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Text Analytics With R | How to Connect Facebook with R | Analyzing Facebook in R
 
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In this text analytics with R tutorial, I have talked about how you can connect Facebook with R and then analyze the data related to your facebook account in R or analyze facebook page data in R. Facebook has millions of pages and getting emotions and text from these pages in R can help you understand the mood of people as a marketer. Text analytics with R,how to connect facebook with R,analyzing facebook in R,analyzing facebook with R,facebook text analytics in R,R facebook,facebook data in R,how to connect R with Facebook pages,facebook pages in R,facebook analytics in R,creating facebook dataset in R,process to connect facebook with R,facebook text mining in R,R connection with facebook,r tutorial for facebook connection,r tutorial for beginners,learn R online,R beginner tutorials,Rprg
Java For Text Mining and NLP with Stanford NLP
 
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This technical book aim to equip the reader with Java programming, Text Mining, and Natural Language Processing fundamentals in a fast and practical way. There will be many examples and explanations that are straight to the point. You will develop your own Text Mining Application at the end of the book. Contents 1. Introduction 2. Getting Started (Installing IDE, ...) 3. Language Essentials I (variables, data types, ...) 4. Language Essentials II (loops, if... else..., methods) 5. Object Essentials (classes, inheritance, polymorphism, encapsulation, ...) 6. Text Mining Essentials (Import Text Files, Text Transformation (lowercase, stopwords), Text Understanding (Stanford NLP), Text Classification (Stanford Classifier) ) 7. Conclusion Book: http://www.svbook.com Course: TBA
Views: 61 SVBook
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 280163 Siraj Raval
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 164934 Siraj Raval
How to Extract TEXT From IMAGE/SCANNED DOC !! EASY !! NO SOFTWARES REQUIRED!!
 
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Hey all. NO SOFTWARE'S ARE need to EXTRACT TEXT FROM THE IMAGE or a SCANNED DOCUMENT !! ITS REALLY EASY !! AND YOU CAN DO IT USING UR PC/LAPTOP TOO !! Have a look at it ! :) Please share it ^_^ I hope this helps MANY ! :) Do You Have any questions ?? please comment i will make solution through videos on that Problem ! :) I hope this video helps :) , don't forget to checkout my other videos in this playlist, they are really worth watching. Like,comment,rate video :) Any suggestions are always welcome! Thank you very much for watching have a great day ! http://www.youtube.com/playlist?list=PLB3i9IKhwBX9WIPAr0Jw75cb_VxGF8ftf Support my page :) : www.fb.com/Dosomethingbesomeone I have created this page to spread smile on others face :) I hope you like it !
Views: 349960 sampath ramkumar
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 68178 deltaDNA
Word2Vec (tutorial)
 
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In this video, we'll use a Game of Thrones dataset to create word vectors. Then we'll map these word vectors out on a graph and use them to tell us related words that we input. We'll learn how to process a dataset from scratch, go over the word vectorization process, and visualization techniques all in one session. Code for this video: https://github.com/llSourcell/word_vectors_game_of_thrones-LIVE Join us in our Slack channel: http://wizards.herokuapp.com/ More learning resources: https://www.tensorflow.org/tutorials/word2vec/ https://radimrehurek.com/gensim/models/word2vec.html https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words http://sebastianruder.com/word-embeddings-1/ http://natureofcode.com/book/chapter-1-vectors/ Please subscribe. And like. And Comment. That's what keeps me going. 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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 72717 Siraj Raval
Text mining highlight talk: ISMB 2014
 
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ISMB Highlight talk by Ashutosh Malhotra:Linking hypothetical patterns to disease molecular signatures in Alzheimer's disease
Views: 596 atmbio
dsatlconf18 - Applications of Text Analytics by Rajkumar Bondugula, Ph.D.
 
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Rajkumar Bondugula | Ph.D., Principal Data Scientist and Senior Director, Equifax Dr. Bondugula has earned M.S. and Ph.D. from University of Missouri-Columbia, both in Computer Science with a specialization in Machine Learning. He has co-authored twenty peer-reviewed scientific publications, a book titled “Application of Fuzzy Logic In Bioinformatics”, and his work has been cited more than 250 times. Over the last decade, he has professionally used machine learning for computational biology, improve e-commerce customer experience, gather web and social intelligence, and provide product recommendations. In addition, he is also an expert in natural language processing and distributed computing. He is currently a Principal Data Scientist and he is responsible for: • Leading a team of Data Scientists and Big Data Engineers to develop innovative solutions to hard problems that lead to organizational growth in 3-5 years. • Enabling insight generation from data by architecting and leading projects that standardize structured data and structure unstructured data. • Providing thought leadership to the organization for making investments in next generation big data analytical technologies, tools and processes. Before Equifax, he did a brief stint at a startup called Shoutlet, established data science practice at Home Depot, lead a machine learning team at Sears and was a scientist at Department of Defense.
Literature Recommendations through Text Mining
 
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This is a video I made for my CS599 class. I hope you are entertained and informed. You can also see the related code here http://code.google.com/p/book-parser-recommender/
Views: 105 Taylor Kulp
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: 472732 sentdex
DATA MINING   3 Text Mining and Analytics   4 8 Text Categorization Methods
 
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https://www.coursera.org/learn/text-mining
Views: 40 Ryo Eng
Machine Learning with Text in scikit-learn (PyCon 2016)
 
