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R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 26109 DataCamp
Graph Mining with Deep Learning - Ana Paula Appel (IBM)
 
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Talk Slides: https://drive.google.com/open?id=1nm3jU2sjLxoatWTenffraN3a6xt0QEE8 Deep learning is widely use in several cases with a good match and accuracy, as for example images classifications. But when to come to social networks there is a lot of problems involved, for example how do we represent a network in a neural network without lost node correspondence? Which is the best encode for graphs or is it task dependent? Here I will review the state of art and present the success and fails in the area and which are the perspective. Ana Paula is a Research Staff Member in IBM Research - Brazil, currently work with large amount of data to do Science WITH Data and Science OF Data at IBM Research Brazil. My technical interesting are in data mining and machine learning area specially in graph mining techniques for health and finance data. I am engage in STEAM initiatives to help girls and women to go to math/computer/science are. She is also passion for innovation and thus I become a master inventor at IBM.
Views: 279 PAPIs.io
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 224715 Last moment tuitions
Basics of Scientific Literature Analysis, Part 4: Network analysis/visualization with Gephi
 
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The fourth part of the series demonstrates how to get use of the generated data files from "NAILS" tool in order to sketch a visualize network of the citations in Gephi software. More information about our online analysis service at http://nailsproject.net
Mining Web Graph For Recommendation
 
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Title: Mining Web Graph For Recommendation is developed by Mirror Technologies Pvt Ltd -- Vadapalani, Chennai. Domain: Data Mining. Algorithm Used: Query Suggestion Algorithm Key Features: 1. It is a general method, which can be utilized to many recommendation tasks on the Web. 2. It can provide latent semantically relevant results to the original information need. 3. This model provides a natural treatment for personalized recommendations. 4. The designed recommendation algorithm is scalable to very large datasets. Visit http://www.lbenchindia.com/ For more details contact: Mirror Technologies Pvt Ltd #73 & 79, South Sivan kovil Street, Vadapalani, Chennai, Tamil Nadu. Telephone: +91-44-42048874. Phone: 9381948474, 9381958575. E-Mail: [email protected], [email protected]
Views: 776 Learnbench India
Bioinformatics part 3 Sequence alignment introduction
 
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This Bioinformatics lecture explains the details about the sequence alignment. The mechanism and protocols of sequence alignment is explained in this video lecture on Bioinformatics. For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.[1] Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences, such as those present in natural language or in financial data. Very short or very similar sequences can be aligned by hand. However, most interesting problems require the alignment of lengthy, highly variable or extremely numerous sequences that cannot be aligned solely by human effort. Instead, human knowledge is applied in constructing algorithms to produce high-quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: global alignments and local alignments. Calculating a global alignment is a form of global optimization that "forces" the alignment to span the entire length of all query sequences. By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity. A variety of computational algorithms have been applied to the sequence alignment problem. These include slow but formally correct methods like dynamic programming. These also include efficient, heuristic algorithms or probabilistic methods designed for large-scale database search, that do not guarantee to find best matches. Global alignments, which attempt to align every residue in every sequence, are most useful when the sequences in the query set are similar and of roughly equal size. (This does not mean global alignments cannot end in gaps.) A general global alignment technique is the Needleman--Wunsch algorithm, which is based on dynamic programming. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. The Smith--Waterman algorithm is a general local alignment method also based on dynamic programming. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 166541 Shomu's Biology
Web search 2: big data beats clever algorithms
 
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A simple algorithm operating on lots of data will often outperform a more clever algorithm working with a sample. We illustrate this on the Question Answering (QA) task, where a simple algorithm (rewriting the question into web queries) outperformed systems based on sophisticated linguistic analysis.
Views: 1690 Victor Lavrenko
NLP(Text Mining)
 
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NLP
Views: 156 Amarnadh paritala
Rat Hole Mining in Meghalaya - Thin Coal Seams (Important - Current Affairs)
 
