** Data Science Certification using R: https://www.edureka.co/data-science ** In this video on Text Mining In R, we’ll be focusing on the various methodologies used in text mining in order to retrieve useful information from data. The following topics are covered in this session: (01:18) Need for Text Mining (03:56) What Is Text Mining? (05:42) What is NLP? (07:00) Applications of NLP (08:33) Terminologies in NLP (14:09) Demo Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - - - - #textmining #textminingwithr #naturallanguageprocessing #datascience #datasciencetutorial #datasciencewithr #datasciencecourse #datascienceforbeginners #datasciencetraining #datasciencetutorial - - - - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyze Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyze data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies. For online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
Views: 5343 edureka!
This video is going to talk about how to use stringr to search, locate, extract, replace, detect patterns from string objects, namely text mining. The key part here is to precisely define the pattern you are looking for that can cover all possible format in your text object. Thanks for watching. My website: http://allenkei.weebly.com If you like this video please "Like", "Subscribe", and "Share" it with your friends to show your support! If there is something you'd like to see or you have question about it, feel free to let me know in the comment section. I will respond and make a new video shortly for you. Your comments are greatly appreciated.
Views: 4049 Allen Kei
This video is using Titanic data file that's embedded in R (see here: https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/Titanic.html). You can find both the data and the code here: https://github.com/A01203249/YouTube-Videos.git. Use git clone to clone this repo locally and use the code.
Views: 50492 Ani Aghababyan
Text Mining with R. Import a single document into R.
Views: 22255 Jalayer Academy
Learn how to perform text analysis with R Programming through this amazing tutorial! Podcast transcript available here - https://www.superdatascience.com/sds-086-computer-vision/ Natural languages (English, Hindi, Mandarin etc.) are different from programming languages. The semantic or the meaning of a statement depends on the context, tone and a lot of other factors. Unlike programming languages, natural languages are ambiguous. Text mining deals with helping computers understand the “meaning” of the text. Some of the common text mining applications include sentiment analysis e.g if a Tweet about a movie says something positive or not, text classification e.g classifying the mails you get as spam or ham etc. In this tutorial, we’ll learn about text mining and use some R libraries to implement some common text mining techniques. We’ll learn how to do sentiment analysis, how to build word clouds, and how to process your text so that you can do meaningful analysis with it.
Views: 4121 SuperDataScience
Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 112053 Bharatendra Rai
A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 13746 Stat Pharm
Sentiment Analysis Implementation and Wordcloud. Find the terms here: http://ptrckprry.com/course/ssd/data/positive-words.txt http://ptrckprry.com/course/ssd/data/negative-words.txt
Views: 25946 Jalayer Academy
Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 23610 Bharatendra Rai
Provides introduction to text mining with r on a Windows computer. Text analytics related topics include: - reading txt or csv file - cleaning of text data - creating term document matrix - making wordcloud and barplots. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 10786 Bharatendra Rai
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: 68282 deltaDNA
Link for R file: https://goo.gl/BXEf7M Provides image or picture analysis and processing with r, and includes, - reading and writing picture file - intensity histogram - combining images - merging images into one picture - image manipulation (brightness, contrast, gamma correction, cropping, color change, flip, flop, rotate, & resize ) - low-pass and high pass filter R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 17539 Bharatendra Rai
Abstract: Attendees will learn the foundations of text mining approaches in addition to learn basic text mining scripting functions used in R. The audience will learn what text mining is, then perform primary text mining such as keyword scanning, dendogram and word cloud creation. Later participants will be able to do more sophisticated analysis including polarity, topic modeling and named entity recognition. Bio: Ted Kwartler is the Director of Customer Success at DataRobot where he manages the end-to-end customer journey. He advocates for and integrates customer innovation into everyday culture and work. He helps to define and organize all customer service functions and key performance indicators. Thus, he incorporates data-driven customer analytics decisions balanced with qualitative feedback to continuously innovate for the customer experience. Specialties: Statistical forecasting and data mining, IT service management, customer service process improvement and project management, business analytics.
