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Lecture 36 — Mining Data Streams | Mining of Massive Datasets | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
How to Integrate Natural Language Processing and Elasticsearch for Better Analytics - Tech Talks
 
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To learn more, visit: https://aws.amazon.com/comprehend/ There’s a proliferation of unstructured data. Companies collect massive amounts of newsfeed, emails, social media, and other text-based information to get to know their customers better or to comply with regulations. However, most of this data is unused and untouched. Natural language processing (NLP) holds the key to unlocking business value within these huge datasets, by turning free text into data that can be analyzed and acted upon. Join this tech talk and learn how you can get started mining text data effectively and extracting the rich insights it can bring. In this tech talk, you’ll learn how to process, analyze and visualize data by pairing Amazon Comprehend with Amazon Elasticsearch. Learn how you can boost search results, create rich filtering, and develop social media analytics dashboard. Learning Objectives: - Get an introduction to Amazon Comprehend, a natural language processing service from AWS - Understand how to use a natural language processing with Elasticsearch - Learn how to build customer feedback and social media analytics dashboards and how to boost rankings of the search results and build rich filtering
Views: 1597 AWS Online Tech Talks
Lecture 43 — Collaborative Filtering | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Big Data Clustering: A MapReduce Implementation of Hierarchical Affinity Propagation
 
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This project allows users to effectively perform a hierarchical clustering algorithm over extremely large datasets. The research team developed a distributed software system which reads in data from multiple input sources using a common interface, clusters the data according to a user-defined similarity metric, and represents the extracted clusters to the user in an interactive, web-based visualization. In order to deal with large "Big Data" datasets, the team derived and implemented a distributed version of the Hierarchical Affinity Propagation (HAP) clustering algorithm using the MapReduce framework. This parallelization allows the algorithm to run in best-case linear time on any cardinality dataset. It also allows execution of the software within a scalable cloud-computing framework such as Amazon's Elastic Compute Cloud (EC2).
Views: 4590 FIT ICELAB
Visual Analysis of Data
 
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CE502 Fall 2017
Views: 89 Dragos Andrei
Data Science - AntenaDev #02
 
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Data Science - AntenaDev #02 Publicado em 04/12/2017 Antenados e Antenadas! Bem vindos ao segundo episódio da trilha principal do Canal AntenaDev!!!! Apresentamos neste episódio uma lendária entrevista com três renomados Cientistas de Dados. Conheça todas os detalhes dessa área que vem revolucionando a tecnologia e a vida dos seres humanos. Prepare-se!!! Sua vida já está sendo transformada pela Ciência de Dados (ou Data Science, em inglês). Descubra o que é Data Science, seus principais conceitos, suas aplicações, as oportunidades que estão surgindo, como o mundo está sendo transformado por ela e como se tornar um Cientistas dos Dados. Participantes: Hosts: - Prof. José Maria Monteiro, um host que tenta ser politicamente correto. Mas, só tenta mesmo. - Prof. Marcelo Gonçalves, o DevMan que ficou sem energia elétrica... Entrevistados: - Aderson Olivera - http://aderson.com - Igo Brilhante - https://www.wanderpaths.com/ - Tales Matos - www.arida.ufc.b - Nauber Gois - innovanti.io/workshopdatascience Links do episódio: - Workshop de Data Science: innovanti.io/workshopdatascience - Kaggle: www kaggle.com - Coursera: www.coursera.com - Certificação de DataScience (CAP): https://www.certifiedanalytics.org/ - Wanderpaths: https://www.wanderpaths.com - Wanderpaths no GooglePlay: https://play.google.com/store/apps/details?id=com.wanderpaths.app - Wanderpaths no Apple Store: https://itunes.apple.com/br/app/wanderpaths/id1147166365?mt=8 - Wanderpaths no Instagram: @wanderpathsapp - Wanderpaths no Facebook: @wanderpaths - Canal do Nauber: https://www.youtube.com/channel/UCZctB98Gn7af-3OpGC-7-Sg - Como encontrar o Igo Brilhante: @igobrilhante - Visualização de Dados: https://uber.github.io/deck.gl/#/ https://d3js.org/ - Livros sobre Data Science: https://www.amazon.com/Data-Mining-Concepts-Techniques-Management/dp/0123814790 https://www.amazon.com/Mining-Social-Web-Facebook-LinkedIn/dp/1449367615/ref=sr_1_1?s=home-garden&ie=UTF8&qid=1512041753&sr=8-1&keywords=mining+web+python https://www.amazon.com/Mining-Massive-Datasets-Jure-Leskovec/dp/1107077230/ref=sr_1_1?ie=UTF8&qid=1512041778&sr=8-1&keywords=mining+of+massive+datasets - Datasets Interessantes: https://elitedatascience.com/datasets - Plataformas https://databricks.com/ https://databricks.com/blog/2016/01/25/deep-learning-with-apache-spark-and-tensorflow.html https://hortonworks.com/ https://www.cloudera.com/ - Diary of a data scientist at Booking.com (Sou fã da Booking) https://towardsdatascience.com/diary-of-a-data-scientist-at-booking-com-924734c71417 Produção e conteúdo: • AntenaDev Inovação Edição e sonorização:  • AntenaDev Inovação Redes Sociais: • Blog: www.antenadev.com.br • Facebook: www.facebook.com/AntenaDev • Instagram: @AntenaDev – http://www.instagram.com/AntenaDev • Twitter: @AntenaDev – www.twitter.com/AntenaDev Agradecimentos: • https://www.freesound.org/ • https://www.pond5.com • http://br.freepik.com/ • https://pixabay.com/ • https://www.pexels.com/ • http://audiomicro.com/royalty-free-music Categoria • Educação Licença • Licença padrão do YouTube
Views: 199 AntenaDev
Mining Competitors from Large Unstructured Datasets
 