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Dr. Manishika Jain discusses the issue of rat hole mining in Meghalaya and ban of this mining - cause and consequences Refer - https://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Geography-Series.htm Also refer https://www.doorsteptutor.com/Exams/IAS/Mains/Optional/Geography/ #IAS #UPSC #rathole ngt ban on rat hole mining in meghalaya rat hole mining in cherrapunji disadvantages of rat hole mining rat hole mining upsc history of coal mining in meghalaya rat hole mining diagram rat hole mining in meghalaya upsc rat hole mining the hindu Mining of Coal @0:19 Two Types of Coal Structure @0:24 Coal of Seams @0:26 Thin Coal Seams @0:53 Meghalaya Economic @1:06 Rat Hole Mining @1:23 Government of Meghalaya @2:45 Seams @3:50 #Historical #Globally #Production #Environment #Structure #Mining #Seams #Coal #Thin #Economic #Manishika #Examrace
Views: 5010 Examrace
Design Mining the Web
 
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This seminar by Apropose, Inc., Chief Scientist Ranjitha Kumar is part of the Design at Large lecture series organized by CSE Prof. Scott Klemmer, and hosted by the Qualcomm Institute. The billions of pages on the Web today provide an opportunity to understand design practice on a truly massive scale: each page comprises a concrete example of visual problem solving, creativity, and aesthetics. In recent years, data mining and knowledge discovery have revolutionized the Web, driving search engines and recommender systems that are used by millions of people every day. However, data mining traditionally focuses on the content of Web pages, ignoring how that content is presented. What can we learn from miningdesign? This talk presents design mining for the Web, and presents a scalable platform for Web design mining called Webzeitgeist. Webzeitgeist consists of a repository of pages processed into data structures that facilitate large-scale design knowledge extraction. With Webzeitgeist, users can find, understand, and leverage visual design data in Web applications. I will demonstrate how software tools built on top of Webzeitgeist can be used to dynamically curate design galleries, search for design alternatives, retarget content between page designs, and even predict the semantic role of page elements from design data. As more and more creative work is done digitally and shared in the cloud, Webzeitgeist provides a concrete illustration of how design mining principles can be applied to benefit content creators and consumers. To learn more, visit webzeitgeist.stanford.edu.
Views: 1608 Calit2ube
data mining in healthcare
 
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how does the data mining technique help in solving healthcare problem-- Created using PowToon -- Free sign up at http://www.powtoon.com/ . Make your own animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 6255 Fouz Alaseeri
Lecture - 39 Natural Language Processing - I
 
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Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 89759 nptelhrd
SEIntelligence - Web Page Link Structure
 
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http://www.ragesw.com/products/seintelligence.html Learn about the Links tab in SEIntelligence and how anchor text plays a large role in how a site gets indexed.
Views: 1105 EverWeb
Text Mining Applications by Text Mining Solutions
 
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This is a brief insight into how Text Mining can be utilised across different industries. This video focuses on how Text Mining can be applied in the following industries: - Healthcare - Research - Corporate - Industry - Software - Publishing Text Mining is a flexible tool that can be utilised in near enough every industry. Interested and want to find out more? Go to http://www.textminingsolutions.co.uk Want to know the basics of Text Mining go to https://www.youtube.com/watch?v=zOcvi2R1FOA Follow Text Mining Solutions on: Facebook: https://www.facebook.com/TextMiningSolutions?fref=ts Twitter: https://twitter.com/Txt_Mining LinkedIn: https://www.linkedin.com/company/text-mining-solutions Music by: http://www.purple-planet.com
Views: 613 TxtMining
The future will be decentralized | Charles Hoskinson | TEDxBermuda
 
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This talk was given at a local TEDx event, produced independently of the TED Conferences. Tech entrepreneur and mathematician Charles Hoskinson says Bitcoin-related technology is about to revolutionise property rights, banking, remote education, private law and crowd-funding for the developing world. Charles Hoskinson is Chief Executive Officer at Thanatos Holdings, Director at The Bitcoin Education Project, and President at the Hoskinson Content Group LLC. Charles is a Colorado based technology entrepreneur and mathematician. He attended University of Colorado, Boulder to study analytic number theory in graduate school before moving into cryptography and social network theory. His professional experience includes work with NoSQL and Bigdata using MongoDB and Hadoop for several data mining projects involving crowdsource research and also development of web spiders. He is the author of several white papers on the design and deployment of low bandwidth prolog based semantical web scraping bots as well as analysis of metamorphic computer viruses through a case study on Zmist. His current projects focus on evangelism and education for Bitcoin and fully homomorphic encryption schemes. About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 312191 TEDx Talks
Interviewing with McKinsey: Case study interview
 
