Search results “Black box text mining with r”
Interpretable Machine Learning Using LIME Framework - Kasia Kulma (PhD), Data Scientist, Aviva
This presentation was filmed at the London Artificial Intelligence & Deep Learning Meetup: https://www.meetup.com/London-Artificial-Intelligence-Deep-Learning/events/245251725/. Enjoy the slides: https://www.slideshare.net/0xdata/interpretable-machine-learning-using-lime-framework-kasia-kulma-phd-data-scientist. - - - Kasia discussed complexities of interpreting black-box algorithms and how these may affect some industries. She presented the most popular methods of interpreting Machine Learning classifiers, for example, feature importance or partial dependence plots and Bayesian networks. Finally, she introduced Local Interpretable Model-Agnostic Explanations (LIME) framework for explaining predictions of black-box learners – including text- and image-based models - using breast cancer data as a specific case scenario. Kasia Kulma is a Data Scientist at Aviva with a soft spot for R. She obtained a PhD (Uppsala University, Sweden) in evolutionary biology in 2013 and has been working on all things data ever since. For example, she has built recommender systems, customer segmentations, predictive models and now she is leading an NLP project at the UK’s leading insurer. In spare time she tries to relax by hiking & camping, but if that doesn’t work ;) she co-organizes R-Ladies meetups and writes a data science blog R-tastic (https://kkulma.github.io/). https://www.linkedin.com/in/kasia-kulma-phd-7695b923/
Views: 15992 H2O.ai
Support Vector Machine (SVM) with R - Classification and Prediction Example
Includes an example with, - brief definition of what is svm? - svm classification model - svm classification plot - interpretation - tuning or hyperparameter optimization - best model selection - confusion matrix - misclassification rate Machine Learning videos: https://goo.gl/WHHqWP svm is an important machine learning tool related to analyzing big data or working in data science field. 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: 39788 Bharatendra Rai
"Why Should I Trust you?" Explaining the Predictions of Any Classifier
Author: Marco Tulio Ribeiro, Department of Computer Science and Engineering, University of Washington Abstract: Despite widespread adoption, machine learning models re- main mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one. In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 10314 KDD2016 video
Support Vector Machine in R | SVM Algorithm Example | Data Science With R Tutorial | Simplilearn
This Support Vector Machine in R tutorial video will help you understand what is Machine Learning, what is classification, what is Support Vector Machine (SVM), what is SVM kernel and you will also see a use case in which we will classify horses and mules from a given data set using SVM algorithm. SVM is a method of classification in which you plot raw data as points in an n-dimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph. SVM is a classification algorithm used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Now, let us get started and understand Support Vector Machine in detail. Below topics are explained in this "Support Vector Machine in R" video: 1. What is machine learning? 2. What is classification? 3. What is support vector machine? 4. Understanding support vector machine 5. Understanding SVM kernel 6. Use case: classifying horses and mules To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/w72XBR Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Support-Vector-Machine-in-R-QkAmOb1AMrY&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 8011 Simplilearn
Support Vector Machines (SVM) Overview and Demo using R
Quick overview and examples /demos of Support Vector Machines (SVM) using R. The getting started with SVM video covers the basics of SVM machine learning algorithm and then finally goes into a quick demo
Views: 59709 Melvin L
Bioconductor Workshop 1: R/Bioconductor Workshop for Genomic Data Analysis
The Computational Biology Core (CBC) at Brown University (supported by the COBRE Center for Computational Biology of Human Disease) and R/Bioconductor Staff team up to provide training on analysis, annotation, and visualization of Next Generation Sequencing (NGS) data. For more info: https://www.brown.edu/academics/computational-molecular-biology/bioconductor-workshop-1-rbioconductor-workshop-genomic-data-analysis Wednesday, February 7th 2018 Brown University
Views: 2504 Brown University
Imagine Dragons - Natural (Lyrics)
Get Origins, ft. Natural, Zero, Machine and Bad Liar, out now: http://smarturl.it/OriginsID Shop Imagine Dragons: http://smarturl.it/ImagineDragonsShop Sign up for email updates: http://smarturl.it/ID_Email Listen to Imagine Dragons on Spotify: http://smarturl.it/ID_Discography Catch Imagine Dragons on tour: http://imaginedragonsmusic.com/tour Follow Imagine Dragons: Facebook: https://www.facebook.com/ImagineDragons Twitter: https://twitter.com/Imaginedragons Instagram: https://www.instagram.com/imaginedragons LYRICS Will you hold the line, when every one of them has given up and given in, tell me In this house of mine Nothing ever comes without a consequence or cost tell me Will the stars align Will heaven step in will it save us from our sin, will it? ‘Cause this house of mine stands strong That's the price you pay Leave behind your heart and cast away Just another product of today Rather be the hunter than the prey And you're standing on the edge face up ‘Cause you’re a natural A beating heart of stone You gotta be so cold To make it in this world Yeah you’re a natural Living your life cutthroat You gotta be so cold Yeah you’re a natural Will somebody, let me see the light within the dark trees shadowing What’s happenin? Looking through the glass find the wrong within the past knowing We are youth Cut until it bleeds inside a world without the peace, face it. A bit of the truth, the truth That's the price you pay Leave behind your heart and cast away Just another product of today Rather be the hunter than the prey And you're standing on the edge face up ‘Cause you’re a natural A beating heart of stone You gotta be so cold To make it in this world Yeah you’re a natural Living your life cutthroat You gotta be so cold Yeah you’re a natural Deep inside me I’m fading to black I’m fading Took an oath by the blood of my hand, won’t break it I can taste it the end is upon us I swear gonna make it I’m gonna make it Natural A beating heart of stone You gotta be so cold To make it in this world Yeah you’re a natural Living your life cutthroat You gotta be so cold Yeah you’re a natural Music video by Imagine Dragons performing Natural. © 2018 KIDinaKORNER/Interscope Records http://vevo.ly/JLrG1u