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Mining Competitors from Large Unstructured Datasets To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains.
Views: 1650 jpinfotechprojects
Apache Spark Tutorial for Beginners Part 3 - Resilient Distributed Dataset - Frank Kane
 
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Explore the full course on Udemy (special discount included in the link): https://www.udemy.com/the-ultimate-hands-on-hadoop-tame-your-big-data/?couponCode=HADOOPUYT Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment. In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets. You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it. In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.
Views: 2696 Udemy Tech
Introduction to recommendation systems
 
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The video is based on the online resources and the book "Mining of Massive Datasets"
Views: 35 Yayati Gupta
Concept Drift Detector in Data Stream Mining
 
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Jorge Casillas, Shuo Wang, Xin Yao, Concept Drift Detection in Histogram-Based Straightforward Data Stream Classification, 6th International Workshop on Data Science and Big Data Analytics, IEEE International Conference on Data Mining, November 17-20, 2018 - Singapore http://decsai.ugr.es/~casillas/downloads/papers/casillas-ci44-icdm18.pdf This presentation shows a novel algorithm to accurately detect changes in non-stationary data streams in a very efficiently way. If you want to know how the yacare caiman, the cheetah and the racer snake are related to this research, do not stop watching the video! More videos here: http://decsai.ugr.es/~casillas/videos.html
Views: 145 Jorge Casillas
Using Data to Make Big Money on Amazon w/ Greg Mercer
 
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http://www.fanaticsmedia.com/amazon Fanatics eCommerce TV Episode 4 - Justin Simon interviews Greg Mercer What is the best way to identify products to sell on Amazon? What apps must all Amazon sellers use? How do you get great reviews for Amazon products? How do you optimize your page listings? What kinds of things can you sell on Amazon? Amazon best seller and eCommerce software genius Greg Mercer answers all of those questions on this week's edition of the Amazon Fanatics Program Podcast, where we speak to the biggest sellers, most influential names and get you the best tips and tricks to find success on Amazon using Seller Central and the Fulfilled By Amazon (FBA) program. Mercer is a software savant, the man behind the top rated product data tool http://www.JungleScout.com, the highly regarded review site http://www.ReviewKick.com and the brilliant marketing A/B tester for your listings http://www.Splitly.com. (Get the JungleScout Chrome extension here: http://bit.ly/2aZPiJw) Widely considered one of the foremost eCommerce experts in the world, Mercer shows you how to take a product from concept to reality -- just make sure to avoid the products he stays away from! For more, visit FanaticsMedia.com. NEW EPISODES EVERY THURSDAY AT 11AM Do you need JungleScout? http://www.fanaticsmedia.com/amazon-product-checklist How does your Amazon page grade? http://www.fanaticsmedia.com/amazon Do you want to learn how to change your life and start your own Amazon or eCommerce business? http://www.fanaticsmedia.com/AmazonTraining ****************************************************** Want to hire us? We create digital marketing campaigns that drive revenue and massive awareness for your company. http://www.fanaticsmedia.com ******************************************************* Find us on Social Media Twitter: http://www.twitter.com/justinsimon LinkedIn: https://www.linkedin.com/in/justinsimon Websites http://www.fanaticsmedia.com (digital marketing and ecommerce http://www.evolvesinc.com for influencer marketing Instagram: http://www.instagram.com/markfidelman Community Link http://www.fanaticsmedia.com/fandamonium -~-~~-~~~-~~-~- Don't miss this one! "Ep 7: How to Get 70 Million Views on Facebook" https://www.youtube.com/watch?v=fkpZ6r-MKt4 -~-~~-~~~-~~-~-
Views: 1055 Fanatics Media
Apache Spark Tutorial for Beginners Part 7 - Using MLLib - Frank Kane
 
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Explore the full course on Udemy (special discount included in the link): https://www.udemy.com/the-ultimate-hands-on-hadoop-tame-your-big-data/?couponCode=HADOOPUYT Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment. In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets. You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it. In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.
Views: 1052 Udemy Tech
frequent itemset mining using map reduce framework
 
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This project is for identifying the Frequent Itemset mining of amazon datasets using mapreduce framework
Apache Spark Tutorial for Beginners Part 5 - Spark SQL - Frank Kane
 
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Explore the full course on Udemy (special discount included in the link): https://www.udemy.com/the-ultimate-hands-on-hadoop-tame-your-big-data/?couponCode=HADOOPUYT Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment. In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets. You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it. In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.
Views: 1274 Udemy Tech
Taming Big Data with Apache Spark and Python - Hands On!
 