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Learn what to expect during the case study interview. Hear what some recent hires did - and did not - do to prepare.
Views: 602153 McKinsey & Company
Web 3.0 Introduction | Semantic Web Technologies/Concepts | Challenges/Issues | Web 2.0 Vs 3.0
 
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Web 3.0 Introduction | Semantic Web Technologies/Concepts | Challenges/Issues | Web 2.0 Vs 3.0 Like Us on Facebook - https://goo.gl/DdiLta Web Security Classes in Hindi Web Security Tutorial for Beginners Web Security Study Notes Web Engineering Notes Web Technology Notes
Social Network Analysis
 
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An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 4596 Microsoft Research
Graph Mining and Analysis Lecture-1
 
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GIAN_Video_Series_Lecture_Part_1 Lecture recorded on: 17 December 2015 Department of Computer Science, Jamia Millia Islamia, A Central University, New Delhi-110025
Spectral analysis for geological applications
 
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Discover geological exploration on-demand! Charlotte Bishop from CGG's NPA Satellite Mapping presented this webinar on geological exploration combining satellite imagery, DEM and spectral analysis. Hosted by Exelis (a specialist software provider), this webinar revealed how NPA Satellite Mapping uses satellite imagery in its geological studies, and how spectral analysis is of particular benefit, illustrated through a case study on gold mineralization. As part of CGG, NPA Satellite Mapping a world-leading supplier of satellite images, multi-client products and bespoke mapping services.
Views: 13135 CGGcompany
Locally Densest Subgraph Discovery
 
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Authors: Lu Qin, Rong-Hua Li, Lijun Chang, Chengqi Zhang Abstract: Mining dense subgraphs from a large graph is a fundamental graph mining task and can be widely applied in a variety of application domains such as network science, biology, graph database, web mining, graph compression, and micro-blogging systems. Here a dense subgraph is defined as a subgraph with high density (#.edge / #.node). Existing studies of this problem either focus on finding the densest subgraph or identifying an optimal clique-like dense subgraph, and they adopt a simple greedy approach to find the top-k dense subgraphs. However, their identified subgraphs cannot be used to represent the dense regions of the graph. Intuitively, to represent a dense region, the subgraph identified should be the subgraph with highest density in its local region in the graph. However, it is non-trivial to formally model a locally densest subgraph. In this paper, we aim to discover top-k such representative locally densest subgraphs of a graph. We provide an elegant parameter-free definition of a locally densest subgraph. The definition not only fits well with the intuition, but is also associated with several nice structural properties. We show that the set of locally densest subgraphs in a graph can be computed in polynomial time. We further propose three novel pruning strategies to largely reduce the search space of the algorithm. In our experiments, we use several real datasets with various graph properties to evaluate the effectiveness of our model using four quality measures and a case study. We also test our algorithms on several real web-scale graphs, one of which contains 118.14 million nodes and 1.02 billion edges, to demonstrate the high efficiency of the proposed algorithms. ACM DL: http://dl.acm.org/citation.cfm?id=2783299 DOI: http://dx.doi.org/10.1145/2783258.2783299
Graph Mining and Analysis Lecture-2
 
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GIAN_Video_Series_Lecture_Part_2 Lecture recorded on: 17 December 2015 Department of Computer Science, Jamia Millia Islamia, A Central University, New Delhi-110025
Structured Data in Web Search by Alon Halevy of Google - Technion lecture
 
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Structured Data in Web Search Lecture by Henry Taub Distinguished Visitor, Alon Halevy head of the Structured Data Management Research group at Google. For the first time since the emergence of the Web, structured data is playing a key role in search engines and is therefore being collected via a concerted effort. Much of this data is being extracted from the Web, which contains vast quantities of structured data on a variety of domains, such as hobbies, products and reference data. Moreover, the Web provides a platform that encourages publishing more data sets from governments and other public organizations. The Web also supports new data management opportunities, such as effective crisis response, data journalism and crowd-sourcing data sets. I will describe some of the efforts we are conducting at Google to collect structured data, filter the high-quality content, and serve it to our users. These efforts include providing Google Fusion Tables, a service for easily ingesting, visualizing and integrating data, mining the Web for high-quality HTML tables, and contributing these data assets to Google's other services.
Views: 1046 Technion
Mining Entity-Relation-Attribute Structures from Massive Text Data part 1
 