Views: 94547014 ImagineDragonsVEVO
A Boogie Wit Da Hoodie - Drowning [Official Music Video]
The official video for A Boogie Wit Da Hoodie's "Drowning" featuring Kodak Black from his debut studio album 'The Bigger Artist' - Available Now! Subscribe for the latest official music videos, performances, behind the scenes and more from A Boogie: https://ABoogie.lnk.to/subscribe Download/Stream "Drowning feat. Kodak Black": https://Atlantic.lnk.to/Drowning Download/Stream The Bigger Artist - https://Atlantic.lnk.to/TheBiggerArtist Follow A Boogie Instagram: https://www.instagram.com/ArtistHBTL Twitter: https://twitter.com/ArtistHBTL Facebook: https://www.facebook.com/ArtistHBTL Soundcloud: https://soundcloud.com/a-boogie-wit-da-hoodie Spotify: https://open.spotify.com/artist/31W5EY0aAly4Qieq6OFu6 Website: https://aboogiehbtl.com Follow High Bridge Facebook: https://www.facebook.com/HighBridgeOfficial Twitter: https://twitter.com/Highbridgelabel Instagram: https://www.instagram.com/highbridgethelabel Soundcloud: https://soundcloud.com/user-939666509 Director – The RiTE Brothers Video Commissioner/VP Video Production - Emmanuelle Cuny-Diop Producers – Sam Green & Paris Schulman Creative Director – Lucas Prevost Associate Director, Video Administration – Lily F Thrall Manager, Video Production – Joseph Boyd Assistant, Video Content – Austin Gomez Assistant, Video Production – Trevor Joseph Newton The official YouTube channel of multi-platinum rapper/singer-songwriter: Artist Dubose, known as A Boogie Wit Da Hoodie. He made waves with the breakout hit “Still Think About You” featured on his 2016 debut mixtape, ‘Artist.’ The mixtape also introduced fans to “My Shit” which went on to become RIAA certified platinum and was also listed as one of “The Best Songs of 2016” on Apple Music. A Boogie went on to release the 3x platinum “Drowning feat. Kodak Black” along with the platinum singles “Jungle” and “Timeless.” In fall 2017, A Boogie released his gold certified debut album, ‘The Bigger Artist,’ jumping into the Top 5 on Billboard’s Top 200 and #1 on Billboard’s Emerging Artists chart. He is nominated for the 2018 BET Awards for “Best New Artist” and the release of his sophomore album, ‘Hoodie SZN’ spent two weeks as the #1 album on the Billboard 200. Subscribe for the latest official music videos, performances, behind the scenes and more from A Boogie: https://ABoogie.lnk.to/subscribe #ABoogie #ABoogieWitDaHoodie #Drowning #OfficialVideo #AtlanticRecords #Atlantic #HBTL #HighBridge
Views: 54068548 A-Boogie Wit Da Hoodie
Advanced Data Mining with Weka (3.3: Using R to plot data)
Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 3: Using R to plot data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 4046 WekaMOOC
Daniel Radcliffe Raps Blackalicious' "Alphabet Aerobics"
Jimmy challenges hip-hop lover Daniel Radcliffe to rap Blackalicious' tongue-twisting "Alphabet Aerobics." Subscribe NOW to The Tonight Show Starring Jimmy Fallon: http://bit.ly/1nwT1aN Watch The Tonight Show Starring Jimmy Fallon Weeknights 11:35/10:35c Get more Jimmy Fallon: Follow Jimmy: http://Twitter.com/JimmyFallon Like Jimmy: https://Facebook.com/JimmyFallon Get more The Tonight Show Starring Jimmy Fallon: Follow The Tonight Show: http://Twitter.com/FallonTonight Like The Tonight Show: https://Facebook.com/FallonTonight The Tonight Show Tumblr: http://fallontonight.tumblr.com/ Get more NBC: NBC YouTube: http://bit.ly/1dM1qBH Like NBC: http://Facebook.com/NBC Follow NBC: http://Twitter.com/NBC NBC Tumblr: http://nbctv.tumblr.com/ NBC Google+: https://plus.google.com/+NBC/posts The Tonight Show Starring Jimmy Fallon features hilarious highlights from the show including: comedy sketches, music parodies, celebrity interviews, ridiculous games, and, of course, Jimmy's Thank You Notes and hashtags! You'll also find behind the scenes videos and other great web exclusives. Daniel Radcliffe Raps Blackalicious' "Alphabet Aerobics" http://www.youtube.com/fallontonight
Text Analytics Challenges
Text Analytics Challenges
Views: 275 DecoodaTV
SAS® Text Analytics Software Demo
http://www.sas.com/en_us/software/analytics/text-miner.html SAS Text Analytics help companies address big data issues that arise from unstructured content by applying linguistic rules and statistical methods. SAS TEXT MINER Get faster, deeper insight from unstructured data. Why limit yourself to analyzing legacy data? Our text mining software lets you easily analyze text data from the web, comment fields, books and other text sources. Discover new information, topics and term relationships that deepen your understanding. And add what you learn to your models to improve lift and performance. Benefits: * Improve model performance. * Add subject-matter expertise. * Automatically know more. * Determine what's hot and what's not. LEARN MORE ABOUT SAS TEXT MINER http://www.sas.com/en_us/software/analytics/text-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss To learn more about SAS Text Analytics, visit http://www.sas.com/textanalytics
Views: 24840 SAS Software
Data Science at Scale with R on GCP (Cloud Next '19)