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get this course from here: http://cuon.io/R1x6p New! Updated for Spark 2.0.0 “Big data" analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You'll learn those same techniques, using your own Windows system right at home. It's easier than you might think. Learn and master the art of framing data analysis problems as Spark problems through over 15 hands-on examples, and then scale them up to run on cloud computing services in this course. You'll be learning from an ex-engineer and senior manager from Amazon and IMDb. Learn the concepts of Spark's Resilient Distributed Datastores Develop and run Spark jobs quickly using Python Translate complex analysis problems into iterative or multi-stage Spark scripts Scale up to larger data sets using Amazon's Elastic MapReduce service Understand how Hadoop YARN distributes Spark across computing clusters Learn about other Spark technologies, like Spark SQL, Spark Streaming, and GraphX By the end of this course, you'll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes. This course uses the familiar Python programming language; if you'd rather use Scala to get the best performance out of Spark, see my "Apache Spark with Scala - Hands On with Big Data" course instead. We'll have some fun along the way. You'll get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you've got the basics under your belt, we'll move to some more complex and interesting tasks. We'll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We'll analyze a social graph of superheroes, and learn who the most “popular" superhero is – and develop a system to find “degrees of separation" between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You'll find the answer. This course is very hands-on; you'll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon's Elastic MapReduce service. 5 hours of video content is included, with over 15 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX. Enjoy the course! Who is the target audience? People with some software development background who want to learn the hottest technology in big data analysis will want to check this out. This course focuses on Spark from a software development standpoint; we introduce some machine learning and data mining concepts along the way, but that's not the focus. If you want to learn how to use Spark to carve up huge datasets and extract meaning from them, then this course is for you. If you've never written a computer program or a script before, this course isn't for you - yet. I suggest starting with a Python course first, if programming is new to you. If your software development job involves, or will involve, processing large amounts of data, you need to know about Spark. If you're training for a new career in data science or big data, Spark is an important part of it.
Streaming Data
 
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Here is Brightlight Sr. Principal Consultant Frank Blau discussing the new frontier of Streaming Data and how it relates to Business Intelligence and Analytics. This video provides a basic introduction to the concepts and evolution of streaming data as the enabling technology for the future of BI.
Views: 165 BrightlightConsult
1 - Data Mining: Learning From Large Datasets (ETH Zürich, Fall 2017)
 
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Professor Andreas Krause and Dr.Kfir Levy lectures on Data Mining. Topics covered: - Dealing with large data (Data centers; Map-Reduce/Hadoop; Amazon Mechanical Turk) - Fast nearest neighbor methods (Shingling, locality sensitive hashing) - Online learning (Online optimization and regret minimization, online convex programming, applications to large-scale Support Vector Machines) - Multi-armed bandits (exploration-exploitation tradeoffs, applications to online advertising and relevance feedback) - Active learning (uncertainty sampling, pool-based methods, label complexity) - Dimension reduction (random projections, nonlinear methods) - Data streams (Sketches, coresets, applications to online clustering) - Recommender systems
Views: 278 Open ETH
Building Your Data Lake on AWS
 
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Learn more about Big Data on AWS at - https://amzn.to/2MOMLPA. A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.
Views: 4318 Amazon Web Services
Mining for Data
 
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The Search for Information on Groundwater Conditions and Use in the Colorado River Border Region. Mike Cohen, Senior Research Associate, the Pacific Institute
Views: 39 CRLdotEDU
AWS re:Invent 2016: Data Polygamy: Relationships among Urban Spatio-Temporal Datasets (WWPS401)
 
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In this session, learn how Data Polygamy, a scalable topology-based framework, can enable users to query for statistically significant relationships between spatio-temporal datasets. With the increasing ability to collect data from urban environments and a push toward openness by governments, we can analyze numerous spatio-temporal datasets covering diverse aspects of a city. Urban data captures the behavior of the city’s citizens, existing infrastructure (physical and policies), and environment over space and time. Discovering relationships between these datasets can produce new insights by enabling domain experts to not only test but also generate hypotheses. However, discovery is difficult. A relationship between two datasets can occur only at locations or time periods that behave differently compared to the regions’ neighborhood. The size and number of datasets and diverse spatial and temporal scales at which the data is available presents computational challenges. Finally, of several thousand possible relationships, only a small fraction is actually informative. We have implemented the framework on Amazon EMR and show through an experimental evaluation using over 300 spatial-temporal urban datasets how our approach is scalable and effective at identifying significant relationships. Find details about the work at http://dl.acm.org/citation.cfm?id=2915245. The code and experiments are available at https://github.com/ViDA-NYU/data-polygamy.
Views: 423 Amazon Web Services
Neuromation - the knowledge mining era
 