Views: 1530 Open Data Science
Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 28392 DataCamp
In this video you will learn how to do Association Rule Mining using R. Also watch our regression & Logistic regression videos on our channel. To Learn Analytics Contact [email protected] Watch all our videos here-http://analyticuniversity.com/
Views: 9297 Analytics University
This video shows six undergrad level data analysis methods in RStudio: 0:01 Intro 0:59 Descriptive stats 2:39 Correlation 4:15 T test 8:19 Chi square 10:01 ANOVA 12:08 Regression R script used in the video: # descriptives attach(TB_data) mean(age_employee) range(age_employee) var(age_employee) sd(age_employee) summary(age_employee) table(gender_employee) table(gender_employee)/102 # correlation cor.test(age_employee,experience) plot(age_employee,experience) cor(TB_data[,10:12]) # T test mean(experience) mean(experience[gender_employee=="M"]) mean(experience[gender_employee=="F"]) range(experience[gender_employee=="M"]) range(experience[gender_employee=="F"]) var(experience[gender_employee=="M"]) var(experience[gender_employee=="F"]) t.test(experience~gender_employee,var.equal=T) t.test(experience~gender_employee,var.equal=F) leveneTest(experience,gender_employee) # Chi sq names(chisq_data) table(Gender) table(Sport) table(Gender,Sport) chisq.test(Gender,Sport) # ANOVA mean(Sup_responsiveness) mean(Sup_responsiveness[Nationality=="German"]) mean(Sup_responsiveness[Nationality=="Chinese"]) mean(Sup_responsiveness[Nationality=="Dutch"]) OUTCOME = aov(Sup_responsiveness~Nationality) summary(OUTCOME) TukeyHSD(OUTCOME) # Regression plot(adverts,sales) plot(airplay,sales) reg1 = lm(sales~adverts) reg2 = lm(sales~adverts+airplay) summary(reg1) summary(reg2) === * In this video, the term "gender" is used in the traditional sense -- biological anatomical sex. The examples dealing with gender are not meant to condone gender and sexual stereotypes. The examples involving nationalities are not intended to condone national, cultural, or ethnical stereotypes. === Please LIKE this video if you enjoyed it. Otherwise, there is a thumb-down button, too... :P ▶ Please SUBSCRIBE to see new videos (almost) every week! ◀ ▼MY OTHER CHANNEL (MUSIC AND PIANO TUTORIALS)▼ https://www.youtube.com/ranywayz ▼MY SOCIAL MEDIA PAGES▼ https://www.facebook.com/ranywayz https://nl.linkedin.com/in/ranywayz https://www.twitter.com/ranywayz Music files retrieved from YouTube Audio Library. All images used in this video are free stock images or are available in the public domain and are labeled for free reuse with modifications. Animations are made with Sparkol. The views expressed in this video are my own and do not necessarily reflect the organizations with which I am affiliated. #RanywayzRandom #RStudio
Views: 725 Ranywayz Random
Learn more about text mining with R: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Boom, we’re back! You used bag of words text mining to make the frequent words plot. You can tell you used bag of words and not semantic parsing because you didn’t make a plot with only proper nouns. The function didn’t care about word type. In this section we are going to build our first corpus from 1000 tweets mentioning coffee. A corpus is a collection of documents. In this case, you use read.csv to bring in the file and create coffee_tweets from the text column. coffee_tweets isn’t a corpus yet though. You have to specify it as your text source so the tm package can then change its class to corpus. There are many ways to specify the source or sources for your corpora. In this next section, you will build a corpus from both a vector and a data frame because they are both pretty common.
