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Hierarchical Clustering | Hierarchical Clustering in R |Hierarchical Clustering Example |Simplilearn
 
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This hierarchical clustering video will help you understand what is clustering, what is hierarchical clustering, how does hierarchical clustering work, what is distance measure, what is agglomerative clustering, what is divisive clustering and you will also see a demo on how to group states based on their sales using clustering method. Clustering is the method of dividing the objects into clusters which are similar between them and are dissimilar to the objects belonging to another cluster. It is used to find data clusters such that each cluster has the most closely matched data. Prototype-based clustering, hierarchical clustering and density-based clustering are the three types of clustering algorithms. Lets us discuss hierarchical clustering in this video. In simple terms, Hierarchical clustering is separating data into different groups based on some measure of similarity. Now, let us get started and understand hierarchical clustering in detail. Below topics are explained in this "Hierarchical Clustering" video: 1. What is clustering? (00:33) 2. What is hierarchical clustering (04:28) 3. How hierarchical clustering works? (05:52) 4. Distance measure ( 07:24) 5. What is agglomerative clustering (11:03) 6. What is divisive clustering ( 16:14) 7. Demo: to group states based on their sales (18:32) Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 To access the slides, check this link: https://www.slideshare.net/Simplilearn/hierarchical-clustering-hierarchical-clustering-in-r-hierarchical-clustering-example-simplilearn/Simplilearn/hierarchical-clustering-hierarchical-clustering-in-r-hierarchical-clustering-example-simplilearn Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=hierarchical-clustering-9U4h6pZw6f8&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - 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: 3938 Simplilearn
Clustering technique for conceptual clusters
 
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Clustering technique for conceptual clusters http://www.slideshare.net/esug/clustering-technique-for-conceptual-clusters
Views: 284 esugboard
Cluster Analysis for Market Segmentation (.ppt) || #004
 
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Cluster Analysis for Market Segmentation in Presentation Format.. Include information about Cluster Analysis and the relation between Cluster Analysis and Market Segmentation .. This is only for EDUCATIONAL & KNOWLEDGE purpose... :).. Hit Like if you Like the video & comment..
Views: 1025 Vishal TecHs
Sampling: Simple Random, Convenience, systematic, cluster, stratified - Statistics Help
 
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This video describes five common methods of sampling in data collection. Each has a helpful diagrammatic representation. You might like to read my blog: https://creativemaths.net/blog/
Views: 753340 Dr Nic's Maths and Stats
How SVM (Support Vector Machine) algorithm works
 
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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: 523081 Thales Sehn Körting
Building a Multi-Region Cluster at Target (Aaron Ploetz & Andrew From, Target) | C* Summit 2016
 
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Slides: https://www.slideshare.net/DataStax/building-a-multiregion-cluster-at-target-aaron-ploetz-target-cassandra-summit-2016 | Lessons learned from a year spent building a Cassandra cluster over multiple regions, data centers, and providers. Will discuss our successes and learnings on replication, operations, and application development. About the Speakers Aaron Ploetz, Lead Technical Architect, Target Andrew From, Senior Engineer, Target Aaron is a Lead Technical Architect for Target, where he coaches development teams on modeling and building applications for Cassandra. He is active in the Cassandra tags on StackOverflow, and has also contributed patches to cqlsh. Aaron holds a B.S. in Management/Computer Systems from the University of Wisconsin-Whitewater, a M.S. in Software Engineering and Database Technologies from Regis University, and is a 2x DataStax MVP for Apache Cassandra.
Views: 442 DataStax
Christian Hennig - Assessing the quality of a clustering
 