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Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn. (Presented at PyCon on May 28, 2016.) GitHub repository: https://github.com/justmarkham/pycon-2016-tutorial Enroll in my online course: http://www.dataschool.io/learn/ == OTHER RESOURCES == My scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A My pandas video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y == LET'S CONNECT! == Newsletter: https://www.dataschool.io/subscribe/ Twitter: https://twitter.com/justmarkham Facebook: https://www.facebook.com/DataScienceSchool/ LinkedIn: https://www.linkedin.com/in/justmarkham/ YouTube: https://www.youtube.com/user/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool
Views: 89664 Data School
EmoText for opinion mining in long texts
 
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http://socioware.de https://www.researchgate.net/publication/278383087_Opinion_Mining_and_Lexical_Affect_Sensing EmoText for opinion mining in long texts illustrates a domain-independent approach to opinion mining. A thorough description is available in the book "Opinion mining and lexical affect sensing". Empirically revealed that texts should contain not less than 200 words for reliable classification. The engine evaluates features (lexical, stylometric, grammatical, deictic) using different evaluation methods and uses the SMO or NaiveBayes classifiers from the WEKA data mining toolkit for text classification. Statistical EmoText formed a basis for the statistical framework for experimentation and rapid prototyping. The approach was tested on the following English corpora: a Pang corpus with weblogs, Berardinelli movie review corpus with movie reviews, a corpus with spontaneous dialogues (the SAL corpus), and a corpus with product reviews.
Views: 974 Alexander Osherenko
Using R to Access Medical Literature  and Research from PUBMED
 
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PubMed is a great source of medical literature. If you are working on a Natural Language Processing (NLP) project and need 100's or 1000's of topic-based medical text, the RISmed package can simplify and automate that process. Full walkthrough: http://amunategui.github.io/pubmed-query/ MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 3645 Manuel Amunategui
Text Classification Using Naive Bayes
 
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This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 101407 Francisco Iacobelli
HOW TO ANALYZE PEOPLE ON SIGHT - FULL AudioBook - Human Analysis, Psychology, Body Language
 
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How To Analyze People On Sight | GreatestAudioBooks 🎅 Give the gift of audiobooks! 🎄 Click here: http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8&a_bid=ec49a209 🌟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: 2114406 Greatest AudioBooks
PDF Data Extraction and Automation 3.1
 
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Learn how to read and extract PDF data. Whether in native text format or scanned images, UiPath allows you to navigate, identify and use PDF data however you need. Read PDF. Read PDF with OCR.
Views: 142262 UiPath
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: 8885 Machine Learning TV
Word2vec with Gensim - Python
 
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This video explains word2vec concepts and also helps implement it in gensim library of python. Word2vec extracts features from text and assigns vector notations for each word. The word relations are preserved using this. A famous result of word2vec is King - Man + Woman = Queen . This concept has lots other applications as well. Gensim is a library in python which is used to create word2vec models for your corpus. We Learn CBOW- Continuous bowl of words and Skip Gram models to get an intuition about word2vec. Download pretrained word2vec models : https://github.com/jhlau/doc2vec Dataset : https://www.kaggle.com/jiriroz/qa-jokes Find the code GitHub: https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Recommended book for Deep Learning : http://amzn.to/2nXweQS
Views: 74599 The Semicolon
Part 1 - Using Excel for Open-ended Question Data Analysis
 
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Completing data analysis on open-ended questions using Excel. For analyzing multiple responses to an open-ended question see Part 2: https://youtu.be/J_whxIVjNiY Note: Selecting "HD" in the video settings (click on the "gear" icon) makes it easier to view the data entries
Views: 171571 Jacqueline C
Introduction - Learn Python for Data Science #1
 
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Welcome to the 1st Episode of Learn Python for Data Science! This series will teach you Python and Data Science at the same time! In this video we install Python and our text editor (Sublime Text), then build a gender classifier using the sci-kit learn library in just about 10 lines of code. Please subscribe & share this video if you liked it! The code for this video is here: https://github.com/llSourcell/gender_classification_challenge I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Download Python here: https://www.python.org/downloads/ Download Sublime Text here: https://www.sublimetext.com/3 Some Great simple sci-kit learn examples here: https://github.com/chribsen/simple-machine-learning-examples and the official scikit website: http://scikit-learn.org/ Highly recommend this online book as supplementary reading material: https://learnpythonthehardway.org/book/ Wondering when to use which model? This chart helps, but keep in mind deep neural nets outperform pretty much any model given enough data and computing power. so use these when you don't have access to loads of data and compute: http://scikit-learn.org/stable/tutorial/machine_learning_map/ Thank you guys for watching! Subscribe, like, and comment! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 514785 Siraj Raval
Python Tutorial: Anaconda - Installation and Using Conda
 