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Authors: Xiang Ren, University of Illinois at Urbana-Champaign Jiawei Han, Department of Computer Science, University of Illinois at Urbana-Champaign Abstract: Entity-Relation-Attribute (ERA) structures, forming structured networks between entities and attributes, have demonstrated the flexibility of storing rich information and the effectiveness of gaining insights and knowledge. However, the majority of massive amount of data in the real world are unstructured text, ranging from news articles, social media post, advertisements, to a wide range of textual information from various domains (medical records, corporate reports). Without heavy human annotations and curations, most of existing approaches have difficulties in extracting named entities and their relations as well as typing and organizing knowledge as networks. Link to tutorial: https://shangjingbo1226.github.io/2017-08-11-kdd-tutorial/ More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 143 KDD2017 video
Data Discovery: Tell Me Something I Don't Know - Part 1/3 - eMetrics Marketing Optimization Summit
 
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http://online-behavior.com/emetrics In this presentation, Neil Mason explores the approach to insight generation through data mining and predictive analytical technologies. Using real world case studies he covers the ins and outs of data mining analytics on digital data, which types of techniques can be used to solve which kinds of problems and some of the challenges that you will inevitable face along the way. Discover what your data can tell you if you ask it the right questions. *About Neil Mason* Neil Mason joined Foviance as part of an acquisition of Applied Insights whom he was director and co-founder. With 25 years of in-depth industry experience in marketing analytics and strategy, Neil leads Foviance's analytical consulting practice. This delivers an enhanced digital marketing analytics capability to both Foviance's and Applied Insights existing and future clients. *About Online Behavior* Online Behavior is a source of knowledge for website owners and analysts looking to understand how their online customers behave. But that's not all, understanding does not make a website better, action is required. That's why a broad range of techniques and strategies is needed to help optimizing websites. You will find information pertaining to: Market Targeting & Segmentation ( http://online-behavior.com/targeting ): Learn how to segment, provide the right message, and personalize your visitors' experience. Increased website relevance equals increased conversions and customer satisfaction. Website Testing & Usability ( http://online-behavior.com/testing ): Learn how to prioritize testing efforts, understand what affects user experience, and get ideas on the most profitable way to test your website. Web Analytics & Optimization ( http://online-behavior.com/analytics ): Learn the most advanced conversion optimization techniques to optimize your current traffic; turn new visitors into customers, and returning customers into loyal friends.
Views: 619 Online Behavior
Comprehensive Extraction of Chemical Information from Text
 
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Key chemical information is locked within patents and internal documents. In this talk we will overview the chemical text mining provided by the combination of the Linguamatics I2E text mining platform with name-to-structure, substructure and similarity search from ChemAxon. We will describe how this combination of technologies allows us to address some of the most difficult challenges such as extraction of structure activity relationships from tables. To accommodate the fast growing scientific literature from Asia, ChemAxon recently added support for Chinese naming, and we will discuss the advantages of mining in the original language rather than in a machine translation.
Views: 156 ChemAxon
Bruno Goncalves, Anastasios Noulas: Mining Georeferenced Data
 
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PyData NYC 2015 The democratization of GPS enabled devices has led to a surge of interest in the availability of high quality geocoded datasets. This data poses both opportunities and challenges for the study of social behavior. The goal of this tutorial is to introduce its attendants to the state-of-the-art in the mining and analysis in this new world of spatial data with a special focus on the real world. In this tutorial we will provide an overview of workflows for location rich data, from data collection to analysis and visualization using Python tools. In particular: Introduction to location rich data: In this part tutorial attendees will be provided with an overview perspective on location-based technologies, datasets, applications and services Online Data Collection: A brief introductions to the APIs of Twitter, Foursquare, Uber and AirBnB using Python (using urllib2, requests, BeautifulSoup). The focus will be on highlighting their similarities and differences and how they provide different perspectives on user behavior and urban activity. A special reference will be provided on the availability of Open Datasets with a notable example being the NYC Yellow Taxi dataset (NYC Taxy) Data analysis and Measurement: Using data collected using the APIs listed above we will perform several simple analyses to illustrate not only different techniques and libraries (geopy, shapely, data science toolkit, etc) but also the different kinds of insights that are possible to obtain using this kind of data, particularly on the study of population demographics, human mobility, urban activity and neighborhood modeling as well as spatial economics. Applied Data Mining and Machine Learning: In this part of the tutorial we will focus on exploiting the datasets collected in the previous part to solve interesting real world problems. After a brief introduction on python’s machine learning library, scikit-learn, we will formulate three optimization problems: i) predict the best area in New York City for opening a Starbucks using Foursquare check-in data, ii) predict the price of an Airbnb listing and iii) predict the average Uber surge multiplier of an area in New York City. Visualization: Finally, we introduce some simple techniques for mapping location data and placing it in a geographical context using matplotlib Basemap and py.processing. Slides available here: http://www.slideshare.net/bgoncalves/mining-georeferenced-data Code here: https://github.com/bmtgoncalves/Mining-Georeferenced-Data
Views: 1198 PyData
Relationships Matter: Mining relationships using Deep Learning by Dr. Atul Singh at #ODSC_India
 