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PyData London 2016 There are many different methods for finding groups in data (cluster analysis), and on many datasets they will deliver different results. How good a clustering is for given data depends on the aim of clustering. I will present a number of methods that can be used to assess the quality of a clustering and to compare different clusterings, taking into account different aims of clustering. There are many different methods for finding groups in data (cluster analysis), and on many datasets they will deliver different results. How good a clustering is for given data depends on the aim of clustering and on the user's concept of what makes objects "belong together". I will present some approaches to assess the quality of a clustering and to compare different clusterings. Particularly, I will present some indexes that measure various desirable aspects of a clustering such as stability, separateness of clusters etc. Different aims of clustering can be taken into account by specifying which aspects are particularly relevant in the situation at hand. Slides available here: http://www.slideshare.net/PyData/christian-henning-assessing-the-quality-of-a-clustering
Views: 2600 PyData
Bart Baddeley - Measuring Similarity & Clustering Data
 
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http://www.slideshare.net/PyData/measuring-similarity-and-clustering-data-bart-baddeley Clustering data is a fundamental technique in data mining and machine learning. The basic problem can be specified as follows: "Given a set of data, partition the data into a set of groups so that each member of a given group is as similar as possible to the other members of that group and as dissimilar as possible to members of other groups". In this talk I will try to unpack some of the complexities inherent in this seemingly straightforward description. Specifically, I will discuss some of the issues involved in measuring similarity and try to provide some intuitions into the decisions that need to be made when using such metrics to cluster data.
Views: 970 PyData
Data Mining, Лекция №2
 
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Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Алгоритмы интеллектуальной обработки больших объемов данных", Лекция №2 "Задача кластеризации и ЕМ-алгоритм" Лектор - Николай Анохин Постановка задачи кластеризации. Функции расстояния. Критерии качества кластеризации. EM-алгоритм. K-means и модификации. Слайды лекции http://www.slideshare.net/Technosphere1/lecture-2-47107553 Другие лекции курса Data Mining | https://www.youtube.com/playlist?list=PLrCZzMib1e9pyyrqknouMZbIPf4l3CwUP Наш видеоканал | http://www.youtube.com/user/TPMGTU?sub_confirmation=1 Официальный сайт Технопарка | https://tech-mail.ru/ Официальный сайт Техносферы | https://sfera-mail.ru/ Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Блог на Хабре | http://habrahabr.ru/company/mailru/ #ТЕХНОПАРК #ТЕХНОСФЕРА x
Data Science - Part III -  EDA & Model Selection
 
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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.
Views: 37840 Derek Kane
Data Mining with WEKA and KNIME
 
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This video is present the Data mining GUI tools and concept. WEKA is a collection of state-of-the-art machine learning algorithms and data preprocessing tools written in Java, developed at the University of Waikato, New Zealand. KNIME stands for Konstanz Information Miner It is an Open Source Data Analytics, Reporting and Integration platform you can view the presentation on https://www.slideshare.net/pgpm64/data-mining-gui-tools-with-demo
Data Mining - Klasifikasi pada Blood Transfusion Data Set
 
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Selengkapnya... Syntax python https://gist.github.com/ddamayanti/ea9fcf41b0649aae567b0b82d5890d39 File presentasi https://www.slideshare.net/DewiDamayanti7/klasifikasi-pada-blood-transfusion-data-set Report https://www.academia.edu/38011093/Klasifikasi_pada_Blood_Transfusion_Data_Set
Views: 16 Dewi Damayanti
Data Science - Part VII -  Cluster Analysis
 
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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of clustering techniques, including K-Means, Hierarchical Clustering, and Gaussian Mixed Models. We will go through some methods of calibration and diagnostics and then apply the technique on a recognizable dataset.
Views: 17634 Derek Kane
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 110896 LearnEveryone
OneR Algorithm
 
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Walk through of a OneR (1R) Algorithm. Slides can be found at: https://www.slideshare.net/secret/pAjdEHBmTMqGZk
Views: 1878 MLCollab
Clustering + Feature Extraction on Text with H2O and Lexalytics - Seth Redmore
 
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Seth Redmore, Chief Marketing Officer at Lexalytics, Inc. H2O World 2015, Day 3 Contribute to H2O open source machine learning software https://github.com/h2oai Check out more slides on open source machine learning software at: http://www.slideshare.net/0xdata
Views: 915 H2O.ai
Data Mining, Лекция №1
 