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In this Python Tutorial, we will be learning how to install Anaconda by Continuum Analytics. Anaconda is a data science platform that comes with a lot of useful features right out of the box. Many people find that installing Python through Anaconda is much easier than doing so manually. Also, we will look at Conda. Conda is Continuum's package, dependency and environment manager. Let's get started. Anaconda Download Page: https://www.anaconda.com/download/ ✅ Support My Channel Through Patreon: https://www.patreon.com/coreyms ✅ Become a Channel Member: https://www.youtube.com/channel/UCCezIgC97PvUuR4_gbFUs5g/join ✅ One-Time Contribution Through PayPal: https://goo.gl/649HFY ✅ Cryptocurrency Donations: Bitcoin Wallet - 3MPH8oY2EAgbLVy7RBMinwcBntggi7qeG3 Ethereum Wallet - 0x151649418616068fB46C3598083817101d3bCD33 Litecoin Wallet - MPvEBY5fxGkmPQgocfJbxP6EmTo5UUXMot ✅ Corey's Public Amazon Wishlist http://a.co/inIyro1 ✅ Equipment I Use and Books I Recommend: https://www.amazon.com/shop/coreyschafer ▶️ You Can Find Me On: My Website - http://coreyms.com/ My Second Channel - https://www.youtube.com/c/coreymschafer Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 610837 Corey Schafer
Sentiment Analysis via Chaos theory
 
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We read emotions from text. ThenWat.com
Automate Data Extraction Without Opening Destination Workbook
 
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Our Excel training videos on YouTube cover formulas, functions and VBA. Useful for beginners as well as advanced learners. New upload every Thursday. For details you can visit our website: http://www.familycomputerclub.com/automate-data-extraction-without-opening-destination-workbook.html In today's video we learn how to automate data extraction without opening destination workbook using Excel VBA. The concept can be used to get data from multiple workbooks into a new workbook. We can also get data on to user-forms automatically. Get the book Excel 2016 Power Programming with VBA: http://amzn.to/2kDP35V If you are from India you can get this book here: http://amzn.to/2jzJGqU
Views: 172486 Dinesh Kumar Takyar
Learn Python - Full Course for Beginners
 
04:26:52
This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you'll be a python programmer in no time! ⭐️ Contents ⭐ ⌨️ (0:00) Introduction ⌨️ (1:45) Installing Python & PyCharm ⌨️ (6:40) Setup & Hello World ⌨️ (10:23) Drawing a Shape ⌨️ (15:06) Variables & Data Types ⌨️ (27:03) Working With Strings ⌨️ (38:18) Working With Numbers ⌨️ (48:26) Getting Input From Users ⌨️ (52:37) Building a Basic Calculator ⌨️ (58:27) Mad Libs Game ⌨️ (1:03:10) Lists ⌨️ (1:10:44) List Functions ⌨️ (1:18:57) Tuples ⌨️ (1:24:15) Functions ⌨️ (1:34:11) Return Statement ⌨️ (1:40:06) If Statements ⌨️ (1:54:07) If Statements & Comparisons ⌨️ (2:00:37) Building a better Calculator ⌨️ (2:07:17) Dictionaries ⌨️ (2:14:13) While Loop ⌨️ (2:20:21) Building a Guessing Game ⌨️ (2:32:44) For Loops ⌨️ (2:41:20) Exponent Function ⌨️ (2:47:13) 2D Lists & Nested Loops ⌨️ (2:52:41) Building a Translator ⌨️ (3:00:18) Comments ⌨️ (3:04:17) Try / Except ⌨️ (3:12:41) Reading Files ⌨️ (3:21:26) Writing to Files ⌨️ (3:28:13) Modules & Pip ⌨️ (3:43:56) Classes & Objects ⌨️ (3:57:37) Building a Multiple Choice Quiz ⌨️ (4:08:28) Object Functions ⌨️ (4:12:37) Inheritance ⌨️ (4:20:43) Python Interpreter Course developed by Mike Dane. Check out his YouTube channel for more great programming courses: https://www.youtube.com/channel/UCvmINlrza7JHB1zkIOuXEbw 🐦Follow Mike on Twitter - https://twitter.com/mike_dane 🔗If you liked this video, Mike accepts donations on his website: https://www.mikedane.com/contribute/ ⭐️Other full courses by Mike Dane on our channel ⭐️ 💻C: https://youtu.be/KJgsSFOSQv0 💻C++: https://youtu.be/vLnPwxZdW4Y 💻SQL: https://youtu.be/HXV3zeQKqGY 💻Ruby: https://youtu.be/t_ispmWmdjY 💻PHP: https://youtu.be/OK_JCtrrv-c 💻C#: https://youtu.be/GhQdlIFylQ8 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 6223002 freeCodeCamp.org
Rick Astley - Never Gonna Give You Up (Video)
 
03:33
Rick Astley - Never Gonna Give You Up (Official Video) - Listen On Spotify: http://smarturl.it/AstleySpotify Learn more about the brand new album ‘Beautiful Life’: https://RickAstley.lnk.to/BeautifulLifeND Buy On iTunes: http://smarturl.it/AstleyGHiTunes Amazon: http://smarturl.it/AstleyGHAmazon Follow Rick Astley Website: http://www.rickastley.co.uk/ Twitter: https://twitter.com/rickastley Facebook: https://www.facebook.com/RickAstley/ Instagram: https://www.instagram.com/officialric... #RickAstley #NeverGonnaGiveYouUp #RickAstleyofficial #RickAstleyAlbum #RickAstleyofficialvideo #RickAstleyofficialaudio #RickAstleysongs #RickAstleyNeverGonnaGiveYouUp #WRECKITRALPH2 #RALPHBREAKSTHEINTERNET Lyrics We're no strangers to love You know the rules and so do I A full commitment's what I'm thinking of You wouldn't get this from any other guy I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you We've known each other for so long Your heart's been aching, but You're too shy to say it Inside, we both know what's been going on We know the game and we're gonna play it And if you ask me how I'm feeling Don't tell me you're too blind to see Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you (Ooh, give you up) (Ooh, give you up) Never gonna give, never gonna give (Give you up) Never gonna give, never gonna give (Give you up) We've known each other for so long Your heart's been aching, but You're too shy to say it Inside, we both know what's been going on We know the game and we're gonna play it I just wanna tell you how I'm feeling Gotta make you understand Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you Never gonna give you up Never gonna let you down Never gonna run around and desert you Never gonna make you cry Never gonna say goodbye Never gonna tell a lie and hurt you"
Views: 561450972 RickAstleyVEVO
HOW TO LEARN 100+ ENGLISH WORDS A DAY. ENGLISH VOCABULARY.
 