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Cryptonomos will hold a token sale for Neuromation that is creating a distributed platform of synthetic data ecosystem. The enormous computing capacity that will become available on the platform will be game-changing for wide AI adoption by the Enterprise. Neuromation is creating a platform to allow users to create dataset generators, generate massive datasets, train deep learning models. Users will also be able to trade datasets and models in the platform marketplace. Neuromation engages crypto-currency miners in computationally intensive tasks of data generation and model training. By performing these tasks they will be mining Neuromation Tokens. Tokens for the Neuromation platform are already available for purchase on pre-sale via Cryptonomos. Buy tokens: https://neuromation.cryptonomos.com Telegram RU: https://t.me/icocryptonomosrus Telegram EN: https://t.me/Cryptonomos_ICOs
Views: 3198 Cryptonomos Platform
One-Pass Ranking Models for Low-Latency Product Recommendations
 
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Authors: Antonino Freno, Martin Saveski, Rodolphe Jenatton, Cedric Archambeau Abstract: Purchase logs collected in e-commerce platforms provide rich information about customer preferences. These logs can be leveraged to improve the quality of product recommendations by feeding them to machine-learned ranking models. However, a variety of deployment constraints limit the naive applicability of machine learning to this problem. First, the amount and the dimensionality of the data make in-memory learning simply not possible. Second, the drift of customers' preference over time require to retrain the ranking model regularly with freshly collected data. This limits the time that is available for training to prohibitively short intervals. Third, ranking in real-time is necessary whenever the query complexity prevents us from caching the predictions. This constraint requires to minimize prediction time (or equivalently maximize the data throughput), which in turn may prevent us from achieving the accuracy necessary in web-scale industrial applications. In this paper, we investigate how the practical challenges faced in this setting can be tackled via an online learning to rank approach. Sparse models will be the key to reduce prediction latency, whereas one-pass stochastic optimization will minimize the training time and restrict the memory footprint. Interestingly, and perhaps surprisingly, extensive experiments show that one-pass learning preserves most of the predictive performance. Additionally, we study a variety of online learning algorithms that enforce sparsity and provide insights to help the practitioner make an informed decision about which approach to pick. We report results on a massive purchase log dataset from the Amazon retail website, as well as on several benchmarks from the LETOR corpus. ACM DL: http://dl.acm.org/citation.cfm?id=2788579 DOI: http://dx.doi.org/10.1145/2783258.2788579
Lecture 44 — Implementing Collaborative Filtering (Advanced) | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Making Movie Recommendations with Item-Based Collaborative Filtering
 
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Complete course: https://www.udemy.com/building-recommender-systems-with-machine-learning-and-ai/?couponCode=RECSYS15 Learn how to design, build, and scale recommender systems from Frank Kane, who led teams building them at Amazon.com for 9 years. In this excerpt from "Building Recommender Systems with Machine Learning and AI," Frank covers the item-based collaborative filtering technique used by Amazon to produce product recommendations at massive scale. Well, at least the parts he can legally tell you about! The idea is simple, but it works remarkably well: find the items a given individual has expressed interest in through their purchases, ratings, or other activity - and recommend the items most similar to those items. Measuring item similarity for every possible pair of items is the key issue. Metrics such as cosine similarity, pearson correlation, or Jaccard similarity may be used.
Machine Learning #73 - Support Vector Machines #4 - Soft Margin
 
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In diesem Tutorial behandeln wir Soft Margins. ❤❤❤ Früherer Zugang zu Tutorials, Abstimmungen, Live-Events und Downloads ❤❤❤ ❤❤❤ https://www.patreon.com/user?u=5322110 ❤❤❤ ❤❤❤ Keinen Bock auf Patreon? ❤❤❤ ❤❤❤ https://www.paypal.me/TheMorpheus ❤❤❤ 🌍 Website 🌍 https://the-morpheus.de ¯\_(ツ)_/¯ Tritt der Community bei ¯\_(ツ)_/¯ ** https://discord.gg/BnYZ8XS ** ** https://www.reddit.com/r/TheMorpheusTuts/ ** ( ͡° ͜ʖ ͡°) Mehr News? Mehr Code? ℱ https://www.facebook.com/themorpheustutorials 🐦 https://twitter.com/TheMorpheusTuts 🐙 https://github.com/TheMorpheus407/Tutorials Du bestellst bei Amazon? Bestell über mich, kostet dich null und du hilfst mir »-(¯`·.·´¯)-» http://amzn.to/2slBSgH Videowünsche? 🎁 https://docs.google.com/spreadsheets/d/1YPv8fFJOMRyyhUggK8phrx01OoYXZEovwDLdU4D4nkk/edit#gid=0 Fragen? Feedback? Schreib mir! ✉ https://www.patreon.com/user?u=5322110 ✉ https://www.facebook.com/themorpheustutorials ✉ https://discord.gg/BnYZ8XS ✉ [email protected] oder schreib einfach ein Kommentar :)
2015 CASIS Keynote: 'Learning Tools for Big Data Analytics'
 