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Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Алгоритмы интеллектуальной обработки больших объемов данных", Лекция №1 - "Задачи Data Mining" Лектор - Николай Анохин Обзор задач Data Mining. Стандартизация подхода к решению задач Data Mining. Процесс CRISP-DM. Виды данных. Кластеризация, классификация, регрессия. Понятие модели и алгоритма обучения. Слайды лекции: http://www.slideshare.net/Technosphere1/lecture-1-47107550 Другие лекции курса Data Mining | https://www.youtube.com/playlist?list=PLrCZzMib1e9pyyrqknouMZbIPf4l3CwUP Официальный сайт Технопарка | https://tech-mail.ru/ Официальный сайт Техносферы | https://sfera-mail.ru/ Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Блог на Хабре | http://habrahabr.ru/company/mailru/ #ТЕХНОПАРК #ТЕХНОСФЕРА x
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
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In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 133206 Well Academy
Extracting relevant Metrics with Spectral Clustering - Evelyn Trautmann
 
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PyData Berlin 2018 On a fast growing online platform arise numerous metrics. With increasing amount of metrics methods of exploratory data analysis are becoming more and more important. We will show how recognition of similar metrics and clustering can make monitoring feasible and provide a better understanding of their mutual dependencies. Slides: https://www.slideshare.net/PyData/extracting-relevant-metrics-with-spectral-clustering-evelyn-trautmann --- 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: 582 PyData
Data Science - Part VI - Market Basket and Product Recommendation Engines
 
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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of association analysis, which includes topics such as market basket analysis and product recommendation engines. The first practical example centers around analyzing supermarket retailer product receipts and the second example touches upon the use of the association rules in the political arena.
Views: 32137 Derek Kane
Curso de C++ - Aula 95 - Agrupamento (Clustering) - K-Means
 
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Slides: http://pt.slideshare.net/mcastrosouza/agrupamento-clustering-kmeans Código: https://github.com/marcoscastro/kmeans
Views: 3520 Marcos Castro
Comparing Classification and Regression
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 9896 Udacity
Time Series with Driverless AI - Marios Michailidis and Mathias Müller - H2O AI World London 2018
 
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This video was recorded in London on October 30th, 2018. Slides from the video can be viewed here: https://www.slideshare.net/0xdata/time-series-with-driverless-ai-marios-michailidis-and-mathias-mller-h2o-ai-world-london-2018 Time series is a unique field in predictive modelling where standard feature engineering techniques and models are employed to get the most accurate results. In this session we will examine some of the most important features of Driverless AI’s newest recipe regarding Time Series. It will cover validation strategies, feature engineering, feature selection and modelling. The capabilities will be showcased through several cases. Bio: Marios Michailidis is now a Competitive Data Scientist at H2O.ai He holds a Bsc in accounting Finance from the University of Macedonia in Greece and an Msc in Risk Management from the University of Southampton. He has also nearly finished his PhD in machine learning at University College London (UCL) with a focus on ensemble modelling. He has worked in both marketing and credit sectors in the UK Market and has led many analytics’ projects with various themes including: Acquisition, Retention, Recommenders, Uplift, fraud detection, portfolio optimization and more. He is the creator of KazAnova, a freeware GUI for credit scoring and data mining 100% made in Java as well as is the creator of StackNet Meta-Modelling Framework. In his spare time he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular Kaggle.com data competition platform. Here is a blog about Marios being ranked at the top in Kaggle and sharing his knowledge with tricks and ideas. Finally, Marios’ likendin profile can be found here, with more information about what he is working on now or past projects. Linkedin: https://www.linkedin.com/in/mariosmichailidis// Bio: A Kaggle Grandmaster and a Data Scientist at H2O.ai, Mathias Müller holds an AI and ML focused diploma (eq. M.Sc.) in computer science from Humboldt University in Berlin. During his studies, he keenly worked on computer vision in the context of bio-inspired visual navigation of autonomous flying quadrocopters. Prior to H2O.ai, he as a machine learning engineer for FSD Fahrzeugsystemdaten GmbH in the automotive sector. His stint with Kaggle was a chance encounter as he stumbled upon the data competition platform while looking for a more ML-focused platform as compared to TopCoder. This is where he entered his first predictive modeling competition and climbed up the ladder to be a Grandmaster. He is an active contributor to XGBoost and is working on Driverless AI with H2O.ai. Linkedin: https://www.linkedin.com/in/muellermat/
Views: 712 H2O.ai
Java in production for Data Mining Research projects (JavaDayKiev'15)
 