09:04
In this video I share hacks that help memorize thousands of English words per day and grow your vocabulary. 📝 Get your English text corrected instantly - https://fluent.express/ Related videos: 50 most common English phrases - https://www.youtube.com/watch?v=bj5btO2nvt8 - Best TV shows to learn English - http://bit.ly/2zI5DN2 - How to speak English like an American - http://bit.ly/2muNpcQ 📗🇺🇸 My book about how I got full financial aid to study in the USA (my story + tips) - http://bit.ly/2ZwlkRB How to learn hundreds of English words a day 1. There are words in English that work both as a noun and verb - so by learning one work your learning two -    work, drive, try, kiss, drink, dream, milk (доить), hope, love 2. Words that end with -tion would be almost the same in your own language, super easy to learn - ambition (ambicion, Ambition, ambitzione), motivation (motivacion, Motivation, motivazione). Dissertation, profession, portion, intonation, convention, sanction, concentration, delegation, illustration 3. Look for images for particular words - you will be surprised you’ve already used them a lot in your life. Sometimes words just remind you of something - a song, a band. Spear which is a weapon - I think of Britney Spears. Beetle = a bug, Volkswagen New Beetle car, which looks like a bug, gosling - is a baby goose - and you know who I am thinking about - Ryan Gosling. 4. Some words have several meanings - try to learn at least a couple of meanings to diversify your vocabulary - fire = flame, to let someone go (employee), date = going out with a loved one, date as a fruit, date as a day in a calendarmiss = to skip something, to long to see someone, miss like a girl 5. There are a lot of words that are almost identical in a lot of languages - aquarium, antidepressant, avocado, argument, sensor, caramel, control, cocktail, scandal, organism 6. When you speak it’s very important to remember the words, which I call a safety net - these should be learnt by heart right now. Words include - I, you, she, he, eye, female, etc. - they are all mentioned in my video - 100 Most Common English Words 7. Learn expressions - are you nuts? 🥜 save face, I am all ears, to feel blue- every day words form combinations that have a completely different meaning 8. Talk to yourself when you learn a new word or expression using it. For example, if you liked the video you can comment below “Marina, I was all ears!” Which means that you listened to the video really carefully and wrote things down. 9. Be aware of false friends. Some words in English might sound like a word a your language, but mean a completely different thing. One of the common words is Actual - in English in means current, related to present time. In Spanish Actual is real.Eventuell = perhaps, eventually = in the end ⭐ INSTAGRAM - linguamarina ⭐ LEARN LANGUAGES ABROAD - https://linguatrip.com 📷 FILMING EQUIPMENT VLOGS (outdoors): - Canon G7X - https://geni.us/canonG7X VIDEOS (indoors): - Sony A7R II (also perfect for Instagram) - https://geni.us/sonyA7RII - Sony 50 mm lens - https://geni.us/Sony50mmlens SOUND: - Zoom H4n Pro (better than any built-in camera sound) - https://geni.us/ZoomH4nPro - Rode video mic (when I have to use my camera to record the sound) - https://geni.us/rodeVideoMic 🎈PROMOS $20 TO SPEND ON AIRBNB - https://abnb.me/e/B2yx6PJZER $20 TO SPEND ON UBER - http://ubr.to/2k1B89L
Views: 2046513 linguamarina
Management Information System(Quick Review) in  Hindi  हिंदी Urdu With Examples
 
04:20
System: Group of components related with each other working to gather in order to achieve a common goal is called system. Information System: An information system (IS) is any organized system for the collection, organization, storage and communication of information. More specifically, it is the study of complementary networks that people and organizations use to collect, filter, process, create and distribute data. Management Information System: A management information system (MIS) is a computerized database of information organized and programmed in such a way that it produces regular reports on operations for every level of management in a company. It is usually also possible to obtain special reports from the system easily. For further information feel free to contact: +923007660084
Views: 189043 Humayoon Chaudhry
AWS re:Invent 2018: High Performance Data Streaming with Amazon Kinesis: Best Practices (ANT322-R1)
 
01:03:07
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we dive deep into best practices for Kinesis Data Streams and Kinesis Data Firehose to get the most performance out of your data streaming applications. Comcast uses Amazon Kinesis Data Streams to build a Streaming Data Platform that centralizes data exchanges. It is foundational to the way our data analysts and data scientists derive real-time insights from the data. In the second part of this talk, Comcast zooms into how to properly scale a Kinesis stream. We first list the factors to consider to avoid scaling issues with standard Kinesis stream consumption, and then we see how the new fan-out feature changes these scaling considerations. Complete Title: AWS re:Invent 2018: [REPEAT 1] High Performance Data Streaming with Amazon Kinesis: Best Practices (ANT322-R1)
Views: 6669 Amazon Web Services
How to recognize text from image with Python OpenCv OCR ?
 