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The desire to reduce the cognitive load on human agents for processing swathes of data in natural languages is driving the adoption of machine learning based software solutions for extracting structured information from unstructured text for a variety of use case scenarios such as monitoring Internet sites for potential terror threat and analyzing documents from disparate sources to identify potentially illegal transactions. These aforementioned software solutions for extracting structured information from unstructured text rely on the ability to identify the entities and the relationship between the entities using Natural Language Processing that has benefitted immensely from the progress in deep learning. The goal of this talk is to introduce relationship extraction a key plinth stone of natural language understanding, and its use for building knowledge graphs to represent structured information extracted from unstructured text. The talk demonstrates how deep learning lends itself well to the problem of relationship extraction and provides an elegant and simple solution. Details: https://confengine.com/odsc-india-2018/proposal/7264/relationships-matter-mining-relationships-using-deep-learning Conference: https://india.odsc.com/
Views: 147 ConfEngine
Structuring Data
 
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Beginner level. Basic rules for structuring your data and making it easy to work with. See the code on my blog here: http://douglaskline.blogspot.com/2018/07/structuring-data-in-tables.html
Views: 165 Database by Doug
NLP at Scale for Maintenance and Supply Chain Management — Ryan Chandler, Caterpillar
 
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How do you read 100,000 documents? The connection between the words we use and things and ideas that they represent can be represented as a structure. Using Neo4j this linguistic and semantic structure is developed to facilitate the large-scale analysis of text for meaning representation and automatic reading at scale. Learn how natural language processing can be implemented within Neo4j at scale to reveal actionable insights.  Also, see how these structures are visualized in virtual reality. Speaker: Ryan Chandler Location: GraphConnect NYC 2017
Views: 1312 Neo4j
Presentation Data Mining & Decision-making: Case of Amazon.com
 
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Week 2 assignment for MooreFMIS7003 course at NCU. Prepared by FahmeenaOdetta Moore.
Mining Knowledge from Databases: An Information Network Analysis Approach
 
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Most people consider a database is merely a data repository that supports data storage and retrieval. Actually, a database contains rich, inter-related, multi-typed data and information, forming one or a set of gigantic, interconnected, heterogeneous information networks. Much knowledge can be derived from such information networks if we systematically develop an effective and scalable database-oriented information network analysis technology. In this talk, we introduce database-oriented information network analysis methods and demonstrate how information networks can be used to improve data quality and consistency, facilitate data integration, and generate interesting knowledge. Moreover, we present interesting case studies on real datasets, including DBLP and Flickr, and show how interesting and organized knowledge can be generated from database-oriented information networks
Views: 74 Microsoft Research
▶ Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining ?
 
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Data Mining becomes a very hot topic in this moments because of its various uses. We can apply data mining to predict about an event that might happen. ✔Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining? We're gonna learn some real-life scenario of Data Mining in this video. »See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on #Data_Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner ট্র্যাডিশনাল পদ্ধতিতে যে সকল সমস্যার সহজে কোন সমাধান দেয়া যায় না #ডেটা_মাইনিং ব্যবহারে সহজেই একটি সিদ্ধান্তে পৌঁছানো সম্ভব। আর সে সিদ্ধান্ত কাজে লাগিয়ে ব্যবসায়িক অথবা যে কোন সম্পর্কিত সিদ্ধান্ত গ্রহন সম্ভব। Data Mining,big data,data analysis,data mining tutorial,book bd,Bangla tutorials,data mining software,Data Mining,What is data mining,bookbd,data analysis,data mining tutorial,data science,big data, business intelligence,data mining tools,bangla tutorial,data mining bangla tutorial,how to,how to mine data, knowledge discovery, Artificial Intelligence,Deep learning,machine learning,Python tutorials, Data Mining in the Retail Industry What does the future of business look like? How data will transform business? How data mining will transform business?
Views: 8359 BookBd
International Journal of Web & Semantic Technology (IJWesT)
 