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The Center for Advanced Signal and Image Sciences (CASIS) sponsored this keynote address by Georgios Giannakis of the University of Minnesota, entitled “Learning Tools for Big Data Analytics” on May 13, 2015, during the 19th annual CASIS Workshop at Lawrence Livermore National Laboratory. More about the 2015 CASIS Workshop: https://casis.llnl.gov/casis-2015/ Abstract: We live in an era of data deluge. Pervasive sensors collect massive amounts of information on every bit of our lives, churning out enormous streams of raw data in various formats. Mining information from unprecedented volumes of data promises to limit the spread of epidemics and diseases, identify trends in financial markets, learn the dynamics of emergent social-computational systems, and also protect critical infrastructure including the smart grid and the Internet’s backbone network. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. The sheer volume of data makes it often impossible to run analytics using a central processor and storage, and distributed processing with parallelized multi-processors is preferred while the data themselves are stored in the cloud. As many sources continuously generate data in real time, analytics must often be performed “on-the-fly” and without an opportunity to revisit past entries. Due to their disparate origins, massive datasets are noisy, incomplete, prone to outliers, and vulnerable to cyber-attacks. These effects are amplified if the acquisition and transportation cost per datum is driven to a minimum. Overall, Big Data present challenges in which resources such as time, space, and energy, are intertwined in complex ways with data resources. Given these challenges, ample signal processing opportunities arise. This keynote lecture outlines ongoing research in novel models applicable to a wide range of Big Data analytics problems, as well as algorithms to handle the practical challenges, while revealing fundamental limits and insights on the mathematical trade-offs involved.
Apache Spark with Scala :  Learn Spark from a Big Data Guru | BEST SELLER
 
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This course covers all the fundamentals about Apache Spark with Scala and teaches you everything you need to know about developing Apache Spark applications with Scala Spark. At the end of this course, you will gain in-depth knowledge about Apache Spark Scala and general big data analysis and manipulations skills to help your company to adapt Apache Scala Spark for building big data processing pipeline and data analytics applications. This course covers 10+ hands-on big data examples involving Apache Spark. You will learn valuable knowledge about how to frame data analysis problems as Scala Spark problems. Together we will learn examples such as aggregating NASA Apache web logs from different sources; we will explore the price trend by looking at the real estate data in California; we will write Scala Spark applications to find out the median salary of developers in different countries through the Stack Overflow survey data; we will develop a system to analyze how maker spaces are distributed across different regions in the United Kingdom. And much much more. What will you learn from this lecture: In particularly, you will learn: An overview of the architecture of Apache Spark. Develop Apache Spark 2.0 applications with Scala using RDD transformations and actions and Spark SQL. Work with Apache Spark's primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets. Deep dive into advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs. Scale up Apache Spark applications on a Hadoop YARN cluster through Amazon's Elastic MapReduce service. Analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding of Apache Spark SQL. Share information across different nodes on an Apache Spark cluster by broadcast variables and accumulators. Best practices of working with Apache Spark Scala in the field. Big data ecosystem overview. Why shall we learn Apache Spark: Apache Spark gives us unlimited ability to build cutting-edge applications. It is also one of the most compelling technologies of the last decade in terms of its disruption to the big data world. Apache Scala Spark provides in-memory cluster computing which greatly boosts the speed of iterative algorithms and interactive data mining tasks. Apache Spark is the next-generation processing engine for big data. Tons of companies are adapting Apache Spark to extract meaning from massive data sets, today you have access to that same big data technology right on your desktop. Apache Spark is becoming a must tool for big data engineers and data scientists. What programing language is this course taught in? This course is taught in Scala. Scala is the next generation programming language for functional programing that is growing in popularity and it is one of the most widely used languages in the industry to write Apache Spark programs. Let's learn how to write Apache Spark programs with Scala to model big data problem today! 30-day Money-back Guarantee! You will get 30-day money-back guarantee from Udemy for this course. If not satisfied with Apache Spark course, simply ask for a refund within 30 days. You will get a full refund. No questions whatsoever asked. Are you ready to take your big data analysis skills and career to the next level, take this course now! You will go from zero to Apache Spark hero in 4 hours. Course Link : http://bit.ly/2DKjsZD Google Searching Text: spark with scala tutorial,scala for spark pdf,apache spark tutorial,spark with scala book,spark scala example,spark scala wiki,spark tutorials with scala the beginner's guide pdf,what is spark,
Streaming analytics basics for Python developers Course
 