Cloud computing has opened up new opportunities for the R programming community. Google Cloud lets R developers enhance and scale their data science work to large complex datasets. We will cover (1) managed GCP services for R (2) training & serving architectures and (3) end-to-end R pipelines on GCP "Announcing the beta release of SparkR job types in Cloud Dataproc → https://bit.ly/2K6wFo3 Cloud Machine Learning Engine → https://bit.ly/2K55JF3" Watch more: Next '19 ML & AI Sessions here → https://bit.ly/Next19MLandAI Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform Speaker(s): Mikhail Chrestkha, Christopher Crosbie, Greg Mikels Session ID: MLAI214 product:Cloud ML Engine,Cloud Dataproc,BigQuery,APIs,TensorFlow; fullname:Mikhail Chrestkha,Christopher Crosbie,Greg Mikels;
Michael Jackson - Man In The Mirror (Official Video)
Michael Jackson's official video for "Man In The Mirror" In keeping with the lyrical message of "Man in the Mirror," which was strongly identified with Michael Jackson and reflective of his own philosophies, the short film features powerful images of events and leaders whose work embodies the song's message to "make that change." Rolling Stone praised the short film in 2014 as "a powerful statement to deliver to personality-driven MTV." Buy/Listen to Bad 25: Amazon - http://smarturl.it/mj_bad25_amzn?IQid=ytd.mj.mitm iTunes - http://smarturl.it/MJ_Bad25A_iTunes?IQid=ytd.mj.mitm Official Store - http://smarturl.it/MJBAD25_OS?IQid=ytd.mj.mitm Spotify - http://smarturl.it/MJ_Bad_Sptfy?IQid=ytd.mj.mitm Written by Siedah Garrett and Glen Ballard Produced by Quincy Jones for Quincy Jones Productions Co-Produced by Michael Jackson for MJJ Productions, Inc. From the album Bad, released August 31, 1987 Released as a single January 16, 1988 THE SHORT FILM Director: Don Wilson Michael Jackson's short film for "Man in the Mirror" was the third of nine short films produced for recordings from Bad, one of the best selling albums of all time. The "Man in the Mirror" single hit No. 1 in four countries in 1988, topping the charts in the United States, Italy, Belgium and Poland and reaching Top 5 in Canada, Ireland and New Zealand. In the U.S., "Man in the Mirror" was the fourth of five consecutive No. 1 singles from one album on the Billboard Hot 100-making Michael the first artist to achieve this milestone. "Man in the Mirror," written by Siedah Garrett (Michael's duet partner on "I Just Can't Stop Loving You") and Glen Ballard, is one of only two songs on Bad not written by Michael Jackson and, even though it wasn't a song he wrote himself, it was a message that was strongly identified with him and reflective of his own philosophies, as demonstrated through his actions and expressed in some of his own lyrics. "'Man in the Mirror' has a great message," he wrote in his 1988 memoir Moonwalk. "I love that song. ..Start with yourself. Don't be looking at all the other things. Start with you. That's the truth." A review of Bad in Rolling Stone in 1987 called the song "among the half dozen best things Jackson has done." In contrast to Michael's other short films of the Bad era, "Man of the Mirror" tells a story not through performance, but through powerful images of oppression, homelessness, hunger, police brutality and other ills of the world, as well as events and leaders of the 20th century whose work is reflective of the song's message to "make that change." Follow the Official Michael Jackson Accounts: Facebook - http://smarturl.it/mj_facebook?IQid=ytd.mj.mitm Twitter - http://smarturl.it/mj_twitter?IQid=ytd.mj.mitm Spotify - http://smarturl.it/mj_spotify?IQid=ytd.mj.mitm Newsletter - http://smarturl.it/mj_newsletter?IQid=ytd.mj.mitm YouTube - http://smarturl.it/mj_youtube?IQid=ytd.mj.mitm Website - http://smarturl.it/mj_website?IQid=ytd.mj.mitm #MichaelJackson #ManInTheMirror #OfficialMusicVideo
Views: 102208508 michaeljacksonVEVO
Symbolic and Subsymbolic AI for Sentiment Analysis | ForwardLeading Big Data & AI Leaders Summit
This presentation was delivered by Erik Cambria, Associate Professor & AI's 10 to Watch 2018, from Nanyang Technological University at the Big Data & AI Leaders Summit, Singapore 2018. With the recent developments of deep learning, AI research has gained new vigor and prominence. However, machine learning still faces three big challenges: (1) it requires a lot of training data and is domain-dependent; (2) different types of training or parameter tweaking leads to inconsistent results; (3) the use of black-box algorithms makes the reasoning process uninterpretable. At SenticNet, we address such issues in the context of NLP via sentic computing, a multidisciplinary approach that aims to bridge the gap between statistical NLP and the many other disciplines necessary for understanding human language such as linguistics, commonsense reasoning, and affective computing. Sentic computing is both top-down and bottom-up: top-down because it leverages symbolic models such as semantic networks and conceptual dependency representations to encode meaning; bottom-up because it uses subsymbolic methods such as deep neural networks and multiple kernel learning to infer syntactic patterns from data. Watch full sessions of the Big Data & AI Leaders Summit Singapore 2018 via Leading Online: https://forwardleading.co.uk/leading-online Follow Forward Leading on: LinkedIn: https://www.linkedin.com/company/forwardleading/ Twitter: https://twitter.com/ForwardLeading Facebook: https://www.facebook.com/forwardleadingLTD/ Instagram: https://www.instagram.com/forwardleadingltd/
Views: 109 ForwardLeading
IHPI Seminar: White Coat, Black Box: Augmenting Clinical Care with AI in the Era of Deep Learning
February 21, 2019 Speaker: Jenna Wiens, Ph.D., assistant professor engineering, Department of Electrical Engineering and Computer Science, U-M College of Engineering Jenna Wiens is a Morris Wellman Assistant Professor of Computer Science and Engineering (CSE) at the University of Michigan in Ann Arbor. Her primary research interests lie at the intersection of machine learning, data mining, and healthcare. She is particularly interested in time-series analysis and transfer/multitask learning. The overarching goal of her research agenda is to develop the computational methods needed to help organize, process, and transform patient data into actionable knowledge.