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International Journal of Web & Semantic Technology (IJWesT) ISSN : 0975 - 9026 ( Online ) 0976- 2280 ( Print ) http://www.airccse.org/journal/ijwest/ijwest.html Scope & Topics International journal of Web & Semantic Technology (IJWesT) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of web & semantic technology. The growth of the World-Wide Web today is simply phenomenal. It continues to grow rapidly and new technologies, applications are being developed to support end users modern life. Semantic Technologies are designed to extend the capabilities of information on the Web and enterprise databases to be networked in meaningfulways. Semantic web is emerging as a core discipline in the field of Computer Science & Engineering from distributed computing, web engineering, databases, social networks, Multimedia, information systems, artificial intelligence, natural language processing, soft computing, and human-computer interaction. The adoption of standards like XML, Resource Description Framework and Web Ontology Language serve as foundation technologies to advancing the adoption of semantic technologies. Topics of Interest Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to • Semantic Query & Search • Semantic Advertising and Marketing • Linked Data, Taxonomies • Collaboration and Social Networks • Semantic Web and Web 2.0/AJAX, Web 3.0 • Semantic Case Studies • Ontologies (creation , merging, linking and reconciliation) • Semantic Integration, Rules • Data Integration and Mashups • Unstructured Information • Developing Semantic Applications • Semantics for Enterprise Information Management (EIM) • Knowledge Engineering and Management • Semantic SOA (Service Oriented Architectures) • Database Technologies for the Semantic Web • Semantic Web for e-Business, Governance and e-Learning • Semantic Brokering, Semantic Interoperability, Semantic Web Mining • Semantic Web Services (service description, discovery, invocation, composition) • Semantic Web Inference Schemes • Semantic Web Trust, Privacy, Security and Intellectual Property Rights • Information discovery and retrieval in semantic web; • Web services foundation, Architectures and frameworks. • Web languages & Web service applications. • Web Services-driven Business Process Management. • Collaborative systems Techniques. • Communication, Multimedia applications using web services • Virtualization • Federated Identity Management Systems • Interoperability and Standards • Social and Legal Aspect of Internet Computing • Internet and Web-based Applications and Services Paper Submission Authors are invited to submit papers for this journal through E-mail : [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 101 IJWEST JOURNAL
How to trade BitCoins Real-Time SEO Rankings Case Study 2018
 
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This is the first video from the real-time SEO case study of Google organic rankings for Howtotradebitcoins.org. As you see, the business niche that I chose is the Cryptocurrency, specifically BitCoin market. I chose this niche because I am personally not much familiar with the BitCoin and Cryptocurrencies, I have just a general knowledge as anyone else would so most of the people will probably be the same. The only thing that is my secret weapon is SEO, otherwise, I am a complete beginner in the Crypto market without any idea how competitive it is. This 2018 SEO case study training should help anyone who would like to see in a real time and reality how SEO rankings work. The case study can be useful for starting SEO learners or for the businesses to educate themselves on that topic to understand more what are they paying for. Either way, I hope it's going to give anyone right idea about how Search Engine Optimization works. Welcome to 2018 SEO Case Study of https://howtotradebitcoins.org #SEO2018CaseStudy #RealTimeSEORankingsCaseStudy #2018SEORankingsCaseStudy
Jure Leskovec, "The Web as a Laboratory for Studying Humanity", by  Jure Leskovec 2011-10-24:
 