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https://developer.ibm.com/courses/
Views: 2736 TheOnDemandDemoGuy
xStream: Outlier Detection in Feature-Evolving Data Streams
 
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Authors: Emaad Manzoor (CMU), Hemank Lamba (CMU), Leman Akoglu (CMU) Abstract: This work addresses the outlier detection problem for feature-evolving streams, which has not been studied before. In this setting both (1) data points may evolve, with feature values changing, as well as (2) feature space may evolve, with newly-emerging features over time. This is notably different from row-streams, where points with fixed features arrive one at a time. We propose a density-based ensemble outlier detector, called xStream, for this more extreme streaming setting which has the following key properties: (1) it is a constant-space and constant-time (per incoming update) algorithm, (2) it measures outlierness at multiple scales or granularities, it can handle (3i) high-dimensionality through distance-preserving projections, and (3ii) non-stationarity via O(1)-time model updates as the stream progresses. In addition, xStream can address the outlier detection problem for the (less general) disk-resident static as well as row-streaming settings. We evaluate xStream rigorously on numerous real-life datasets in all three settings: static, row-stream, and feature-evolving stream. Experiments under static and row-streaming scenarios show that xStream is as competitive as state-of-the-art detectors and particularly effective in high-dimensions with noise. We also demonstrate that our solution is fast and accurate with modest space overhead for evolving streams, on which there exists no competition. More on http://www.kdd.org/kdd2018/
Views: 319 KDD2018 video
What is Unsupervised Learning? Netflix User Recommendations using Artificial Intelligence
 
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Artificial Intelligence For Everyone: Episode #5 What is Unsupervised Learning? Learn the basics of Unsupervised Learning and how Netflix can use Unsupervised Learning for User Recommendations and the Netflix Suggestion Algorithm of movies and series. In this tutorial we discuss how unsupervised learning can find structure in unlabelled data, in order to understand data clustering. This artificial intelligence tutorial is for anyone interested in AI and programming, who wants to understand how AI could be used for data clustering. Artificial Intelligence and Machine Learning shapes the world around us more than ever, and understanding the basic concepts is an useful asset for any person, regardless of their walk in life or profession. www.kodkompis.com PEACE! ---------------------------------------------------------------------------------------------- JOIN NO PMO NATION 👬: ---------------------------------------------------------------------------------------------- 👬 Instagram: https://www.instagram.com/nopmonation/ ---------------------------------------------------------------------------------------------- JOIN THE ARMY OF HAPPIER AND STRONGER PEOPLE 👬: ---------------------------------------------------------------------------------------------- 🎓 SUBSCRIBE ON YOUTUBE: https://goo.gl/JDWLKZ 🎓 JOIN US ON SLACK: https://goo.gl/srBTka 🎓 JOIN MY EXCLUSIVE MAILING LIST: http://eepurl.com/di4dNj ---------------------------------------------------------------------------------------------- POPULAR EDUCATION SERIES 💝: ---------------------------------------------------------------------------------------------- 🎓 MASTER NOFAP: https://goo.gl/z6E6HU 🎓 BECOME HAPPIER: https://goo.gl/DZ4cps 🎓 ATTRACT WOMEN: https://goo.gl/MKxdeS 🎓 MACHINE LEARNING: https://goo.gl/hULpKQ 🎓 ARTIFICIAL INTELLIGENCE: https://goo.gl/pzCWpU ---------------------------------------------------------------------------------------------- HOW TO ASK OSCAR QUESTIONS 🎤: ---------------------------------------------------------------------------------------------- 👬 MESSAGE ME ON INSTAGRAM: https://www.instagram.com/oscaralsing/ 👬 ASK ME ON SLACK: https://goo.gl/srBTka Linkedin: https://www.linkedin.com/in/oscaralsing/ Facebook: https://www.facebook.com/oscaralsingcom Website: http://www.oscaralsing.com ---------------------------------------------------------------------------------------------- PRODUCTS I LOVE ❤️: ---------------------------------------------------------------------------------------------- LIFE-CHANGING BOOKS: https://goo.gl/MMH4XG MY CAMERA/PROGRAMMING GEAR: https://goo.gl/WPCkZr ---------------------------------------------------------------------------------------------- ABOUT OSCAR 💝: ---------------------------------------------------------------------------------------------- Oscar is a leader, educator and programmer specialised in Artificial Intelligence and Machine Learning who strives to build a world where all leadership spawns from an intrinsic compassion for others. He is heavily interest in mindfulness and meditation and is a daily Brazilian Jiu-Jitsu practitioner. Furthermore, he Loves lifting heavy things and reads a lot of books and believes in a world where compassion and mutual understanding and respect permeate all of our actions. 🎉 Leader of the Year (2017, All Swedish Students) 🎉 10/100 @ Sweden's Top Future 100 Leaders 2018 🎉 37/100 @ Sweden's Top Future 100 Leaders 2017
Views: 1178 Oscar Alsing
GOTO 2015 • How Go is Making us Faster • Wilfried Schobeiri
 