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Alexey Zinoviev presented this paper on the JavaDayKiev'15 conference Slides: http://www.slideshare.net/zaleslaw/javadaykiev15-java-in-production-for-data-mining-research-projects This paper covers next topics: Data Mining, Machine Learning, Hadoop, Spark, MLlib
Views: 327 Alexey Zinoviev
Machine Learning #73 BIRCH Algorithm | Clustering
 
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Machine Learning #73 BIRCH Algorithm | Clustering In this lecture of machine learning we are going to see BIRCH algorithm for clustering with example. BIRCH algorithm (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm which is used to perform hierarchical clustering over particularly large data-sets.The advantage of using BIRCH algorithm is its ability to incrementally & dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). single scan of the database is needed by BIRCH algorithm in most of the cases. Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE) Best Programming Courses @ https://goo.gl/MVVDXR Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if MATLAB Tutorials @ https://goo.gl/EiPgCF
Views: 8839 Xoviabcs
RedisConf17 - Case Study: Redis Cluster at Flickr Yahoo! - Sean Perkins
 
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Slide deck: https://www.slideshare.net/RedisLabs/redisconf17-redis-cluster-at-flickr-and-tripod The Flickr team at Yahoo was an early adopter of Redis. Flickr uses Redis to cache camera roll and activity feed data, support notifications using Pub/Sub and manage an event queue. In this talk, we cover the use of Redis Cluster at Flickr - how challenges supporting high availability with Twemproxy and pre-cluster Redis, let us to investigate and deploy Redis Cluster.
Views: 308 Redis Labs
Linear Regression - Machine Learning Fun and Easy
 
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Linear Regression - Machine Learning Fun and Easy ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHIN LEARNING COURSE - http://augmentedstartups.info/machine-learning-courses ---------------------------------------------------------------------------- Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. Dependent Variable – Variable who’s values we want to explain or forecast Independent or explanatory Variable that Explains the other variable. Values are independent. Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents. And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways Used for 2 Applications To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables- • To see how increase in sin tax has an effect on how many cigarettes packs are consumed • Sleep hours vs test scores • Experience vs Salary • Pokemon vs Urban Density • House floor area vs House price Forecast new observations – Can use what we know to forecast unobserved values Here are some other examples of ways that linear regression can be applied. • So say the sales of ROI of Fidget spinners over time. • Stock price over time • Predict price of Bitcoin over time. Linear Regression is also known as the line of best fit The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x You most likely learnt this in school. So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis. M is your slope or gradient, if you change this, then your line rotates along the intercept. Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series. So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 133849 Augmented Startups
Clustering Individual Transactional Data for Masses of Users
 
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Author: Riccardo Guidotti, National Research Council (CNR) Abstract: Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 390 KDD2017 video
Practical Tips for Interpreting Machine Learning Models - Patrick Hall, H2O.ai
 