Views: 222 Michigan Medicine
Support Vector Machine Intro and Application  - Practical Machine Learning Tutorial with Python p.20
In this tutorial, we introduce the theory of the Support Vector Machine (SVM), which is a classification learning algorithm for machine learning. We also show how to apply the SVM using Scikit-Learn on some familiar data. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 94099 sentdex
How SVM (Support Vector Machine) algorithm works
In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share
Views: 534216 Thales Sehn Körting
Driverless AI Hands-On Focused on Machine Learning Interpretability - H2O.ai
This video was recorded at #H2OWorld 2017 in Mountain View, CA. Enjoy the slides: https://www.slideshare.net/0xdata/driverless-ai-handson-focused-on-machine-learning-interpretability-h2oai. Learn more about H2O.ai here: https://www.h2o.ai/. Follow @h2oai: https://twitter.com/h2oai. - - - Abstract: Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them. This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you! Patrick Hall is a senior director for data science products at H2O.ai where he focuses mainly on model interpretability. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning. Prior to joining H2O.ai, Patrick held global customer facing roles and R & D research roles at SAS Institute. He holds multiple patents in automated market segmentation using clustering and deep neural networks. Patrick was the 11th person worldwide to become a Cloudera certified data scientist. He studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University. Navdeep Gill is a Software Engineer/Data Scientist at H2O.ai. He graduated from California State University, East Bay with a M.S. degree in Computational Statistics, B.S. in Statistics, and a B.A. in Psychology (minor in Mathematics). During his education, he gained interests in machine learning, time series analysis, statistical computing, data mining, & data visualization. Previous to H2O.ai he worked at Cisco Systems, Inc. focusing on data science & software development. Before stepping into industry, he worked in various Neuroscience labs as a researcher/analyst. These labs were at institutions such as California State University, East Bay, University of California, San Francisco, and Smith Kettlewell Eye Research Institute. His work across these labs varied from behavioral, electrophysiology, and functional magnetic resonance imaging research. In his spare time Navdeep enjoys watching documentaries, reading (mostly non-fiction or academic), and working out. Mark Chan is a hacker at H2O.ai. He was previously in the finance world as a quantitative research developer at Thomson Reuters and Nipun Capital. He also worked as a data scientist at an IoT startup, where he built a web-based machine learning platform and developed predictive models. Mark has a MS Financial Engineering from UCLA and a BS Computer Engineering from University of Illinois Urbana-Champaign. In his spare time Mark likes competing on Kaggle and cycling.
Views: 1135 H2O.ai
Letters For Toddlers | Alphabets For Kids | ABCD For Children | A For Apple
Letters For Toddlers | Alphabets For Kids | ABCD For Children | A For Apple In this video children will be able to learn how to write alphabets and letters and also kids will learn two things that start with each alphabet with the help of 3D animation and colorful visuals. Lyrics A for Apple | a for apple B for Bus | b for ball C for Cat | c for car D for Dog | d for dog E for Elephant | e for egg F for Fish | f for flag G for Goat | g for gate H for Hen | h for hat I for Igloo | i for ice cream cart J for Jack in the box | j for juice K for Kangaroo | k for kite L for Lion | l for lamp M for Magnet | m for mango N for Nest | n for nine O for Orange | o for oven P for Parrot | p for pigeon Q for Quiver | q for question R for Rocket | r for road roller S for Ship | s for sun T for Tree | t for tricycle U for Unicycle | u for umbrella V for Van | v for vase W for Wheel | w for watch X for Box | x for x-mas tree Y for Yatch | y for yellow Z for Zoo | z for zebra
Weka Text Classification for First Time & Beginner Users
59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 138170 Brandon Weinberg
Midnight Oil - Beds Are Burning
Midnight Oil's official music video for 'Beds Are Burning'. Click to listen to Midnight Oil on Spotify: http://smarturl.it/MidnightOilSpotify... As featured on Diesel And Dust. Click to buy the track or album via iTunes: http://smarturl.it/MidnightOilDADiTun... Google Play: http://smarturl.it/MidnightOilGPlay?I... Stream more music from Midnight Oil here: http://smarturl.it/MidOilMulti?IQid=M... More from Midnight Oil Blue Sky Mine: https://youtu.be/Ofrqm6-LCqs The Dead Heart: https://youtu.be/16bFBzx7I_0 Forgotten Years: https://youtu.be/X9eap_cKLP4 More great 80's videos here: http://smarturl.it/Ultimate80?IQid=Mi... Follow Midnight Oil Website: http://www.midnightoil.com Facebook: http://www.facebook.com/midnightoilof... Subscribe to Midnight Oil on YouTube: http://smarturl.it/MidnightOilSubscri... --------- Lyrics: The time has come To say fair's fair To pay the rent To pay our share The time has come A fact's a fact It belongs to them Let's give it back How can we dance When our earth is turning How do we sleep While our beds are burning" #MidnightOil #BedsAreBurning #Vevo #ClassicRock #VevoOfficial
Views: 131604477 MidnightOilVEVO
Rammstein - Sonne (Official Video)
► 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: 142744449 Rammstein Official
Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
Find the rest of the How Neural Networks Work video series in this free online course: https://end-to-end-machine-learning.teachable.com/p/how-deep-neural-networks-work A gentle walk through how they work and how they are useful. Some other helpful resources: RNN and LSTM slides: http://bit.ly/2sO00ZC Luis Serrano's Friendly Intro to RNNs: https://youtu.be/UNmqTiOnRfg How neural networks work video: https://youtu.be/ILsA4nyG7I0 Chris Olah's tutorial: http://bit.ly/2seO9VI Andrej Karpathy's blog post: http://bit.ly/1K610Ie Andrej Karpathy's RNN code: http://bit.ly/1TNCiT9 Andrej Karpathy's CS231n lecture: http://bit.ly/2tijgQ9 DeepLearning4J tutorial: https://deeplearning4j.org/lstm RNN/LSTM blog post: https://brohrer.github.io/how_rnn_lstm_work.html Data Science and Robots blog: https://brohrer.github.io/blog.html Follow me for announcements: https://twitter.com/_brohrer_
Views: 324898 Brandon Rohrer
How to Install Codeblocks IDE on Windows 10 with Compilers ( GCC , G++)
