GUI frontend[4] that can be used by any OCR to convert scanned images of printed books to Braille books. In the 2000s, OCR was made available online as a service (WebOCR), in a cloud computing environment, and in mobile applications like real-time translation of foreign-language signs on a smartphone...
Views: 8740 Jeff Quitney
Visual Analytics for Narrative Text
 
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The study of novels and the analysis of their plot, characters and other entities are time-consuming and complex tasks in literary science. The digitization of literature and the proliferation of electronic books provide new opportunities to support these tasks with visual abstractions. Methods from the fields of computational linguistics can be used to automatically extract entities and their relations from digitized novels, which can then be visualized to ease exploration and analysis tasks. This demo video shows a web-based system developed at the University of Stuttgart that combines automatic analysis methods and effective visualization techniques. Different views on the extracted entities are provided and relations between them across the plot are indicated. All views enable literary scholars to jump to the corresponding passage in the novel and work directly with the text. Therefore, the approach facilitates distant reading and may provide a good starting point for new ideas, hypotheses and further analyses. More information at: http://textvis.visualdataweb.org
Views: 208 visualdataweb
6 Best And Free OCR Software 2015 List For Windows 10/7/8/XP/Vista
 
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6 Best, top And Free OCR Software 2015 List For Windows 10/7/8/XP/Vista http://www.softsuggester.com/best-and-free-ocr-software/ Today's video is all about 6 Best And Free Optical character recognition (OCR) software to extract text from images. http://www.softsuggester.com/best-and-free-ocr-software/ What is Optical character recognition (OCR) software? and what does ocr software do? Optical character recognition (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text. It is widely used as a form of data entry from printed paper data records, whether passport documents, invoices, bank statements, computerized receipts, business cards, mail, printouts of static-data, or any suitable documentation. It is a common method of digitizing printed texts so that it can be electronically edited, searched, stored more compactly, displayed on-line, and used in machine processes such as machine translation, text-to-speech, key data and text mining. OCR is a field of research in pattern recognition, artificial intelligence and computer vision. You can learn more about free ocr software at WikiPedia-https://en.wikipedia.org/wiki/Optical_character_recognition this list of 6 Best And Free OCR Software 2015 List For Windows 10/7/8/XP/Vista contains 1)-Microsoft OneNote 2)-Gimagereader 3)-FreeOCR 4)-Boxoft Free OCR 5)-Google Docs 6)-OnlineOCR.Net 6 Best And Free OCR Software 2015 List For Windows 10/7/8/XP/Vista https://youtu.be/8ZVdXrfiy3w
Views: 41535 Simple Tutorials
Google Tag Manager Button Click Tracking (2018 version) for Google Analytics, Facebook and AdWords
 
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Tracking Button Clicks used to take serious technical chops to pull off. If you have Google Tag Manager installed you simply need to follow a few steps and will be able to send Events to Google Analytics, Facebook and AdWords. In this video you are going to learn the 4 steps you need to follow to setup your Events correctly with Google Tag Manager The Steps are: 1. Setup a generic Click Trigger 2. Perform the Click to see what GTM picks up 3. Inspect the variables and refine your Trigger 4. Connect your Trigger to a Tag (such as Google Analytics, Facebook, AdWords and more….) #ButtonClickTracking #GoogleAnalytics #GoogleTagManager 🔗 Links from the video: GTM Event-Tracking Playlist: https://www.youtube.com/watch?v=b48PbFCNyOM&list=PLgr_8Hk8l4ZHqk0w9OU2IypiZsH2qqdoS&index=1&t=0s GTM for Beginners series: https://www.youtube.com/watch?v=WCmdRivjvRk&list=PLgr_8Hk8l4ZEY-rBGG99Y9V10Dc7g7cHt 🎓 Learn more from Measureschool: http://measureschool.com/products GTM Copy Paste https://chrome.google.com/webstore/detail/gtm-copy-paste/mhhidgiahbopjapanmbflpkcecpciffa 🚀Looking to kick-start your data journey? Hire us: https://measureschool.com/services/ 📚 Recommended Measure Books: https://kit.com/Measureschool/recommended-measure-books 📷 Gear we used to produce this video: https://kit.com/Measureschool/measureschool-youtube-gear 👍 FOLLOW US Facebook: http://www.facebook.com/measureschool Twitter: http://www.twitter.com/measureschool
Views: 57363 Measureschool
Python 3.5 Tutorial - 8. Dictionaries & How They Work
 
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► SPONSORS ◄ DevMountain Coding Bootcamp https://goo.gl/P4vgKS Pluralsight - FREE TRIAL! http://www.pluralsight.pxf.io/c/1302163/448522/7490 .Tech domains https://goo.gl/6EnZmg Use Coupon Code - HISPERT18 - at checkout Get a domain: $4.99 for 1 year | $24.99 for 5 years ► TOP 5 PYTHON PROGRAMMING BOOKS OF 2018 ◄ Python Programming: For the Beginners (An Introduction to the Python Computer Language and Computer Programming) https://amzn.to/2Iou1VS Deep Learning with Python https://amzn.to/2QcDL8I Cracking Codes with Python: An Introduction to Building and Breaking Ciphers https://amzn.to/2QdZ6hN Machine Learning for Beginners: Your Ultimate Guide To Machine Learning For Absolute Beginners, Neural Networks, Scikit-Learn, Deep Learning, TensorFlow, Data Analytics, Python, Data Science https://amzn.to/2R7Gi5p Python 3 Guide: A Beginner Crash Course Guide to Learn Python 3 in 1 Week https://amzn.to/2Qgk2Fc ► DONATIONS ◄ Patreon https://www.patreon.com/chrishawkes PayPal https://bit.ly/2R64WD7 https://www.patreon.com/chrishawkes Description: New to Python? https://www.youtube.com/watch?v=IZj8hLrkABs&list=PLei96ZX_m9sWS2gm1zGqGq6ZzU9T5QSg7 New to Django? https://www.youtube.com/watch?v=CfbDxoRFByY&list=PLei96ZX_m9sWowRU2mn0ccUNIBTTclcWO JavaScript (ES6) https://www.youtube.com/watch?v=jqtNVgecVvo&list=PLei96ZX_m9sX6RUTyhGkCSzfKXpExcVSm Flask https://www.youtube.com/watch?v=gDSLrpxR3G4&list=PLei96ZX_m9sWQco3fwtSMqyGL-JDQo28l React/Redux https://www.youtube.com/watch?v=53SNhzt7AqA&list=PLei96ZX_m9sUDK-1b8fNXZgBnnb6wA7sB NodeJS https://www.youtube.com/edit?o=U&video_id=SJl5THmcQik C# + React https://www.youtube.com/edit?o=U&video_id=bnFgGYooDCM Recommended Books: The Self-Taught Programmer https://amzn.to/2k3RTmx Disrupted: My Misadventure in the Start-Up Bubble https://amzn.to/2k9qijH Python Programming: An Introduction to Computer Science https://amzn.to/2k3gZSm Links: My Python For Beginners Course https://www.udemy.com/python-for-beginners-w/learn/v4/overview EBAY BLACK FRIDAY TECH DEALS https://ebay.to/2PjqeQs MAKE MONEY TAKING SURVEYS! http://trkur.com/322490/8484 BEST PROGRAMMING BOOK TO GET THROUGH THE INTERVIEW https://amzn.to/2AZfs9z
Views: 10117 Chris Hawkes

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