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This talk was recorded at H2O World 2018 NYC on June 7th, 2018. The slides from the talk can be viewed here: https://www.slideshare.net/0xdata/practical-tips-for-interpreting-machine-learning-models-patrick-hall-h2oai Session description: The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes! This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models: *Model visualizations including decision tree surrogate models, individual conditional expectation (ICE) plots, partial dependence plots, and residual analysis. *Reason code generation techniques like LIME, Shapley explanations, and Treeinterpreter. *Sensitivity Analysis. Plenty of guidance on when, and when not, to use these techniques will also be shared, and the talk will conclude by providing guidelines for testing generated explanations themselves for accuracy and stability. Open source examples (with lots of comments and helpful hints) for building interpretable machine learning systems are available to accompany the talk at: https://github.com/jphall663/interpretable_machine_learning_with_python Bio: Patrick Hall is 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 research and development roles at SAS Institute. Speaker's Bio: 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.
Views: 1521 H2O.ai
SF Data Mining MeetUp hosted by Marin Software
 
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Listen to AMPLab’s (Algorithms Machines People) Daniel Crankshaw talk about Velox! What’s Velox? Velox is a new component of the Berkeley Data Analytics Stack that addresses the critical missing component of current analytics process: the deployment and serving of models at scale. Filmed March 11, 2015 at Marin Software SF, CA. Thanks to everyone who came out to this event! Here's a link to Dan Crankshaw's slides: http://www.slideshare.net/dscrankshaw/velox-at-sf-data-mining-meetup Here are a few of the papers Dan mentions in the presentation: "A Contextual-Bandit Approach to Personalized News Article Recommendation," Lihong Li et al (http://www.research.rutgers.edu/~lihong/pub/Li10Contextual.pdf) "LASER: a scalable response prediction platform for online advertising" Deepak Agrawal et al. from LinkedIn (http://dl.acm.org/citation.cfm?id=2556252) And the two on fast top-k: "Fast top-k similarity queries via matrix compression" Yucheng Low et al. (http://research.microsoft.com/pubs/171030/topk.pdf) "Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)" Shrivastava et al. (http://papers.nips.cc/paper/5329-asymmetric-lsh-alsh-for-sublinear-time-maximum-inner-product-search-mips.pdf)
Views: 868 marinsoftware
Data Science - Part XI - Text Analytics
 
01:57:28
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
Views: 17366 Derek Kane
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
07:28
Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 172426 Augmented Startups
Data Mining, Лекция №3
 
01:31:08
Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Алгоритмы интеллектуальной обработки больших объемов данных", Лекция №3 "Различные алгоритмы кластеризации" Лектор - Николай Анохин Иерархическая кластеризация. Agglomerative и Divisive алгоритмы. Различные виды расстояний между кластерами. Stepwise-optimal алгоритм. Случай неэвклидовых пространств. Критерии выбора количества кластеров: rand, silhouette. DBSCAN. Слайды лекции http://www.slideshare.net/Technosphere1/lecture-3-47107546 Другие лекции курса Data Mining | https://www.youtube.com/playlist?list=PLrCZzMib1e9pyyrqknouMZbIPf4l3CwUP Наш видеоканал | http://www.youtube.com/user/TPMGTU?sub_confirmation=1 Официальный сайт Технопарка | https://tech-mail.ru/ Официальный сайт Техносферы | https://sfera-mail.ru/ Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Блог на Хабре | http://habrahabr.ru/company/mailru/ #ТЕХНОПАРК #ТЕХНОСФЕРА x
FOSDEM 2013 - Mining Social Data
 
29:03
Slides: http://www.slideshare.net/malk_zameth/mining-social-data-16288490 Hands-on section showing mining techniques for the social web (as a graph): Use them to visualize human interactions at a higher level, be it on public social networks (like facebook) or Enterprise private social networks (like yammer). while the examples will be social, the techniques exposed are usable in any graph datastore, the exact techniques I shall focus on will be: * Extraction * Finding frequent patterns * (Un)Supervised pattern learning * Constructing decision trees * Entity resolution Everything will be illustrated in code, All code will be open-source and pushed to github. Romeu MOURA (R&D Architect at Linagora) Architect in Linagora's cloud R&D projects, Romeu spends unrelenting verbiage trying to convince people to thrust public clouds, that the mining of big data can be ethical and improve everyone's life and that our current communication tools are artifacts of a bygone era.
Views: 188 Leonhard Euler
10 Myths About Data Science | Uncovering Data Science Myths | Data Science Training | Edureka
 