In this video I am going to show How to Install Codeblocks IDE on Windows 10 with Compilers. We will see how to install MinGw compiler with code blocks. ( GCC , G++) -------------------Online Courses to learn---------------------------- Data Analytics with R Certification Training- http://bit.ly/2rSKHNP DevOps Certification Training - http://bit.ly/2T5P6bQ AWS Architect Certification Training - http://bit.ly/2PRHDeF Python Certification Training for Data Science - http://bit.ly/2BB3PV8 Java, J2EE & SOA Certification Training - http://bit.ly/2EKbwMK AI & Deep Learning with TensorFlow - http://bit.ly/2AeIHUR Big Data Hadoop Certification Training- http://bit.ly/2ReOl31 AWS Architect Certification Training - http://bit.ly/2EJhXjk Selenium Certification Training - http://bit.ly/2BFrfZs Tableau Training & Certification - http://bit.ly/2rODzSK Linux Administration Certification Training-http://bit.ly/2Gy9GQH ----------------------Follow--------------------------------------------- My Website - http://www.codebind.com My Blog - https://goo.gl/Nd2pFn My Facebook Page - https://goo.gl/eLp2cQ Google+ - https://goo.gl/lvC5FX Twitter - https://twitter.com/ProgrammingKnow Pinterest - https://goo.gl/kCInUp Text Case Converter - https://goo.gl/pVpcwL ------------------Facebook Links ---------------------------------------- http://fb.me/ProgrammingKnowledgeLearning/ http://fb.me/AndroidTutorialsForBeginners http://fb.me/Programmingknowledge http://fb.me/CppProgrammingLanguage http://fb.me/JavaTutorialsAndCode http://fb.me/SQLiteTutorial http://fb.me/UbuntuLinuxTutorials http://fb.me/EasyOnlineConverter Best C++ Complier : How to Install Code:Block in Windows 10 , Windows c++ - Setting up MingW and Code::Blocks in Windows 10 64 Searches related to install codeblocks on windows 10 how to install codeblocks on mac download codeblocks for windows download codeblocks for windows 10 64 bit download codeblocks for windows 8 install gcc windows
Views: 395034 ProgrammingKnowledge
Design Is [Elemental] - From Building Blocks to Great Experiences
Design systems offer us the building blocks and the elements, for designing delightful digital engagements. In this talk I will share an analogy and a metaphor for thinking about the elements that make for delightful digital experiences. My analogy/metaphor is the Periodic Table of Elements. I will share a little about the history of the Periodic Table of elements, and I will suggest that design systems like Material Design offer us the beginnings of a new table of interacting and interactional elements. I will chart a story that takes us from alchemy to science and from black-box inspiration to validated design elements, from design thinking to design doing. I will invite us to reconsider the relationship between art, science, design, & data. Design Is […] is a monthly speaker series on the future of design and creativity. Each public talk is centered on a theme, and the series highlights a broad range of perspectives on everything from human-centered design to VR and ethics. Learn more at → https://goo.gl/d9JSxL Watch more videos on the Design Is [...] Playlist → https://bit.ly/2UGgU7z Subscribe to the Design Channel for more videos like this → https://bit.ly/G-Design1
Views: 2450 Google Design
Touchdown Localisation with aircraft flight data - Jonathan G. Pelham
PyData London 2018 How to take data from an aircraft black box and use it in the real world to improve landing safety using python. A discussion of methods, data quality, and finding solutions. --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 357 PyData
How Machine Learning Helps Humans Search Millions of Documents Instantly
TRANSCRIPT: Exaptive uses sophisticated technology to make discoveries easier, especially when researchers are looking through data contained within millions of documents. We use machine learning to facilitate the exploration of data that would otherwise be too vast to support valuable insights. Machine Learning allows for a model to improve over time.. given new training data.. without requiring more human effort. For example, a common “text-classification benchmark task” is to train a model on messages for multiple discussion board threads.. and then later use it to predict what the topic of discussion was.. Whether it was space, computers, religion, anything. Besides being able to classify new texts, Machine Learning approaches can also attempt to identify the authors or find similar documents. The ability to identify similar documents can lead to a “recommender system” for new content that a user might find interesting. Machine Learning-based models are commonly desired to be “black-box” in the sense that a user desires to be able to put data in.. and get answers out.. without having to know the details of how this is achieved. However, there is usually a desire to understand the resulting model and why a recommendation is given. There is also a desire to understand a collection of texts, such as search results, where the user may want a summary of a 100 page list of a thousand “ranked results.” In this use case, we build a data landscape, which is a visualization of the documents that conveys their similarities as well as the relationships to key terms which were identified when learning the model. In one application of data landscape technology, Exaptive processed over 100 million documents which had been “machine read” from “scanned documents” via “optical character recognition”. Some of the documents were hundreds of years old. We recorded counts for roughly 200,000 words.. and then estimated the importance of those words to the documents as a “feature engineering” step. This measure is known as “term frequency-inverse document frequency” or TF-IDF. “Singular value decomposition”.. or S-V-D.. was then used to find high level concepts which are each defined by many words. At that point in the process, documents are described by high level concepts that align with areas of medicine, economics, religion, politics, et cetera. The concepts that are learned are data-dependent. If only medical documents are used, then the model’s resources will be used to identify more “finely-detailed” categories. We then clustered the documents in that “topic space” to find which documents are similar. The “silhouette coefficient measure” allowed us to automatically select a good “number of clusters.” Next, we projected the documents down to a two dimensional scatterplot using a combination of SVD and multi-dimensional scaling. Based on the density of the documents, we fit a contour map, which looks like a topological map. Color varies across the contour map according to the cluster assignment for documents in that area. Finally, we solve for landmarks which correspond to the x-y location of the key “driver terms” for each cluster. Using these same concepts, the Exaptive team designed the PubMed® Explorer. PubMed Explorer makes it easy to search PubMed’s extensive collection of papers. One of the visualizations provided is a “term landscape”. The term landscape is similar to the key “term landmarks” from the previously described data landscape. The positions are found in a more direct method by projecting TF-IDF values directly to 2-D. For a collection of search results, the user may then view a two-dimensional landscape where related terms are grouped together spatially. Depending on how this project is performed, it is easy to obtain either the documents locations, or the term locations. This allows us to provide the user with options to create the same visualization for articles or journals, instead of topics. As with the previously described visualizations, the documents are categorized using clustering which provides for distinction with the term and cluster colors. Many people associate Machine Learning with A-I, or Artificial Intelligence. At Exaptive, we use it to support I-A, or “intelligence augmentation.” The difference is.. that instead of using machine learning to eliminate the need for humans in a process, the technology supports the intelligence of the human researcher, so researchers can accomplish more than what would otherwise be humanly possible. www.exaptive.com