07:09
Recognize text from image using Python+ OpenCv + OCR. Do you want to Donate me to buy a CAMERA for next demo https://www.paypal.me/tramvm/5 Source code: http://blog.tramvm.com/2017/05/recognize-text-from-image-with-python.html Relative videos: 1. Recognize digital screen display https://youtu.be/mKYpd6jx3Ms 2. ORM scanner: https://youtu.be/t66OAXI9mkw 3. Recognize answer sheet with mobile phone: https://youtu.be/82FlPaQ92OU 4. Recognize marked grid with USB camera: https://youtu.be/62P0c8YqVDk 5. Recognize answers sheet with mobile phone: https://youtu.be/xVLC4WdXvhE
Views: 115210 Tram Vo Minh
DATA MINING: Predicting Tipping Points: By Dr. Philip Gordon, PhD
 
01:51
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: 275 BlueMatrixCatalog
▶ Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining ?
 
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Data Mining becomes a very hot topic in this moments because of its various uses. We can apply data mining to predict about an event that might happen. ✔Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining? We're gonna learn some real-life scenario of Data Mining in this video. »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 ট্র্যাডিশনাল পদ্ধতিতে যে সকল সমস্যার সহজে কোন সমাধান দেয়া যায় না #ডেটা_মাইনিং ব্যবহারে সহজেই একটি সিদ্ধান্তে পৌঁছানো সম্ভব। আর সে সিদ্ধান্ত কাজে লাগিয়ে ব্যবসায়িক অথবা যে কোন সম্পর্কিত সিদ্ধান্ত গ্রহন সম্ভব। Data Mining,big data,data analysis,data mining tutorial,book bd,Bangla tutorials,data mining software,Data Mining,What is data mining,bookbd,data analysis,data mining tutorial,data science,big data, business intelligence,data mining tools,bangla tutorial,data mining bangla tutorial,how to,how to mine data, knowledge discovery, Artificial Intelligence,Deep learning,machine learning,Python tutorials, Data Mining in the Retail Industry What does the future of business look like? How data will transform business? How data mining will transform business?
Views: 9106 BookBd
Automated data scraping from websites into Excel
 
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Our Excel training videos on YouTube cover formulas, functions and VBA. Useful for beginners as well as advanced learners. New upload every Thursday. For details you can visit our website: http://www.familycomputerclub.com You can scrape, pull or get data from websites into Excel by performing a few simple steps. 1. record a macro to find out how one or many tables or data can be scraped from the website 2. Study the code carefully 3. Create an Excel sheet containing the links that get you the data from the appropriate web pages 4. Automate the process using a loop that creates a) New worksheets b) Automatically changes the link to the web pages that have the required data You can view the complete Excel VBA code here: http://www.familycomputerclub.com/scrpae-pull-data-from-websites-into-excel.html http://www.familycomputerclub.com/get-web-page-data-int-excel-using-vba.html Interesting Links: http://www.tushar-mehta.com/publish_train/xl_vba_cases/vba_web_pages_services/index.htm Get the book Excel 2016 Power Programming with VBA: http://amzn.to/2kDP35V If you are from India you can get this book here: http://amzn.to/2jzJGqU
Views: 530861 Dinesh Kumar Takyar
Build a TensorFlow Image Classifier in 5 Min
 
05:47
In this episode we're going to train our own image classifier to detect Darth Vader images. The code for this repository is here: https://github.com/llSourcell/tensorflow_image_classifier I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Challenge: The challenge for this episode is to create your own Image Classifier that would be a useful tool for scientists. Just post a clone of this repo that includes your retrained Inception Model (label it output_graph.pb). If it's too big for GitHub, just upload it to DropBox and post the link in your GitHub README. I'm going to judge all of them and the winner gets a shoutout from me in a future video, as well as a signed copy of my book 'Decentralized Applications'. This CodeLab by Google is super useful in learning this stuff: https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/?utm_campaign=chrome_series_machinelearning_063016&utm_source=gdev&utm_medium=yt-desc#0 This Tutorial by Google is also very useful: https://www.tensorflow.org/versions/r0.9/how_tos/image_retraining/index.html This is a good informational video: https://www.youtube.com/watch?v=VpDonQAKtE4 Really deep dive video on CNNs: https://www.youtube.com/watch?v=FmpDIaiMIeA I love you guys! Thanks for watching my videos and if you've found any of them useful I'd love your support on Patreon: https://www.patreon.com/user?u=3191693 Much more to come so please SUBSCRIBE, LIKE, and COMMENT! :) edit: Credit to Clarifai for the first conv net diagram in the video 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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 676189 Siraj Raval
How to prepare for an Excel 2016 assessment test for job applications
 
18:31
How to prepare for an Excel assessment test for job applications. The video will cover the knowledge needed to pass basic Excel knowledge requirements. Download the practice file from: http://unitedcomputerconsultants.com/testsfiles.html and look for "basic job assessment"
Views: 939484 United Computers
SPSS Questionnaire/Survey Data Entry - Part 1
 
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How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.
Views: 630931 Quantitative Specialists
Let's Get Rich With quantmod And R! Rich With Market Knowledge! Machine Learning with R
 