22:46
** Data Scientist Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka live session on “10 Data Science Myths" attempts to take down some of the misconceptions about Data Science and gives a much clearer picture of what data science really is. Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist ------------------------------------- Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Slideshare: https://www.slideshare.net/EdurekaIN/ #edureka #edurekadatascience #datascientist #datasciencemyths #top10datasciencemyths -------------------------------------- How it Works? 1. This is a 30-hour Instructor-led Online Course. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! ------------------------------------- About the Course Edureka's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. ------------------------------------- Who should go for this course? The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for: Developers aspiring to be a 'Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics 'R' professionals who wish to work Big Data Analysts wanting to understand Data Science methodologies ------------------------------------- Why learn Data Science? Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modeling, statistics, and analytics. To take complete benefit of these opportunities, you need structured training with an updated curriculum as per current industry requirements and best practices. Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges. ------------------------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Views: 3208 edureka!
DBSCAN | Density based clustering Algorithm - Simplest Explanation  in Hindi
 
06:46
SImplest Video about density based algorithm - DBSCAN
Views: 36402 Red Apple Tutorials
Driverless AI Hands-On Focused on Machine Learning Interpretability - H2O.ai
 
57:29
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: 1085 H2O.ai
Data Scientist Resume | Data Scientist Jobs, Salary & Skills | Data Science Training | Edureka
 
26:54
** Data Scientist Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This session on Data Scientist Resume will help you understand the demand and the growth of a Data Scientist and their impact on the business world. The following topics are covered in this session: 1. Who is a Data Scientist? 2. Data Scientist Job Trends 3. Data Scientist Salary Trends 4. Job Description 5. Skills required 6. Data Scientist Resume Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist ------------------------------------- Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Edureka! organizes live instructor-led webinars on the latest technologies, to stay updated, join our Meetup community: http://meetu.ps/c/4glvl/JzH2K/f Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Slideshare: https://www.slideshare.net/EdurekaIN/ #edureka #datascienceedureka #datascientistresume #datascientistcareer -------------------------------------- How it Works? 1. This is a 30-hour Instructor-led Online Course. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! ------------------------------------- About the Course Edureka's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. ------------------------------------- Who should go for this course? The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for: Developers aspiring to be a 'Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics 'R' professionals who wish to work Big Data Analysts wanting to understand Data Science methodologies ------------------------------------- Why learn Data Science? Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modeling, statistics, and analytics. To take complete benefit of these opportunities, you need structured training with an updated curriculum as per current industry requirements and best practices. Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges. ------------------------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Views: 4324 edureka!
Daniel Rodriguez: Querying 1 6 billion reddit comments with python
 