Views: 5649 DataCamp
Part 2 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 148344 David Langer
Provides easy to apply example of eXtreme Gradient Boosting XGBoost Algorithm with R . Data: https://goo.gl/VoHhyh R file: https://goo.gl/qFPsmi Machine Learning videos: https://goo.gl/WHHqWP Includes, - Packages needed and data - Partition data - Creating matrix and One-Hot Encoding for Factor variables - Parameters - eXtreme Gradient Boosting Model - Training & test error plot - Feature importance plot - Prediction & confusion matrix for test data - Booster parameters R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 23678 Bharatendra Rai
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: 281336 Siraj Raval
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: 165733 Siraj Raval
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Views: 18633 Artificial Intelligence - All in One
PRE-ORDER THE NEW DAVID GUETTA ALBUM NOW : https://davidguetta.lnk.to/Album7AY Lovers On The Sun available on iTunes http://smarturl.it/itunes-lots Download remixes from Showtek, Stadiumx, Blasterjaxx http://bit.ly/X41EDu Download the TuneMoji® App for iPhone & send FREE music GIFs: http://bit.ly/TuneMoji1TuCWWR LOVERS ON THE SUN (FEAT SAM MARTIN) (David Guetta, Tim Bergling, Giorgio Tuinfort, Frederic Riesterer, Michael Einziger, Sam Martin, Jason Evigan) Produced by David Guetta, Avicii, Giorgio Tuinfort & Frederic Riesterer Publishers: What A Publishing Ltd ; Piano Songs / BMG Talpa Music B.V. (BUMA) / Sony ATV (BMI); Rister Editions ; EMI Scandinavia AB; Warner Chappell ; BMG Rights ; Elementary Particle Music/Universal Music Corp (ASCAP) C/O Universal Music Publishing Group. All instrumentation & programming by David Guetta, Avicii, Giorgio Tuinfort & Frederic Riesterer. Additional Production & programming by Daddy’s Groove. Vocal Production by Jason Evigan. Sound Design by Ralph Wegner. Guitars : Michael Einziger, Sam Martin & Jason Evigan. Piano : Avicii & Giorgio Tuinfort. Recorded at PRMD Studio’s Stockholm, Sweden & Metropolis Studio’s London, UK. Vocals Engineers: John Armstrong, Ryan Gladieux, Vincent Watson. Recording Engineers : Xavier Stephenson & Aaron Ahmad. Orchestra Arrangement written & conducted by Franck van der Heijden. Orchestra Recorded & mixed by Paul Power at Power Sound Studio’s Amsterdam, The Netherlands. Orchestra : Ricciotti Strings. First Violin : Ben Mathot, Ian de Jong, Tseroeja van den Bos, Floortje Beljon, Inger van Vliet, Sofie van der Pol, Sara de Vries, Marleen Veldstra. Second Violin: Judith van Driel, Marleen Wester, Judith Eisenhardt, Diewertje Wanders, Elise Noordhoek, Maartje Korver. Alt Violin: Mark Mulder, Yanna Pelser, Annemarie Hensens, Bram Faber. Cello: David Faber, Thomas van Geelen, Jascha Bordon. Bass: Hinse Mutter, Jesse Feves. Mixed by Daddy’s Groove & David Guetta at Test Pressing Studio’s Naples, Italy. (P) & (C) 2014 What A Music Ltd, Under Exclusive Licence to Parlophone/Warner Music France, a Warner Music Group Company http://www.davidguetta.com http://facebook.com/DavidGuetta http://www.twitter.com/DavidGuetta
Views: 165497480 David Guetta