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This presentation was recorded at GOTO Chicago 2015 http://gotochgo.com Wilfried Schobeiri - SVP of Technology, MediaMath ABSTRACT In a web-performance world, things have to go fast. In less than the blink of an eye, our digital marketing systems host real-time auctions and serve ads across the world to the tune of 2.3 million queries per second. And we are building the next generation of these real-time, high performance systems on Go. In this talk, we’ll dive [...] Download slides and read the full abstract here: http://gotocon.com/chicago-2015/presentation/How%20Go%20is%20making%20us%20faster https://twitter.com/gotochgo https://www.facebook.com/GOTOConference http://gotocon.com
Views: 1603 GOTO Conferences
Applying Geospatial Analytics at a Massive Scale using Kafka, Spark and Elasticsearch on DC/OS
 
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Applying Geospatial Analytics at a Massive Scale using Kafka, Spark and Elasticsearch on DC/OS - Adam Mollenkopf, Esri This session will explore how DC/OS and Mesos are being used at Esri to establish a foundational operating environment to enable the consumption of high velocity IoT data using Apache Kafka, streaming analytics using Apache Spark, high-volume storage and querying of spatiotemporal data using Elasticsearch, and recurring batch analytics using Apache Spark & Metronome. Additionally, Esri will share their experience in making their application for DC/OS portable so that it can easily be deployed amongst public cloud providers (Microsoft Azure, Amazon EC2), private cloud providers and on-premise environments. Demonstrations will be performed throughout the presentation to cement these concepts for the attendees. About Adam Mollenkopf Esri Real-Time & Big Data GIS Capability Lead Redlands, CA Twitter Tweet Websiteesri.com Adam Mollenkopf is responsible for the strategic direction Esri takes towards enabling real-time and big data capabilities in the ArcGIS platform. This includes having the ability to ingest real-time data streams from a wide variety of sources, performing continuous and recurring spatiotemporal analytics on data as it is received & disseminating analytic results to communities of interest. He leads a team of experienced individuals in the area of stream processing and big data analytics.
Views: 1516 The Linux Foundation
AWS re:Invent 2016: Fanatics: Deploying Scalable, Self-Service Business Intelligence on AWS (BDA207)
 
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Data is growing at a quantum scale and one of challenges you face is to enable your users to analyze all this data, extract timely insights from it, and visualize it. In this session, you learn about business intelligence solutions available on AWS. We discuss best practices for deploying a scalable and self-serve BI platform capable of churning through large datasets. Fanatics, the nation’s largest online seller of licensed sports apparel, talks about their experience building a globally distributed BI platform on AWS, that delivers massive volumes of reports, dashboards, and charts on a daily basis to an ever growing user base. Fanatics shares the architecture of their data platform, built using Amazon Redshift, Amazon S3, and open source frameworks like Presto and Spark. They talk in detail about their BI platform including Tableau, Microstrategy, and other tools on AWS to make it easy for their analysts to perform ad-hoc analysis and get real-time updates, alerts, and visualizations. You also learn about the experimentation-based approach that Fanatics adopted to fully engage their business intelligence community and make optimal use of their BI platform resources on AWS.
Views: 1070 Amazon Web Services
Christopher Roach - MapReduce: 0-60 in 40 minutes
 
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PyData SV 2014 TUTORIAL - In 2004, at the Sixth Symposium on Operating System Design and Implementation, Jeffrey Dean and Sanjay Ghemawat, a couple of engineers working for Google, published a paper titled "MapReduce: Simplified Data Processing on Large Clusters" that introduced the world to a simple, yet powerful heuristic for processing large amounts of data at previously unheard of scales. Though the concepts were not new---map and reduce had existed for quite some time in functional programming languages---the observation that they could be used as a general programming paradigm for solving large data processing problems changed the current state of the art. The goal of the tutorial is to give attendees a basic working knowledge of what MapReduce is, and how it can be used to process massive sets of data relatively quickly. We will walk through the basics of what MapReduce is and how it works. Though there are a handful of MapReduce implementations out there to choose from, Hadoop is without a doubt the most well known and, as such, we will take a look at how to use it to run our MapReduce jobs. With that in mind, we will discuss what you need to know to use Hadoop and take a look at how to write our own Hadoop jobs in Python using the Hadoop Streaming utility. Finally, we'll look at a library created at Yelp called MRJob that can make writing Hadoop jobs in Python much easier. By the end of the tutorial an attendee with little to no knowledge of MapReduce, but a working knowledge of Python, should be able to write their own basic MapReduce tasks for Hadoop and run them on a cluster of machines using Amazon's Elastic MapReduce service.
Views: 6981 PyData
Using PySpark and MlLib - PyDataSG
 