19:08
See how easy it is to download, visualize and manipulate daily stock market data and how to use it to build a complex market model. Code and walkthrough: http://amunategui.github.io/wallstreet/ Note: for those that can't use XGBoost - I added an alternative script using GBM in the walkthrough: http://amunategui.github.io/wallstreet/ Top of the page under resources look for link: "Alternative GBM Source Code - for those that can't use xgboost" MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 44392 Manuel Amunategui
Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
 
30:21
In this Python Tutorial, we will be learning how to install, setup, and use Jupyter Notebooks. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Let's get started. ✅ Support My Channel Through Patreon: https://www.patreon.com/coreyms ✅ Become a Channel Member: https://www.youtube.com/channel/UCCezIgC97PvUuR4_gbFUs5g/join ✅ One-Time Contribution Through PayPal: https://goo.gl/649HFY ✅ Cryptocurrency Donations: Bitcoin Wallet - 3MPH8oY2EAgbLVy7RBMinwcBntggi7qeG3 Ethereum Wallet - 0x151649418616068fB46C3598083817101d3bCD33 Litecoin Wallet - MPvEBY5fxGkmPQgocfJbxP6EmTo5UUXMot ✅ Corey's Public Amazon Wishlist http://a.co/inIyro1 ✅ Equipment I Use and Books I Recommend: https://www.amazon.com/shop/coreyschafer ▶️ You Can Find Me On: My Website - http://coreyms.com/ My Second Channel - https://www.youtube.com/c/coreymschafer Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 605194 Corey Schafer
Text Analytics - Cognitive Services - PowerApps
 
03:25
In this video I walk through a Cognitive Services Text Analytics PowerApp that I have uploaded to the TDG PowerPlatform Bank
Views: 34 William Dorrington
Transportation problem [ MODI method - U V method - Optimal  Solution ] :-by #kauserwise
 
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NOTE: Formula "pij = ui+vi-Cij" according to this formula the optimal values should be Zero or less than Zero which mean Zero or negative values, and in this formula if we did not reach the optimality then we should select the maximum positive value to proceed further. If you use this Cij-(u1+vj) formula then the values should be zero or positive value to reach the optimality, and in this formula if we did not reach the optimality then we should select the maximum negative value to proceed further. We can apply either any any one of the formula to find out the optimality. So both the formulas are doing same thing only but the values of sign (- +) will be differ. Here is the video about Transportation problem in Modi method-U V method using north west corner method, optimum solution in operation research, with sample problem in simple manner. Hope this will help you to get the subject knowledge at the end. Thanks and All the best. To watch more tutorials pls visit: www.youtube.com/c/kauserwise * Financial Accounts * Corporate accounts * Cost and Management accounts * Operations Research * Statistics ▓▓▓▓░░░░───CONTRIBUTION ───░░░▓▓▓▓ If you like this video and wish to support this kauserwise channel, please contribute via, * Paytm a/c : 6383617203 * Western Union / MoneyGram [ Name: Kauser, Country: India & Email: [email protected] ] [Every contribution is helpful] Thanks & All the Best!!! ───────────────────────────
Views: 2259300 Kauser Wise
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: 98740 Measureschool
CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters
 
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CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters What IS DATA ANALYTICS? Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data -- with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category. Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals -- all with the ultimate goal of boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources. Types of data analytics applications : At a high level, data analytics methodologies include exploratory data analysis (EDA), which aims to find patterns and relationships in data, and confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial -- a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis. Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive -- it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view. At the application level, BI and reporting provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers working in IT or for a centralized BI team; now, organizations increasingly use self-service BI tools that let execs, business analysts and operational workers run their own ad hoc queries and build reports themselves. Keywords: being a data analyst, big data analyst, business analyst data warehouse, data analyst, data analyst accenture, data analyst accenture philippines, data analyst and data scientist, data analyst aptitude questions, data analyst at cognizant, data analyst at google, data analyst at&t, data analyst australia, data analyst basics, data analyst behavioral interview questions, data analyst business, data analyst career, data analyst career path, data analyst career progression, data analyst case study interview, data analyst certification, data analyst course, data analyst in hindi, data analyst in india, data analyst interview, data analyst interview questions, data analyst job, data analyst resume, data analyst roles and responsibilities, data analyst salary, data analyst skills, data analyst training, data analyst tutorial, data analyst vs business analyst, data mapping business analyst, global data analyst bloomberg, market data analyst bloomberg
Views: 29127 THE MIND HEALING
Dynamic text filtering on the client - #11 React JS prototyping
 
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We're wiring up the filter component to the app. Whatever text is input will dynamically filter both the list of playlist components as well as the aggregation components listing hours and number of playlists. 🔗 The code from this episode https://github.com/mpj/better-playlists/tree/ae0c871454c1345ca726f2ef09b717d328416909 - - - In this series we'll go from start to finish in building a fully functioning high-fidelity prototype with OAuth2 authentication and API connections fetching real data. The user will experience the prototype with her own stuff in it! We do this by using React JS https://reactjs.org/ - a great JavaScript framework for building front-end components. We will even do some back-end to handle authentication eventually. And yes, this is not Travis - David and MPJ are filling in! - - - This video was sponsored by the DevTips Patron Community - https://www.patreon.com/DevTips Listen to Travis' Podcast - http://www.travandlos.com/ Get awesomeness emailed to you every thursday - http://travisneilson.com/notes You should follow DevTips on Twitter - https://twitter.com/DevTipsShow MPJ's channel on programming Fun Fun Function is great - https://www.youtube.com/funfunfunction David is known as Data David discussing analytics and data - https://www.youtube.com/channel/UC0TQC6ZPNm23dU6ecc5x0Gg
Views: 13317 DevTips
FRAppE Detecting Malicious Facebook Applications
 