CHECK OUT THE NEW ROBLOX SONG 👉 https://www.youtube.com/watch?v=SDEfTKM8YWs 🏆6th Annual Bloxy Award Winner 🏆 🎶 GET THE SONG 🎶 ▶︎ Spotify https://goo.gl/TeyajP ▶︎ iTunes https://goo.gl/5TVzk4 ▶︎ Apple Music https://goo.gl/vGEHKg IMPORTANT: PLEASE don't reupload this song or video on your own channel!! This is an original song and an original video that Mr. Miln & I worked super hard on!! Please google "Youtube Copyright" if you want to learn about Youtube's rules around using other people's original content. Of course, anything that's fair use is fine, but make sure to learn about fair use 💞 Don't Call Me A Noob ABC for kid ? My bacon hair is streaky wig I’m just a default male Stuck in Meep city Like a prison cell Where my Robloxian lyfe Is lagging like the server in the dead of night I’m going AFK Till I have enough Robux to really play Kid I’m sorry that I can’t don8 you It doesn’t mean for a second that I hate you And when someone says “learn the rules you noob” But your join date was in like ‘two thousand and two’ Don’t get blue.. just say Don’t call me a Noob I’ve been around the Blox more times than you And yeah I don’t have a lot of Robux But I print a lot of money at the Pizza shop Singing Hey KIDS! What’s that sound ??? Yeah everybody’s throwing that term around But don’t call ME a Noob Bloxburg's about to blow up But I can’t afford 25 bux all up I just wanna role-play But I’m being trolled all day And my Robloxian struggles Got me running round’ poppin' bubble wrap bubbles I’m going AFK Until someone does a Robux giveaway And I’m sorry kid - if I can’t don8 you It doesn’t mean for a second that I hate you! And when someone says “go away noob” But your join date was in like ‘two thousand and two’ Just do what I do And say Don’t call me a Noob I’ve been around the Blox more times than you And I know I don’t have a lot of Robux But I print a lot of money at the Pizza shop Singing HEY KIDS! What’s that sound ??? Nobody’s about to push me around So don’t call ME a Noob No don’t call me a Noob Hey kid what’s the sound?? Everybody’s throwing that term around But don’t call me a Noob No don’t call me a Noob SONG PRODUCED by MR MILN
Views: 16115719 Kawaii Kunicorn
CHECK OUT OUR STORE https://trash.clothing Also check our patreon for exclusives https://www.patreon.com/trash_gang ---- GHOSTEMANE - Mercury Gho$temane https://soundcloud.com/ghostemane/ghostemane-mercury-retrograde TRXSH GXNG --------------------------------------------------------------------------------- ＴＲＡＳＨ 新 ドラゴン - 😈 FB - https://facebook.com/youre.here.on.purpose 😈 IG: https://www.instagram.com/aesthetics_god 😈 IG: https://twitter.com/garbageclub 😈 SHOP - https://trash-gang.com/trash-shop --------------------------------------------------------------------------------- if you have anything you want to show us, work with us, share your ideas, videos, art, contact us here: https://www.trash-gang.com/info-contact PUSHER.co --------------------------------------------------------------------------------- PUSHER & TRASH Montly Video Premiere https://goo.gl/ay5JbS Need Promotion? Try Pusher: https://goo.gl/Y4EDsg
Views: 146064365 ＴＲＡＳＨ 新 ドラゴン
► Website: http://www.rammstein.com ► Shop: http://shop.rammstein.de Premiere: January 29, 2001 Shoot: 13th to 15th January, 2001 Location: Babelsberger Filmstudio, Potsdam Director: Jörn Heitmann Single: Sonne From the Album: Mutter The video shoot for the song SONNE was produced in Potsdam at Babelsberger Filmstudios from the 13th to the 15th of January 2001. It was the first time Jörn Heitmann directed a Rammstein video. SONNE, the first single from the album MUTTER is released soon thereafter. Beside the original and an instrumental version of the song, the single contains the track ADIOS and two remixes by Clawfinger.