Views: 146 Exaptive
Documents Similarity Measures 1l Screen
Project Name: Learning by Doing (LBD) based course content development Project Investigator: Prof. Sandhya Kode Module Name: Using semantic information for text mining
Views: 137 Vidya-mitra
Backstreet Boys - Show Me The Meaning Of Being Lonely (Official Music Video)
Backstreet Boys' official music video for 'Show Me The Meaning Of Being Lonely'. Click to listen to Backstreet Boys on Spotify: http://smarturl.it/BBSpot?IQid=BBBL Backstreet Boys will be going back on tour in 2017 starting on March 1! Buy/Listen The Essential Backstreet Boys: Amazon - http://smarturl.it/bsb_te_amzn?IQid=y... iTunes - http://smarturl.it/bsb_te_itunes?IQid... Spotify - http://smarturl.it/bsb_te_spotify?IQi... About The Essential: Two CD 29-track compilation from the boyband featuring hits, album tracks and fan favorites including "Everybody (Backstreet's Back)", "I Want It That Way", "Larger Than Life", "Quit Playing Games (With My Heart)" and more favorites. More from Backstreet Boys Incomplete: https://youtu.be/WVe80iZtlYU I Want It That Way: https://youtu.be/4fndeDfaWCg As Long As You Love Me: https://youtu.be/0Gl2QnHNpkA More great Ultimate Hits Of The Nineties videos here: http://smarturl.it/UHNPlaylist?IQid=BBTW Follow Backstreet Boys Website - http://www.backstreetboys.com/ Facebook - https://www.facebook.com/backstreetboys Twitter - https://twitter.com/backstreetboys YouTube - http://smarturl.it/BKBSub?IQid=BBTW #BackstreetBoys #ShowMeTheMeaningOfBeingLonely #Vevo #Pop #OfficialMusicVideo --------- Lyrics: Show me the meaning of being lonely Is this the feeling I need to walk with? Tell me why I can't be there where you are There's something missing in my heart Life goes on as it never ends Eyes of stone observe the trends They never say forever gaze if only Guilty roads to an endless love (endless love) There's no control Are you with me now? Your every wish will be done They tell me
Views: 141190511 BackstreetBoysVEVO
just a regular crab rave, nothing to see here folks
10h version: https://youtu.be/f_A_yZfWcxI Feel free to use this meme wherever you like! (just remember to credit me, would appreciate it) If you would like to contact me regarding the video or something else you have in mind, please do so through my Twitter or SoundCloud accounts! Links are at the bottom of the description! ORIGINAL SONG: Noisestorm - Crab Rave https://www.youtube.com/watch?v=LDU_Txk06tM ▼Follow Noisestorm Facebook: https://www.facebook.com/Noisestorm Twitter: http://twitter.com/NoisestormMusic Soundcloud: https://soundcloud.com/noisestorm Youtube: https://www.youtube.com/EoinOBroinMusic ▼ Follow Monstercat Spotify: http://monster.cat/2biZbkd Apple Music: http://apple.co/2xiKWTO Facebook: http://facebook.com/Monstercat Twitter: http://twitter.com/Monstercat Instagram: http://instagram.com/monstercat Snapchat: https://www.snapchat.com/add/monstercat SoundCloud: http://soundcloud.com/Monstercat Sans greenscreen by chiliastic Youtube: https://www.youtube.com/channel/UCuJGCBue0p0HNkYIKBbUyzA Twitter: https://twitter.com/_TheMultiMan_ Tumblr: http://multimuu.tumblr.com/ Shrek blue screen by Dylan O'Connor Youtube: https://www.youtube.com/channel/UC_92ucKsHTPBJhQfNmNvWXA and obviously, melody and the background at the end of the drop was taken from Megalovania made by Toby Fox https://tobyfox.bandcamp.com/album/undertale-soundtrack ----------------------------------------------------------------------------- Twitter: https://twitter.com/fluxxxy_ Twitch: https://www.twitch.tv/fluxxxy Soundcloud: https://soundcloud.com/fluxoid ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Views: 6341668 Fluxxy
Complete Data Science Course | What is Data Science? | Data Science for Beginners | Edureka
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 30432 edureka!
Rethinking classical approaches to analysis and predictive modeling
Synopsis: The speaker will address the need to rethink classical approaches to analysis and predictive modeling. He will examine "iterative analytics" and extremely fine grained segmentation down to a single customer -- ultimately building one model per customer or millions of predictive models delivering on the promise of "segment of one" . The speaker will also address the speed at which all this has to work to maintain a competitive advantage for innovative businesses. Speaker: Afshin Goodarzi, Chief Analyst 1010data A veteran of analytics, Goodarzi has led several teams in designing, building and delivering predictive analytics and business analytical products to a diverse set of industries. Prior to joining 1010data, Goodarzi was the Managing Director of Mortgage at Equifax, responsible for the creation of new data products and supporting analytics to the financial industry. Previously, he led the development of various classes of predictive models aimed at the mortgage industry during his tenure at Loan Performance (Core Logic). Earlier on he had worked at BlackRock, the research center for NYNEX (present day Verizon) and Norkom Technologies. Goodarzi's publications span the fields of data mining, data visualization, optimization and artificial intelligence. Sponsor: 1010Data [ http://1010data.com ] Microsoft NERD [ http://microsoftnewengland.com ] Cognizeus [ http://cognizeus.com ]
Views: 887 AnalyticsWeek
SVM with polynomial kernel visualization
A visual demonstration of the kernel trick in SVM. This short video demonstrates how vectors of two classes that cannot be linearly separated in 2-D space, can become linearly separated by a transformation function into a higher dimensional space. The transformation used is: f([x y]) = [x y (x^2+y^2)] If you would like a stand-alone file with a high-res version of this movie for academic purposes please contact me. Visit my homepage http://www.zutopedia.com/udia.html, or read about my latest book "Zuto: The Adventures of a Computer Virus", http://www.zutopedia.com
Views: 292935 udiprod
Deep Learning Semantic Segmentation for Nucleus Detection - Dawid Rymarczyk
PyData Warsaw 2018 Semantic segmentation is the process which aims to classify individual pixels of an image. Recently, Kaggle hosted the 2018 Data Science Bowl competition dedicated to nucleus detection and segmentation based on microscopic images. In this talk, I will present two approaches to this problem, based on U-Net and Mask R-CNN. === www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 328 PyData
Tutorial Video for Coupling OptiY to PTC Creo Parametric
OptiY® is an open and multidisciplinary design environment providing most modern optimization strategies and state of the art probabilistic algorithms for uncertainty, reliability, robustness, sensitivity analysis, fatigue life prediction, data-mining and meta-modeling. The simulation model can be considered as black box with inputs and outputs. Within, it is an open platform for different kind of model classes. The adaptation to a special simulation environment takes place by a suitable interface. Collaborating different simulation systems is possible as networks, finite-element-method, rigid body dynamics, also material test bench as control optimization for drives.