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Title: FRAppE Detecting Malicious Facebook Applications Domain: Data Mining Key Features: 1. We systematically profile apps and show that malicious app profiles are significantly different than those of benign apps. A striking observation is the “laziness" of hackers; many malicious apps have the same name, as 8% of unique names of malicious apps are each used by more than 10 different apps (as defined by their app IDs). 2. We conduct a forensics investigation on the malicious app ecosystem to identify and quantify the techniques used to promote malicious apps. The most interesting result is that apps collude and collaborate at a massive scale. Apps promote other apps via posts that point to the “promoted" apps. If we describe the collusion relationship of promoting-promoted apps as a graph, we find 1,584 promoter apps that promote 3,723 other apps. 3. We were surprised to find popular good apps, such as ‘FarmVille’ and ‘Facebook for iPhone’, posting malicious posts. On further investigation, we found a lax authentication rule in Facebook that enabled hackers to make malicious posts appear as though they came from these apps. 4. We implemented a desktop application called Phish Shield, which concentrates on URL and Website Content of phishing page. Phish Shield takes URL as input and outputs the status of URL as phishing or legitimate website. The heuristics used to detect phishing are footer links with null value, zero links in body of html, copyright content, title content and website identity. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2016 – 2017 data mining projects 5. 2016 – 2017 best project center in Chennai 6. best guided ieee project center in Chennai 7.ieee projects 2017 for cse 8. ieee projects 2017 for ece 9. 2016 – 2017 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2016 – 2017 data mining weka projects 13. 2016 – 2017 b.e projects 14. 2016 – 2017 m.e projects 15. 2016 – 2017 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2016 – 2017 ieee base paper free download 23. 2016 – 2017 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2016 - 2017 48. 2016 - 2017 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Noun and its types (संज्ञा व उसके प्रकार): Abstract, Personal, Common, Collective, Material, Gerund
 
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इस वीडियो लैक्चर में आप संज्ञा व उसके सभी प्रकारों के बारे में विस्तार से जानेंगे. Noun & its Types: Personal Noun, Common Noun, Collective Noun, Material Noun, Abstract Noun नये Subscribers के लिए Basic से शुरू Lecture 1, 2, 3.... https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5eX6gADDo5ZHykXBaXYpVO Competitive English Grammar Topics https://www.youtube.com/playlist?list=PLsXdBvuJ5ox7JLN-ZOtivNwide92ToFZx Facebook: https://www.facebook.com/englishwaledotcom/ Instagram: https://www.instagram.com/spoken_english_guru_adityarana/ FREE Android App: https://play.google.com/store/apps/details?id=in.qtime.spokenenglishguru Spoken English Book & Daily Use Sentences' Book: http://bit.ly/2Oj7zUF All Video Lectures’ Lesson-wise Pen Drive - http://bit.ly/2wlxv6N Free PDF eBook: http://bit.ly/2LYwO8q FREE CHARTS: http://bit.ly/2LVtbAd FREE Practice Ex: http://bit.ly/2Mz4XRI Complete English Speaking Course Lesson-wise Videos (250+ Videos) Link: Lesson 1: English सीखने की शुरूआत https://www.youtube.com/playlist?list=PLsXdBvuJ5ox4xW_8t2mMZStWZFEdKdvAc Lesson 2: Parts of Speech https://www.youtube.com/playlist?list=PLsXdBvuJ5ox569k1T00UH7zdw0ZETatLz Lesson 3: Simple Sentences - Present, Past & Future https://www.youtube.com/playlist?list=PLsXdBvuJ5ox4xqm9T72J1D6I2IqLG4cJr Lesson 4: सभी 12 Tenses सीखो https://www.youtube.com/playlist?list=PLsXdBvuJ5ox4evkxrt2AnfXpndrYtEo5Q Lesson 5: Modal Helping Verbs in English Grammar https://www.youtube.com/playlist?list=PLsXdBvuJ5ox6FoHE30D7mAk5DylqVR81O Lesson 6: All Prepositions in English Grammar https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5sd3o3RZE9HJcZ_crRvBYG Lesson 7: All Conjunctions in English Grammar https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5cy2xkIQknyfyd9PSxR3JY Lesson 8: Daily Use English Sentences https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5WZDOosR7ihWooeFwnT8Hf Lesson 9: Vocabulary Exercises https://www.youtube.com/playlist?list=PLsXdBvuJ5ox7Ny0kgrgXfoltFX8zxMr10 Lesson 10: Daily English Speaking Practice https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5RSgM5wsAbCbTMXi9AAJFh Lesson 11: Hindi to English Translation & Tricks https://www.youtube.com/playlist?list=PLsXdBvuJ5ox4myjPpnomQnvU37GUbXE2s Lesson 12: English to Hindi Translation https://www.youtube.com/playlist?list=PLsXdBvuJ5ox6n6xk9pPe1xUc3VhAB6Ra0 Lesson 13: Active and Passive Voice https://www.youtube.com/playlist?list=PLsXdBvuJ5ox7M4w-k72XtRwP5OlZEXT_j Lesson 14: Be Being Been | Concept & Use https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5o2yrhbITHJ1T2RbuImFDn Lesson 15: Advance Grammar & Spoken Topics https://www.youtube.com/playlist?list=PLsXdBvuJ5ox53AvjielYUoRlaO_cuBDQb Lesson 16: Gerund, Infinitives and Participles https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5DdSQoWpx85VqxzMr8Rbkf Lesson 17: Phrasal Verbs in English https://www.youtube.com/playlist?list=PLsXdBvuJ5ox4N0emQQe7ZjjzwbcB9jRdQ Lesson 18: English Practice Exercises & Test Papers https://www.youtube.com/playlist?list=PLsXdBvuJ5ox6AvA4NUZyNCpfMXIXwDSNq Lesson 19: English के Doubts Clear करो https://www.youtube.com/playlist?list=PLsXdBvuJ5ox7gZn51MoIEMOvLd36mzdKl Lesson 20: English Conversations https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5BU_Hkqwp7v7UdW9X5_-rh Lesson 21: English Speaking with Kids https://www.youtube.com/playlist?list=PLsXdBvuJ5ox7JDlK6GUD3KkyqzbdGZSXm Lesson 22: Listening Practice Exercises https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5b-qNJZTsRYqUqGmOsb9N1 Lesson 23: Pronunciation & Sound Lectures https://www.youtube.com/playlist?list=PLsXdBvuJ5ox4CdWX12bGL396YGeIEhqiS Lesson 24: Do you know? इंग्लिश की छोटी-2 बातें https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5jCZrLMal3-d4Al5yHwYD7 Lesson 25: Subject Verb Agreement for Competitive Exams https://www.youtube.com/playlist?list=PLsXdBvuJ5ox6lS-vakv_E76Ill-AUH-g9 Lesson 26: Interviews Questions & Tips https://www.youtube.com/playlist?list=PLsXdBvuJ5ox6AzcTWgqWpQpRkOeQuuSMd Lesson 27: Letter Writing in English https://www.youtube.com/playlist?list=PLsXdBvuJ5ox5FG7i2wA5bMxcTr7OxnGyi #spokanenglishguru #englishspeakingcourse This is just a text video. Please watch the featured video for better understanding: https://youtu.be/XjmeEbsCuM0 Examples of: Proper Noun: Aman, Ashish, Madhuri, Prateek, Dehradun, Delhi, Gujarat, Jharkhand, UP, Bihar, USHA {fan}, HAVELLS {bulb}, SAMSUNG {mobile}, NOKIA{mobile}, REYNOLDS {pen} etc. Common Noun: Boy, Girl, Man, Woman, Mobile, Pen, Pencil, Temple, Car, Bike, Bag, Cloth etc. Collective Noun: Army, People, Crowd, Batch, Team, Bunch, Family, Class, Committee, Council, Department, Society, Majority, Minority, Audience, Jury, Company, Corporation etc. Material Noun: Air, Silver, Gold, Iron, Copper, Rain, Earth, Salt, Water, Sand, Stone, Coal, Rock, Sunlight etc. Abstract Noun: Honesty, Love, Address, Theft, Hate, Time, Mathematics, Science, Expectation, Beauty, Brilliance, Confidence, Dedication, Courage, Ego, Fear, Hope, Trust, Patience etc.
Views: 1051784 Spoken English Guru
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
 