Views: 148126105 Rammstein Official
In this video, we'll use machine learning to help classify emotions! The example we'll use is classifying a movie review as either positive or negative via TF Learn in 20 lines of Python. Coding Challenge for this video: https://github.com/llSourcell/How_to_do_Sentiment_Analysis Ludo's winning code: https://github.com/ludobouan/pure-numpy-feedfowardNN See Jie Xun's runner up code: https://github.com/jiexunsee/Neural-Network-with-Python Tutorial on setting up an AMI using AWS: http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/ More learning resources: http://deeplearning.net/tutorial/lstm.html https://www.quora.com/How-is-deep-learning-used-in-sentiment-analysis https://gab41.lab41.org/deep-learning-sentiment-one-character-at-a-t-i-m-e-6cd96e4f780d#.nme2qmtll http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html https://www.kaggle.com/c/word2vec-nlp-tutorial Please Subscribe! And like. And comment. That's what keeps me going. Join us in our Slack channel: wizards.herokuapp.com If you're wondering, I used style transfer via machine learning to add the fire effect to myself during the rap part. 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: 148289 Siraj Raval
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2lXhDAx]. This video gives an overview of the entire course. For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 344 Packt Video
Welcome to Data Lit! This 3-month course is an intro to data science for beginners. In this video, I'll explain how a popular data science technique called sentiment analysis works using a real-world scenario. We'll play the role of a data scientist working at a startup making a personal healthcare device. Using sentiment analysis, we'll understand how consumers feel about a competitors product. That'll help us make decisions on how to promote our own product, and what feature we can focus on the most. Using Python, Twitter, and Google Colab, anyone can do this process in just a few minutes. Enjoy! Code for this video: https://github.com/llSourcell/Sentiment_Analysis Please Subscribe! And Like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Join us at the School of AI: https://theschool.ai/ More learning resources: https://towardsdatascience.com/sentiment-analysis-with-python-part-1-5ce197074184 https://www.geeksforgeeks.org/twitter-sentiment-analysis-using-python/ https://www.datacamp.com/community/tutorials/simplifying-sentiment-analysis-python https://www.kaggle.com/ngyptr/python-nltk-sentiment-analysis https://pythonspot.com/python-sentiment-analysis/ https://www.analyticsvidhya.com/blog/2018/07/hands-on-sentiment-analysis-dataset-python/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ 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 #DataLit #SchoolOfAI #SirajRaval Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 53007 Siraj Raval
LDA is surprisingly simple and anyone can understand it. Here I avoid the complex linear algebra and use illustrations to show you what it does so you will know when to use it and how to interpret the results. Sample code for R is at the StatQuest website: https://statquest.org/2016/07/10/statquest-linear-discriminant-analysis-lda-clearly-explained/ For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest
Views: 134887 StatQuest with Josh Starmer
♪ Out now on iTunes: http://apple.co/2uVgfmL ♪ Amazon UK: http://bit.ly/DiggyAmazonUK ♪ Amazon US: http://bit.ly/DiggyAmazonUS ♥ Diggy Diggy Hole T-shirt: http://bit.ly/DiggyTee ♥ T-shirts and jumpers: https://store.yogscast.com/ Animation by Ciaran: http://www.youtube.com/user/Ceeraanoo Storyboards and art direction by Adam Davis: https://twitter.com/adamladavis Music production by Sparkles*: http://sparkl.es Backing vocals and lots of other work by The Yogscast! Check out our latest Minecraft series Hole Diggers!: https://www.youtube.com/watch?v=1PyOWxpKYQM&list=PL3XZNMGhpynP4JM1ZQo5XHw-ID9MM3I50&index=2 Want more Yogscast? Follow the links below: Subscribe: http://bit.ly/SubscribeYog Twitter: http://www.twitter.com/yogscast Instagram: http://www.instagram.com/yogscast Facebook: https://www.facebook.com/yogscast/ Merch: https://store.yogscast.com/ Twitch: https://www.twitch.tv/yogscast Powered by Chillblast: http://www.chillblast.com/yogscast Mailbox: The Yogscast, PO Box 3125 Bristol BS2 2DG Business enquiries: [email protected] Brothers of the mine rejoice! Swing, swing, swing with me Raise your pick and raise your