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Speaker: Jure Leskovec Event Details http://www.sfbayacm.org/event/dmsig-1024-jure-leskovec-web-laboratory-studying-humanity With an increasing amount of social interaction taking place in on-line settings, we are accumulating massive amounts of data about phenomena that were once essentially invisible to us: the collective behavior and social interactions of hundreds of millions of people. Analyzing this massive data computationally offers enormous potential both to address long-standing scientific questions, and also to harness and inform the design of future social computing applications: What are emerging ideas and trends? How is information being created, how it flows and mutates as it is passed from a node to node like an epidemic? How will a community or a social network evolve in the future? We discuss how computational perspective can be applied to questions involving structure of online networks and the dynamics of information flows through such networks, including analysis of massive data as well as mathematical models that seek to abstract some of the underlying phenomena. Speaker Bio Jure Leskovec (http://cs.stanford.edu/~jure) is an assistant professor of Computer Science at Stanford University where he is a member of the Info Lab and the AI Lab. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems he investigates are motivated by large scale data, the Web and on-line media. He received six best paper awards, a ACM KDD dissertation award, Microsoft Research Faculty Fellowship and appeared on IEEE Intelligent Systems magazine "AI's 10 to Watch". Jure also holds three patents. Before joining Stanford Jure spent a year as a postdoctoral researcher at Cornell University. He completed his Ph.D. in computer science at Carnegie Mellon University in 2008. Jure has authored the Stanford Network Analysis Platform (SNAP), a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes, and billions of edges.
Views: 1536 San Francisco Bay ACM
OSCAR: text mining for chemistry
 
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OSCAR produces semantic annotation of chemistry documents. It uses natural-language processing to identify terms related to chemistry, which allows fast and efficient extraction of chemistry information. http://www.omii.ac.uk/wiki/OSCAR
Views: 355 omiiuk
Incremental Subspace Data-Mining Algorithm Based on Data-flow Density of Complex Networks
 
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Lijuan L. Incremental Subspace Data-Mining Algorithm Based on Data-flow Density of Complex Networks. Journal of Networks, 2014. 9(11): 3175-3180 Shazmeen SF, Baig M M A, Pawar M R. Performance Evaluation of Different Data Mining Classification Algorithm and Predictive Analysis. Journal of Computer Engineering, 2013, 10(6): 01-06. Chen Y G. On-line fast kernel based methods for classification over stream data (with case studies for cyber-security). Auckland University of Technology. 2012.
Views: 174 Leilani Lotti
The rise of ISIS, explained in 6 minutes
 
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Its history goes back way before the group ever existed. Subscribe to our channel! http://goo.gl/0bsAjO In the few short years since the Islamic State of Iraq and Syria formed, it has done the seemingly impossible, seizing vast areas of the Middle East to form a mini-state it calls a reincarnation of the ancient Caliphate. It is at war with all its neighbors and virtually the entire world, yet someone remains, and is launching increasingly deadly terror attacks abroad. To understand how this terrible group came about and how it has grown so powerful, you need to understand the story behind its rise. And that is a story that goes back decades, to long before ISIS existed. Check out our full video catalog: http://goo.gl/IZONyE Follow Vox on Twitter: http://goo.gl/XFrZ5H Or on Facebook: http://goo.gl/U2g06o
Views: 5275487 Vox
BrightEdge Data Cube
 
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Hear BrightEdge's CTO and Co-Founder Lemuel Park discuss the BrightEdge Data Cube. The Data Cube is a massive content repository, the industry's largest data set made up of billions of pieces of information which includes: keywords, search terms, rich media, and content, along with its performance on the web.
Views: 6657 BrightEdge
Graph Mining: Laws, Generators & Tools
 
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Prof. Christos Faloutsos Carnegie Mellon University October 15, 2007 -_-_-_-_-_-_-_-_-_-_-_- Samuel D. Conte Distinguished Lecture Series in Computer Science Sponsored by the Purdue University Department of Computer Science
Views: 2657 Purdue University
Soil-Structure Interaction Under  Extreme Loading Conditions - 2005 Buchanan Lecture by O'Rourke
 