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In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
Views: 4891 MapR Technologies
Using Decisions In Framing Analytics Problems: A Consulting Perspective - DataEDGE 2015
 
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Using Decisions In Framing Analytics Problems: A Consulting Perspective Friday, May 8, 2015 http://dataedge.ischool.berkeley.edu/2015/schedule/using-decisions-framing-analytics-problems-consulting-perspective In data science applications, a key determinant of success is how the analytical problem is framed, even before any data sources or algorithms are selected. This talk discusses a framework helpful where the goal of analytics is to help organizations use data to make better decisions. Application of the framework begins by asking three key questions related to decision-making, and uses the answers to these questions to guide selection of data sources, algorithms, data visualizations as well as how the organization will use the analytic results: What is the decision being improved by the use of analytics? Who is deciding? What is the value of an improved decision? We’ve found that often analytics project sponsors cannot articulate the answers to these questions, at least at the outset of the project, and that a critical role for us as consultants is to help clients refine the answers, thereby better understanding the problems they are trying to solve refine the answers. Sometimes answering these questions yield results that may be surprising to data scientists, such as that the most technically accurate model may not be the best for a given project or that adding big data to a project may be counterproductive. This talk expands on these questions and illustrates with examples taken from consulting practice. (Note: Some of this talk previews concepts to be covered in the course INFO 290: Managing Analytics Projects, to be taught at the iSchool in the fall of 2015.) David Steier Director, Advanced Analytics and Modeling Deloitte Consulting LLP David Steier is a Director in Deloitte Analytics for Deloitte Consulting LLP’s U.S. Human Capital Practice and is Deloitte’s Technology Black Belt for Unstructured Analytics. Using advanced analytic and visualization techniques, including predictive modeling, social network analysis, and text mining, David and his team of quantitative specialists help clients across a variety of industries to solve some of their most complex technical problems. Prior to joining Deloitte, David was Director of Research at the Center for Advanced Research at PwC, and was a Managing Director at Scient, an e-business services consultancy. David has also authored numerous publications and presentations in applications of advanced technology, including two books and a variety of journal papers, conference papers and workshop presentations. David received his PhD in Computer Science from Carnegie Mellon University, where he is currently an adjunct faculty member teaching a course on Managing Analytics Projects.