voice! Sing, sing, sing with me Down and down into the deep Who knows what we'll find beneath? Diamonds, rubies, gold and more Hidden in the mountain store Born underground, suckled from a teat of stone Raised in the dark, the safety of our mountain home Skin made of iron, steel in our bones To dig and dig makes us free Come on brothers sing with me! Chorus I am a dwarf and I'm digging a hole Diggy diggy hole, diggy diggy hole I am a dwarf and I'm digging a hole Diggy diggy hole, digging a hole The sunlight will not reach this low Deep, deep in the mine Never seen the blue moon glow Dwarves won't fly so high Fill a glass and down some mead! Stuff your bellies at the feast! Stumble home and fall asleep Dreaming in our mountain keep Born underground, grown inside a rocky womb The earth is our cradle; the mountain shall become our tomb Face us on the battlefield; you will meet your doom We do not fear what lies beneath We can never dig too deep
Views: 38786699 YOGSCAST Lewis & Simon
Subscribe to 5-Minute Crafts KIDS: https://goo.gl/PEuLVt ---------------------------------------------------------------------------------------- Our Social Media: Facebook: https://www.facebook.com/5min.crafts/ Instagram: https://www.instagram.com/5.min.crafts/ Have you ever seen a talking slime? Here he is – Slick Slime Sam: https://goo.gl/zarVZo The Bright Side of Youtube: https://goo.gl/rQTJZz SMART Youtube: https://goo.gl/JTfP6L ---------------------------------------------------------------------------------------- For more videos and articles visit: http://www.brightside.me/
Views: 267319869 5-Minute Crafts
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2lVMHEP]. This video provides an overview of the entire course. For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 512 Packt Video
Join Bee Gees on Facebook http://facebook.com/beegees & Twitter http://twitter.com/beegeesofficial STAYIN' ALIVE Well, you can tell by the way I use my walk I'm a woman's man, no time to talk Music loud and women warm, I've been kicked around since I was born And now it's all right, it's OK And you may look the other way We can try to understand The New York times effect on man Whether you're a brother or whether you're a mother You're stayin' alive, stayin' alive Feel the city breaking and everybody shaking And were stayin' alive, stayin' alive Ah, ha, ha, ha, stayin' alive, stayin' alive Ah, ha, ha, ha, stayin' alive Well now, I get low and I get high And if I can't get either, I really try Got the wings of heaven on my shoes I'm a dancing man and I just can't lose You know it's all right, it's ok I'll live to see another day We can try to understand The New York times effect on man Whether you're a brother or whether you're a mother You're stayin' alive, stayin' alive Feel the city breakin and everybody shakin And were stayin' alive, stayin' alive Ah, ha, ha, ha, stayin' alive, stayin' alive Ah, ha, ha, ha, stayin' alive Life going nowhere, somebody help me Somebody help me, yeah Life going nowhere, somebody help me Somebody help me, yeah. Stayin' alive From the album 'THE ULTIMATE BEE GEES' BUY THE DELUXE DOUBLE CD & DVD SET Amazon USA http://amzn.to/1brkEjR Amazon Canada http://amzn.to/13m2yuv Amazon UK http://amzn.to/1aT2W9B Amazon France http://amzn.to/1auhjNU Amazon Germany http://amzn.to/12yro7t Amazon Italy http://amzn.to/12XkhoT Amazon Spain http://amzn.to/12NLidu Amazon Japan http://amzn.to/194AqQW BUY THE DOUBLE CD Amazon USA http://amzn.to/1brikth Amazon Canada http://amzn.to/13m1yqh Amazon UK http://amzn.to/11ZpOAr Amazon France http://amzn.to/194C3hv Amazon Germany http://amzn.to/13zPNZf Amazon Italy http://amzn.to/13Qm59C Amazon Spain http://amzn.to/12JOv2f Amazon Japan http://amzn.to/194AqQW DOWNLOAD THE ALBUM iTunes https://itunes.apple.com/album/the-ultimate-bee-gees/id336671732 Amazon USA http://amzn.to/15oILcH Amazon UK http://amzn.to/17ngCEb Amazon France http://amzn.to/15K92lE Amazon Italy http://amzn.to/18pBX4E Amazon Germany http://amzn.to/18mu506 Amazon Spain http://amzn.to/12XiWOL Amazon Japan http://amzn.to/13zLJrN LISTEN ONLINE Deezer http://www.deezer.com/en/album/422914 Grooveshark http://grooveshark.com/#!/album/The+Ultimate+Bee+Gees/4657434 Last.FM http://www.last.fm/music/Bee+Gees/The+Ultimate+Bee+Gees MySpace https://myspace.com/beegees/music/album/the-ultimate-bee-gees-14023661 Rdio http://www.rdio.com/artist/Bee_Gees/album/The_Ultimate_Bee_Gees/ Rhapsody http://www.rhapsody.com/artist/bee-gees/album/the-ultimate-bee-gees Slacker http://slacker.com/r/lLHTB Spotify http://open.spotify.com/album/3JXTUy5Ccdpe79HUgUiMF9 © & ℗ Barry Gibb, The Estate of Robin Gibb and The Estate of Maurice Gibb, under exclusive license to Warner Strategic Marketing Inc., a Warner Music Group Company