Views: 67 OptiY
Kodak Black - Codeine Dreaming (feat. Lil Wayne) [Official Audio]
The official audio of Kodak Black's "Codeine Dreaming (feat. Lil Wayne)" from the mixtape 'Project Baby 2: All Grown Up'. Download/Stream “Project Baby 2 All Grown Up": https://Atlantic.lnk.to/ProjectBaby2AllGrownUp Get tickets to see Kodak on tour: https://Kodak.lnk.to/DyingToLiveTourID Follow Kodak Black https://twitter.com/KodakBlack1k https://facebook.com/TheRealKodakBlack https://instagram.com/kodakblack https://soundcloud.com/kodak-black https://open.spotify.com/artist/46SHBwWsqBkxI7EeeBEQG7 The official YouTube channel of Atlantic Records artist Kodak Black. 2017 saw Kodak rack up impressive certifications including: his gold-certified album ‘Painting Pictures’, 2x platinum single “Tunnel Vision”, and his gold-certified track “Too Many Years (feat. PnB Rock)”. To round out 2017, Kodak Black was named on both Rolling Stone’s and Complex’s “Best of 2017” lists, as well as Billboard’s “21 Under 21” list. In the same year, he released ‘Project Baby 2’ with the single “Codeine Dreaming”, which reached #52 on the Billboard Hot 100. His 2018 mixtape ‘Heart Break Kodak (HBK)’ went on to reach #15 on the US Top R&B/Hip-Hop chart. Following this release, Kodak revealed his studio album ‘Dying to Live’, which debuted #1 on the Billboard 200. The album featured the hits “Testimony” and “Zeze (feat. Travis Scott & Offset)”, which earned #1 on the Top R&B/Hip-Hop Songs and Billboard 200 charts. He has also collaborated with major artists such as Gucci Mane and Bruno Mars on “Wake Up In The Sky” and XXXTentacion on “Roll In Peace”, both of which have also gained platinum certifications. Subscribe for the latest official music videos, official audio videos, performances, behind the scenes and more from Kodak Black: https://Atlantic.lnk.to/KBsubscribe #KodakBlack #CodeineDreaming #LilWayne #ProjectBaby2AllGrownUp #OfficialAudio #Atlantic #AtlanticRecords
Views: 55181088 Kodak Black
BHAD BHABIE feat. Lil Yachty - "Gucci Flip Flops" (Official Music Video) | Danielle Bregoli
BHAD BHABIE "Gucci Flip Flops" ft. Lil Yachty ⛵️ Official Music Video 🔥 STreAM NOW ⏯ http://Atlantic.lnk.to/gucciflipflops - Directed by Nicholaus Goossen 🎤✈️🚍 #BHANNEDINTHEUSA TOUR - MEET & GREET VIPs & TIX ON SALE NOW 🎟👋🏻🔥 ↘️➡️ https://bhadbhabie.com ⬅️↖️ Fri. 05/04 - Miami, FL - The Hanger Sat. 05/05 - Orlando, FL - The Beacham Sun. 05/06 - Atlanta, GA - Hell - Masquerade Tue. 05/08 - Washington, DC - Milkboy Arthouse Wed. 05/09 - New York, NY - SOBs Thu. 05/10 - Philadelphia, PA - Noto Fri. 05/11 - Boston, MA - Middle East Downstairs (Early Show) Sat. 05/12 - New Haven, CT - Toad's Place Mon. 05/14 - Toronto, ON Canada - Mod Club Tue. 05/15 - Chicago, IL - Bottom Lounge Wed. 05/16 - Detroit, MI - Shelter Fri. 05/18 - Indianapolis, IN - Emerson Theatre Tue. 05/22 - Denver, CO - Cervantes - Otherside Fri. 05/25 - Phoenix, AZ - Club Red Sat. 05/26 - San Diego, CA - SOMA Tue. 05/29 - San Francisco, CA - Slim's Wed. 05/30 - Sacramento, CA - Ace of Spades Fri. 06/01 - Portland, OR - Peter's Room Sat. 06/02 - Spokane, WA - Knitting Factory Sun. 06/03 - Seattle, WA - Nuemos Tue. 06/05 - Vancouver, BC - Venue Thu. 06/14 - Los Angeles, CA - The Roxy I'm going to Europe too biches 👅 Fri. 07/06 - Barcelona, Spain - Sala Razzmatazz Sat. 07/07 - Gräfenhainichen, Germany - Splash! Festival Sun. 07/08 - Liège, Belgium - Les Ardentes Mon. 07/09 - Paris, France - La Maroquinerle Wed. 07/11 - London, UK - O2 Islington Academy Thu. 07/12 - Amsterdam, Netherlands - Melkweg Oude Zaal Fri. 07/13 - Copenhagen, Denmark - Hafnia Zoo
Views: 116119694 Bhad Bhabie
Kitbull | Pixar SparkShorts
Kitbull, directed by Rosana Sullivan and produced by Kathryn Hendrickson, reveals an unlikely connection that sparks between two creatures: a fiercely independent stray kitten and a pit bull. Together, they experience friendship for the first time. More #SparkShorts are coming to Disney+ in 2019. Sign up for updates at http://disneyplus.com Meet the filmmakers behind Kitbull: https://youtu.be/7Nj8tNjs074 See how the film was made: https://youtu.be/I6AMdsH0-uo Facebook: https://www.facebook.com/Pixar Instagram: https://www.instagram.com/pixar/ Twitter: https://twitter.com/Pixar Copyright: (C) Disney•Pixar
Views: 25969461 Pixar
Variable Importance using Target Shuffling
This is the recording of Dean Abbott's talk at KNIME Summit 2016 with title "Variable Importance using Target Shuffling". Slides available at https://www.knime.org/files/summit2016/slides/Abbott--Variable%20Importance%20using%20Randomization_FINAL.pdf
Views: 1319 KNIMETV
support vector machine (SVM) - Rattle R language Machine learning
Predicting Customer churn in telecom industry using a machine learning algorithm called support vector machine (SVM) in R programming language using GUI called Rattle. 5:53 skip intro for support vector machines. 7:12 customer churn Data info 12:14 R, R studio , Rattle Quick install info
Views: 669 Brandon Macaulay
Nicki Minaj - Barbie Dreams