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PyData NYC 2015: New tools such as ibis and blaze have given python users the ability to write python expression that get translated to natural expression in multiple backends (spark, impala and more). Attendees will learn how these and other tools allow python to target bigger datasets specially impala. Talk will go through a big data pipeline, moving, converting and querying 1.6 comments from reddit. Since Google started to publish papers about their infrastructure starting with the Map Reduce and Google File System paper, that in time became Hadoop, the amount of tools (from big tech companies like Facebook, Yahoo and Cloudera) to gather and query this increasing amount of data has increased. These tools are often build on languages that run on top of the JVM such as Java (MapReduce, Hive) and Scala (Spark) and in some cases C++ (Impala). There is going to be a discussion on some of this new tools and how they make queries faster, the main focus is going to be Impala and the columnar file format Parquet. These the Big Data technologies that have more and different requirements than the small/medium data tools data scientists like such as R (dyplr) or Python (pandas). While the medium data tools run a single node the big data technologies run in a cluster of nodes and require a knowledge of DevOps data scientists usually don't have. New tools are coming up to help data scientists fill those missing requirements easier and also allowing them to target the big data technologies within python. While deploying clusters and install computing frameworks is now a new need and there have been solutions such as STAR Cluster and Spark includes scripts to deploy a spark cluster on EC2 these tools ofter fell short to the requirements data scientists have such as installing packages and having easy access to the cluster. There a some new tools that provide some of the same solutions but also try to give as much freedom as possible to data scientists and at the same time making the deployment of these tools faster using new Configuration Management tools like Salt. We will talk about Anaconda Cluster, a proprietary tool from Continuum Analytics that offer a free 4 node version, and a small alternative called DataScienceBox. Once a cluster is running its needed to target the big data frameworks from within Python having easy to write expressions that in some cases are transformed to queries to each framework and then are sent to these frameworks to let them do the heavy lifting of the data processing. Spark has always treated Python as a first class language so PySpark has been available for a while, what about the other tools like Impala. New projects have come out recently, they take a different approach than spark and have a write-once target multiple backends expression systems that will be familiar to regular pandas or R users. We will talk about Blaze from Continuum Analytics and Ibis from Cloudera and authored by the author of pandas. After the presentation and overview of the tools there is going to be a small demo in a running cluster using Blaze and Ibis (to target Impala) to query around 1.65 billion comments from Reddit that were recently made available to the public. Slides available here: http://www.slideshare.net/DanielRodriguez459/querying-18-billion-reddit-comments-with-python
Views: 1668 PyData
SFDM 20140922 Text Mining using KNIME
 
01:18:20
Rosaria Silipo and Cathy Pearl of KNIME talk at SF Data Mining meetup on 2014-09-22. I botched this video in a few ways. The worst botch is that there are 7 minutes of video missing after time 18:31. Fortunately, the KNIME YouTube channel has a video on how to read-in data from a database, here: https://www.youtube.com/watch?v=MHblrs6sPpE This slideshare might also be useful: http://www.slideshare.net/gpapadatos/knime-tutorial
Views: 1267 tube19880
Data Science - Part XV - MARS, Logistic Regression, & Survival Analysis
 
01:22:43
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview on extending the regression concepts brought forth in previous lectures. We will start off by going through a broad overview of the Multivariate Adaptive Regression Splines Algorithm, Logistic Regression, and then explore the Survival Analysis. The presentation will culminate with a real world example from my consulting work on how these techniques can be used in the US criminal justice system.
Views: 12856 Derek Kane
From data to AI with the Machine Learning Canvas by Louis Dorard
 
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https://www.bigdataspain.org Abstract: https://www.bigdataspain.org/program/fri-from-data-to-ai-with-the-machine-learning-canvas.html Slides: https://www.slideshare.net/secret/ETf7l0mccVWV8y Session presented at Big Data Spain 2016 Conference 17th Nov 2016 Kinépolis Madrid Event promoted by: http://www.paradigmadigital.com
Views: 601 Big Data Spain
Andre Panisson: Exploring temporal graph data with Python
 
37:19
PyData NYC 2015 We will see how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications in the academy and industry. We will see a use case where tensor decomposition was used to extract structural and temporal signatures from a time-varying social network collected from wearable proximity sensors. Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures. The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications. Slides available here: http://www.slideshare.net/panisson/exploring-temporal-graph-data-with-python-a-study-on-tensor-decomposition-of-wearable-sensor-data Github repo: https://github.com/panisson/ntf-school
Views: 1549 PyData
Computational Intelligence for  Wireless Sensor Networks:  Applications and Clustering Algorithms
 