Views: 426784060 beegees
Create a Twitter Scrapper using R programming language in these very simple steps. Create scraper and start your analysis within a couple lines of code. This is EASY! Go to http://www.devgin.com for the code and more tips. ----------------------------------------------------------------------------------------------- Hello YouTubers. I include some of the equipment and reviews in the comments because I know many of you out there want to create your own reviews, courses, and tutorials. Creating content is not an easy task. In the links below, I actually own the products and have reviews on some of them. Please help my channel out by exploring some of these options if you choose to create online content on your own. ----------------------------------------------------------------------------------------------- MICROPHONE - https://amzn.to/2LYfJkr BACKPACK - https://amzn.to/2Ep4uez GREEN SCREEN - https://amzn.to/2JVzMgP TRIPOD - https://amzn.to/2Eo2wv4 HOMEPAGE - https://www.markgingrass.com/ REVIEWS/BLOG - https://www.markgingrass.com/blogs/reviews UDEMY COURSES - https://www.udemy.com/cplusplusintro/?couponCode=SHOPCPP0001 ----------------------------------------------------------------------------------------------- SOCIAL MEDIA FB - https://www.facebook.com/GingrassOnline/ INSTA: https://www.instagram.com/markgingrass/ ----------------------------------------------------------------------------------------------- Hands on Programming Book: https://amzn.to/2YMxqor VBA Book: https://amzn.to/2YIPIXQ VBA Book: https://amzn.to/2YLzOvE Interested in C++ Videos? Try this playlist below! ------------------------------------------------------ 1. https://youtu.be/_iHMXDzyrhk 2. https://youtu.be/k4r8I7qMU7w 3. https://youtu.be/VzmpwSKVtl8 4. https://youtu.be/GrswnBf_6nU 5. https://youtu.be/FkfxIxNyHo0 6. https://youtu.be/tmi15fqICYY 7. https://youtu.be/3cmkmTs3y84 8.1 https://youtu.be/KXgN_8Zq5Kk 8.2 https://youtu.be/C1xWJ1Phj2M 8.3 https://youtu.be/DpEqA9-s-bs 9. https://youtu.be/FlUy8QtrzkQ 10. https://youtu.be/653uG59ivNQ 11. https://youtu.be/FKw-G8H9xrs 13. https://youtu.be/FGmG8cvAA7g 14. https://youtu.be/R2cViOOgA2w 14.2 https://youtu.be/pp7Xq1gbym4 15. https://youtu.be/9en3IQqpyjY 16. https://youtu.be/SisaLVV8Ws8 17. https://youtu.be/mU3yjD4aWwc 18. https://youtu.be/6N5bBjjxICo 19. https://youtu.be/hv0lv783KqQ
Views: 620 Mark Gingrass
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2mIPNJq]. This video provides an overview of the entire course. For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 621 Packt Video
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2lXhDAx]. The aim of this video is to show how powerful R is as a data language. We will query an internal example dataset and show how it can be filtered and aggregated on. • Learn about the structure of the internal mtcars dataset • Filter on the dataset • Aggregate on the dataset For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 1013 Packt Video
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2lVMHEP]. This video will walk you through the basics of data visualization along with how to create advanced data visualization using existing libraries in R programming language. • Use ggplot() • Change colors, themes and size of a graph For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 175 Packt Video
Take this course on edX: https://www.edx.org/course/text-mining-analytics-delftx-txt1x#! ↓ More info below. ↓ Follow on Facebook: https://www.facebook.com/edX Follow on Twitter: https://www.twitter.com/edxonline Follow on YouTube: https://www.youtube.com/user/edxonline About this course The knowledge base of the world is rapidly expanding, and much of this information is being put online as textual data. Understanding how to parse and analyze this growing amount of data is essential for any organization that would like to extract valuable insights and gain competitive advantage. This course will demonstrate how text mining can answer business related questions, with a focus on technological innovation. This is a highly modular course, based on data science principles and methodologies. We will look into technological innovation through mining articles and