Barbie Dreams (Official Video) Stream / Download Album “Queen” Here: https://nickiminaj.lnk.to/queenYD Connect with Nicki: https://www.instagram.com/nickiminaj https://twitter.com/NICKIMINAJ https://www.facebook.com/nickiminaj/ https://www.mypinkfriday.com/ Video Director: Hype Williams Video Producers: Hype Williams & Keith Brown Video Editor: Eric Hughes for HW Worldwide Music video by Nicki Minaj performing Barbie Dreams. © 2018 Young Money/Cash Money Records http://vevo.ly/aB1cvS
Views: 88838219 NickiMinajAtVEVO
3 Horrifying Cases Of Ghosts And Demons
Can ghosts and demons harm the living? Check out more awesome BuzzFeedBlue videos! http://bit.ly/YTbuzzfeedblue1 GET MORE BUZZFEED www.buzzfeed.com/videoteam www.facebook.com/buzzfeedvideo www.instagram.com/buzzfeedvideo www.buzzfeed.com/video www.youtube.com/buzzfeedvideo www.youtube.com/buzzfeedyellow www.youtube.com/buzzfeedblue www.youtube.com/buzzfeedviolet BUZZFEED BLUE Bite-size knowledge for a big world from the BuzzFeed crew. New facts, hacks, and how-to videos posted daily! Subscribe to BuzzFeedBlue today! http://bit.ly/YTbuzzfeedblue1
Views: 16596693 BuzzFeed Multiplayer
Imagine Dragons – Thunder (Lyrics) 🎵
"Imagine Dragons – Thunder (Lyrics) 🎵" Hit the 🔔 to join the notification squad! Support Pixl Networks http://snapchat.com/add/pixlnetworks http://instagram.com/pixlnetworks http://open.spotify.com/user/pixlnetworks http://facebook.com/pixlnetworks http://twitter.com/pixlnetworks http://soundcloud.com/pixlnetworks http://discord.gg/pixlnetworks Support Imagine Dragons http://facebook.com/imaginedragons http://twitter.com/imaginedragons Artwork by t1na http://t1na.deviantart.com/art/The-Guardian-642792323 Thunder (Lyrics) Imagine Dragons: [Verse 1] Just a young gun with a quick fuse I was uptight, wanna let loose I was dreaming of bigger things in Wanna leave my own life behind Not a yes sir, not a follower Fit the box, fit the mold Have a seat in the foyer, take a number I was lightning before the thunder [Chorus] Thunder, feel the thunder Lightning and the thunder Thunder, feel the thunder Lightning and the thunder Thunder, thunder Thunder [Verse 2] Kids were laughing in my classes While I was scheming for the masses Who do you think you are Dreaming 'bout being a big star? You say you're basic, you say you're easy You're always riding in the back seat Now I'm smiling from the stage While you were clapping in the nose bleeds [Chorus] Thunder, feel the thunder Lightning and the thunder Thunder, feel the thunder Lightning and the thunder Thunder [Bridge] Thunder, feel the thunder Lightning and the thunder, thunder [Chorus] Thunder, feel the thunder Lightning and the thunder, thunder Thunder, feel the thunder Lightning and the thunder, thunder Thunder, feel the thunder Lightning and the thunder, thunder Thunder, feel the thunder Lightning and the thunder, thunder Pixl Networks is an independent music network showcasing high-quality music from major labels as well as rising artists. Subscribe to this channel to be updated with the latest songs.
Views: 52959174 Pixl Networks
Bazzi – Mine (Lyrics) 🎵
"Bazzi – Mine (Lyrics) 🎵" Hit the 🔔 to join the notification squad! Support Pixl Networks http://snapchat.com/add/pixlnetworks http://instagram.com/pixlnetworks http://open.spotify.com/user/pixlnetworks http://facebook.com/pixlnetworks http://twitter.com/pixlnetworks http://soundcloud.com/pixlnetworks http://discord.gg/pixlnetworks Support Bazzi http://instagram.com/bazzi http://facebook.com/bazziworldwide http://twitter.com/bazzi Hashtags: #mine #bazzi #music Pixl Networks is YouTube's leading music promotion network for popular music. Working with the biggest record labels in the world, we keep you updated with your favorite artists and provide you with the lyrics of their latest songs. Subscribe to this channel to stay on top of the hottest music trends!
Views: 82511706 Pixl Networks
sentiment analysis
-- Created using PowToon -- Free sign up at http://www.powtoon.com/youtube/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 100 Ori Ernst
Automated Analytics - Perform a frequent path analysis: SAP Predictive Analytcs
Frequent Path analysis enables the visual analysis of sequences of events that occur within a defined time period. In this tutorial, we will visualize the paths a group of individuals in New York City.
How to Export and Import AutoSurf Play Lists.
How to Export and Import AutoSurf Play Lists. Click on the Setup button and a floating window will pop up. Click on the Manage Play List button to expand the controls. Click on Export Links. A plain text and encrypted play list will be generated. Play lists can be saved as text files and e-mailed to friends. To Auto Play another play list, paste it into the black box and click on Import Links (Firefox users click twice if encrypted). Set the timer and click on the Play button. AutoSurf Play Lists.
Views: 83 CleanBlackRaiser
Kygo, Miguel - Remind Me to Forget
https://lnk.to/KYGOrmtfg Enjoy more videos by Kygo https://bit.ly/2HNCZPB https://bit.ly/2FGSqmW https://bit.ly/1LYWkDn Follow Kygo on: http://www.soundcloud.com/kygo  https://www.facebook.com/kygoofficial/  http://www.instagram.com/kygomusic #Kygo #RemindMeToForget #vevo #electronic #vevoofficial
Views: 59103721 KygoOfficialVEVO

Lets fuck my wike