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Computational Intelligence for Wireless Sensor Networks: Applications and Clustering Algorithms is a Seminar Presentation By Computer Engineering Students 2017. Its based on the following papers: Z.Rezaei and S. Mobininejad. Energy savings in wireless sensor networks. ”International Journal of Computer Science and Engineering Survey (IJCSES)”, 3(1):23–37, 2012. Neelam Srivastava. Challenges of next-generation wireless sensor networks and its impact on society. Journal of Telecommunications, 1(1):128–133, 2010. I. Khemapech, I. Duncan, and A. Miller. A survey of wireless sensor networks technology. The evolution and advance in micro electro-mechanical systems (MEMS) has led to the development of reliable, low cost, small size micro sensors. Hundreds to thousands of these heterogeneous sensors are deployed over a geographic area of interest, and communicate together forming a wireless sensor network. WSNs are deployed in land, underground and underwater. Nodes in the network sense external data from the surrounding environment, process the sensed data locally, and then send the data to a base station for further processing through wireless communication. In this paper, an overview of WSN applications and technology was given. Energy consumption problem was outlined. Various clustering technique were emphasized and the problems associated with traditional clustering methods were listed. CI paradigms used in clustering were briefly described and CI-clustering works were analyzed. It was found that there is still no clear decision about what the optimal number of clusters should be, or an approved mathematical model for optimization This presentation is apt for an engineering student especially IT or CSE student or even ECE student. Please have your queries underneath the video .If you need some other presentations please let us know. Please don't comment your mail ids asking for the PPT. Those who need this presentation as PPT files , contact us at http://ossels.com/contact/
Views: 1834 Ossels Tube
IRE Project: Aspect Based Sentiment Analysis
 
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The majority of current approaches attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e.g., laptops, restaurants) and their aspects (e.g., battery, screen; food, service) Our task was concerned with aspect based sentiment analysis (ABSA), where the goal was to identify the aspects of given target entities and the sentiment expressed towards each aspect. Github code: https://github.com/AkshitaJha/IRE Project Web Page: juhi-ghosh.github.io/IRE Slideshare : http://www.slideshare.net/AkshitaJha1/ire-project-presentation
Views: 2599 Akshita Jha
Tom Kraljevic - Big Data Environments
 
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Tom Kraljevic discusses big data environments with H2O on Hadoop, AWS, Apache Spark, and more. Don’t just consume, contribute your code and join the movement: https://github.com/h2oai User conference slides on open source machine learning software from H2O.ai at: http://www.slideshare.net/0xdata
Views: 818 H2O.ai
Anatomy of RDD :  Deep dive into Spark RDD abstraction
 
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An in depth discussion about Apache Spark RDD abstraction. Presented at Bangalore Apache Spark Meetup by Madhukara Phatak on 28/03/2015. http://www.meetup.com/Bangalore-Apache-Spark-Meetup/events/220684538/ For slides of this talk, refer http://www.slideshare.net/datamantra/anatomy-of-rdd For code, refer https://github.com/phatak-dev/anatomy-of-rdd Connect with Madhukara Phatak at http://www.datamantra.io http://www.madhukaraphatak.com
Views: 20581 datamantra
NLP with H2O, Supervised Learning with Unstructured Text Data - Megan Kurka, H2O.ai
 
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This video was recorded at #H2OWorld 2017 in Mountain View, CA. Enjoy the slides: https://www.slideshare.net/0xdata/nlp-with-h2o-83631331. Learn more about H2O.ai: https://www.h2o.ai/. Follow @h2oai: https://twitter.com/h2oai. - - - Abstract: The focus of this talk is to provide an introduction to Natural Language Processing with a focus on the Word2Vec algorithm. Word2Vec is an algorithm that trains a shallow neural network model to learn vector representations of words. These vector representations are able to capture the meanings of words. During the talk, we will use H2O's Word2Vec implementation to understand relationships between words in our text data. We will use the model results to find similar words, synonyms, and analogies. We will also use it to showcase how to effectively represent text data for machine learning problems where we will highlight the impact this representation can have on accuracy. The talk will cover the theory behind Word2Vec as well as a demo of a machine learning workflow with text data. Megan's Bio: Megan is a Customer Data Scientist at H2O.ai. Prior to working at H2O, she worked as a Data Scientist building products driven by machine learning for B2B customers. She has experience working with customers across multiple industries, identifying common problems, and designing robust and automated solutions. Megan is based in New York City and holds a degree in Applied Mathematics. In her free time, she enjoys hiking and yoga.
Views: 1401 H2O.ai