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Speaker: Juliet Hougland (@j_houg) Abstract: Spark MLlib is a library for performing machine learning and associated tasks on massive datasets. With MLlib, fitting a machine-learning model to a billion observations can take only a few lines of code, and leverage hundreds of machines. This talk will demonstrate how to use Spark MLlib to fit an ML model that can predict which customers of a telecommunications company are likely to stop using their service. It will cover the use of Spark's DataFrames API for fast data manipulation, as well as ML Pipelines for making the model development and refinement process easier. Juliet Hougland answers complex business problems using statistics to tame multi-terabyte datasets. Juliet's been sought after by Cloudera’s customers as a field-facing data scientist advising on which tools to use, teaching how to use them, recommending the best approach to bring together the right data to answer the business problem at hand and building production machine learning models. For many years Juliet has been a contributor in the open source community working on projects such as Apache Spark, Scalding, and Kiji. Juliet is the Head of Data Science for Engineering at Cloudera. https://www.linkedin.com/in/jhlch Event Page: https://www.meetup.com/PyData-SG/events/229711672/ Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/1CrF/
Views: 934 Engineers.SG
Data Stream Processing: Concepts and Implementations by Matthias Niehoff
 
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With data stream processing there are plenty of options. Matthias gives an overview on various concepts used in data stream processing. Most of them are used for solving problems in the field of time, focussing on processing time compared to event time. The techniques shown include the Dataflow API as it was introduced by Google and the concepts of stream and table duality. But I will also come up with other problems like data lookup and deployment of streaming applications and various strategies on solving these problems. The summary contains a brief outline on the implementation status of those strategies in the popular streaming frameworks Apache Spark Streaming, Apache Flink and Kafka Streams. Meet The Experts: Data-driven Day provides an overview of the challenges, possible solutions and technologies for data-driven applications and use cases. This talk is one of the series at codecentric's Data Driven Day. • Complete Playlist: http://bit.ly/mte-datadrivenday
Views: 779 codecentric AG
Big Data Analytics -- Intelligent Analysis & Recommendation System of Dining
 
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This is Columbia University EECSE6893 2017 fall course final project video demonstration made by Xuewei Fan, Xiaotong Qiu, and Shenxiu Wu.
Views: 36 Shenxiu Wu
Correlation with BigQuery
 
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Michael Manoochehri and Felipe Hoffa give us a look at the new and powerful correlation functions now available in Big Query. Find the full code for this demo at http://nbviewer.ipython.org/6459195.
Views: 7268 Google Developers
Yelp dataset analysis
 
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Views: 610 Vallabh Naik
Venkata Pingali – Increasing Trust and Efficiency of Data Science using dataset versioning
 
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As data science grows and matures as a domain, harder questions are being asked by decision makers about trust and efficiency of data science process. Some of them include: Lineage/Auditability: Where did the numbers come from? Reproducibility/Replicability: Is this an accident? Does it hold now? Efficiency/Automation: Can you do it faster, cheaper, better? Significant amount of data scientists’ time goes towards generating, shaping, and using datasets. It is laborious and error prone. In this talk, we introduce an open source tool, dgit - git wrapper to manage dataset versions, and discuss why dgit was developed, and how we can redo the data science process using dgit.
Views: 238 HasGeek TV
Analyzing Big Data in less time with Google BigQuery
 
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Most experienced data analysts and programmers already have the skills to get started. BigQuery is fully managed and lets you search through terabytes of data in seconds. It’s also cost effective: you can store gigabytes, terabytes, or even petabytes of data with no upfront payment, no administrative costs, and no licensing fees. In this webinar, we will: - Build several highly-effective analytics solutions with Google BigQuery - Provide a clear road map of BigQuery capabilities - Explain how to quickly find answers and examples online - Share how to best evaluate BigQuery for your use cases - Answer your questions about BigQuery
Views: 71885 Google Cloud Platform
Introduction to Data Streaming (C. Escoffier, G. Zamarreño)
 
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Dealing with real-time, in-memory, streaming data is a unique challenge and with the advent of the smartphone and IoT (trillions of internet connected devices), we are witnessing an exponential growth in data at scale. Learning how to implement architectures that handle real-time streaming data, where data is flowing constantly, and combine it with analysis and instant search capabilities is key for developing robust and scalable services and applications. In this university session, we will look at how to implement an architecture like this, using reactive open source frameworks. An architecture based on the Swiss rail transport system will be used throughout the university. Technologies: Java (attendees must be comfortable with Java 8), Infinispan, Eclipse Vert.x, Apache Kafka, OpenShift.
Views: 539 Devoxx FR