patents. We will also utilize other available sources of competitive intelligence, such as the gray literature and knowledge bases of companies, news databases, social media feeds and search engine outputs. Text mining will be carried out using Python, and could be easily followed by running the provided iPython notebooks that execute the code. FAQ Who is this course for? The course is intended for data scientists of all levels as well as domain experts on a managerial level. Data scientists will receive a variety of different toolsets, expanding knowledge and capability in the area of qualitative and semantic data analyses. Managers will receive hands-on oversight to a high-growth field filled with business promise, and will be able to spot opportunities for their own organization. You are encouraged to bring your data sources and business questions, and develop a professional portfolio of your work to share with others. The discussion forums of the course will be the place where professionals from around the world share insights and discuss data challenges. How will the course be taught? The first week of the course describes a range of business opportunities and solutions centered around the use of text. Subsequent weeks identify sources of competitive intelligence, in text, and provide solutions for parsing and storing incoming knowledge. Using real-world case studies, the course provides examples of the most useful statistical and machine learning techniques for handling text, semantic, and social data. We then describe how and what you can infer from the data, and discuss useful techniques for visualizing and communicating the results to decision-makers. What types of certificates does DelftX offer? Upon successful completion of this course, learners will be awarded a DelftX Professional Education Certificate. Can I receive Continuing Education Units? The TU Delft Extension School offers Continuing Education Units for this course. Participants of TXT1x who successfully complete the course requirements will earn a Certificate of Completion and are eligible to receive 2.0 Continuing Education Units (2.0 CEUs) How do I receive my certificate and CEUs? Upon successful completion of the course, your certificate can be printed from your dashboard. The CEUs are awarded separately by the TU Delft Extension School. ------- LICENSE The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.
Views: 3011 edX
Artificial intelligence is hot field and is much in demand skill for data science and related fields. This video provides insights based on recent articles. Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE
Views: 573 Bharatendra Rai
dplyr is a new R package for data manipulation. Using a series of examples on a dataset you can download, this tutorial covers the five basic dplyr "verbs" as well as a dozen other dplyr functions. Watch the follow-up tutorial: http://youtu.be/2mh1PqfsXVI View the R Markdown document: http://rpubs.com/justmarkham/dplyr-tutorial Download the source document: https://github.com/justmarkham/dplyr-tutorial Read about why I love dplyr: https://www.dataschool.io/dplyr-tutorial-for-faster-data-manipulation-in-r/ Tutorial contents: 1. Introduction to dplyr (starts at 0:00) 2. Loading dplyr and the example dataset (starts at 2:29) 3. Understanding "local data frames" (starts at 3:23) 4. Verb #1: `filter` (starts at 5:17) 5. Verb #2: `select`, plus `contains`, `starts_with`, `ends_with`, `matches` (starts at 7:54) 6. Using chaining syntax for more readable code (starts at 9:34) 7. Verb #3: `arrange` (starts at 12:53) 8. Verb #4: `mutate` (starts at 13:55) 9. Verb #5: `summarise`, plus `group_by`, `summarise_each`, `n`, `n_distinct`, `tally` (starts at 15:31) 10. Window functions: `min_rank`, `top_n`, `lag` (starts at 26:47) 11. Convenience functions: `sample_n`, `sample_frac`, `glimpse` (starts at 32:44) 12. Connecting to databases (starts at 34:21) == RESOURCES == Reference manual and vignettes: http://cran.r-project.org/web/packages/dplyr/index.html July 2014 webinar: http://pages.rstudio.net/Webinar-Series-Recording-Essential-Tools-for-R.html July 2014 webinar code: https://github.com/rstudio/webinars/tree/master/2014-01 Tutorial by Hadley Wickham: https://www.dropbox.com/sh/i8qnluwmuieicxc/AAAgt9tIKoIm7WZKIyK25lh6a GitHub repo: https://github.com/hadley/dplyr List of releases: https://github.com/hadley/dplyr/releases == 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/
Views: 171642 Data School