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The Spencer J. Buchanan Lecture Series on the GeoChannel is presented by the Geo-Institute of ASCE. For more information about the Geo-Institute: http://www.asce.org/geotechnical-engineering/geo-institute/ Professor T.D. O'Rourke delivered the 13th Spencer J. Buchanan Lecture on November 18, 2005 at the Hilton Hotel in College Station, home of Texas A&M University. "Soil-Structure Interaction Under Extreme Loading Conditions" Abstract: Soil-structure interaction under extreme loading conditions includes performance during earthquakes, floods, landslides, large deformation induced by tunneling and deep excavations, and subsidence caused by severe dewatering or withdrawal of minerals and fluids during mining and oil production. Such loading conditions are becoming increasingly more important as technologies are developed to cope with natural hazards, human threats, and construction in congested urban environments. This paper examines extreme loading conditions with reference to earthquakes, which are used as an example of how extreme loading influences behavior at local and geographically distributed facilities. The paper covers performance from the component to the system-wide level to provide guidance in developing an integrated approach to the application of geotechnology over large, geographically distributed networks. The paper describes the effects of earthquake-induced ground deformation on underground facilities, and extends this treatment to the system-wide performance of the Los Angeles water supply during the 1994 Northridge earthquake. Large-scale experiments to evaluate soil-structure interaction under extreme loading conditions are described with reference to tests of abrupt ground rupture effects on urban gas pipelines. Large-scale tests and the development of design curves are described for the forces imposed on pipelines during ground failure. About Professor T.D. O'Rourke: Professor O’Rourke is a member of the faculty of the School of Civil and Environmental Engineering at Cornell University. He is a member of the US National Academy of Engineering and an elected Fellow of American Association for the Advancement of Science. He has received several awards from professional societies, including the Collingwood, Huber Research, C. Martin Duke Lifeline Earthquake Engineering, Stephen D. Bechtel Pipeline Engineering, and Ralph B. Peck Awards from American Society of Civil Engineers (ASCE), the Hogentogler Award from American Society for Testing and Materials, Trevithick Prize from the British Institution of Civil Engineers, the Japan Gas Award and Earthquake Engineering Research Institute (EERI) Awards for outstanding papers, and Distinguished Service Award from the University of Illinois College of Engineering. He served as President of the EERI and as a member of the US National Science Foundation Engineering Advisory Committee. He is a member of the Executive Committees of the Multidisciplinary Center for Earthquake Engineering Research and the Consortium of Universities for Research in Earthquake Engineering Board of Directors. He has served as Chair of the Executive Committee of the ASCE Technical Council on Lifeline Earthquake Engineering and ASCE Earth Retaining Structures Commitees. He has authored or co-authored over 290 technical publications. He has served on numerous earthquake reconnaissance missions, and has testified before the US Congress in 1999 on engineering implications of the 1999 Turkey and Taiwan earthquakes and in 2003 on the reauthorization of the National Earthquake Hazards Reduction Program. He has served as chair or member of the consulting boards of many large underground construction projects, as well as the peer reviews for projects associated with highway, rapid transit, water supply, and energy distribution systems. He has investigated and contributed to the mitigation of the effects of extreme events, including natural hazards and human threats, on critical civil infrastructure systems. His research interests cover geotechnical engi neering, earthquake engineering, engineering for large, geographically distributed systems (e.g., water supplies, gas and liquid fuel systems, electric power, and transportation facilities), underground construction technologies, and geographic information technologies and database management. Video Extraction by Magnus Media Group: http://www.magnusmediagroup.com/
Views: 2135 Geo-Institute of ASCE
Topic Model for Graph Mining | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 89 Clickmyproject
Solving Problems Declaratively - Mark Engelberg
 
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In this talk, we’ll be looking at how you can use the expressiveness of Clojure to model combinatorially complex problems at a high level, declaratively, and then pass the model to a speedy Java solving engine such as a dancing links solver, a SAT solver, or a constraint solver, in order to efficiently find a solution. We’ll survey a few different Clojure libraries that make it easy to connect to specific Java solvers and for each one, we’ll discuss what classes of problems are a good fit. Mark Engelberg has been an active member of the Clojure community ever since Clojure turned 1.0, and is the primary developer of math.combinatorics, math.numeric-tower, data.priority-map, ubergraph, and a co-developer of instaparse. He creates logic puzzles and games, using Clojure as a “secret weapon” to build his own puzzle development tools. His latest work is a line of programming-themed puzzle games for kids, produced by Thinkfun and slated to arrive in toy stores later this year.
Views: 6320 ClojureTV
Cloud FTP: A Case Study of Migrating Traditional Applications to the Cloud
 
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To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, 45, KAMARAJ SALAI, THATTANCHAVADY, PUDUCHERRY-9 Landmark: Opposite to Thattanchavady Industrial Estate, Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Cloud FTP: A Case Study of Migrating Traditional Applications to the Cloud The cloud computing is growing rapidly for it offers on-demand computing power and capacity. The power of cloud enables dynamic scalability of applications facing various business requirements. However, challenges arise when considering the large amount of existing applications. In this work we propose to move the traditional FTP service to the cloud. We implement FTP service on Windows Azure Platform along with the auto-scaling cloud feature. Based on this, we implement a benchmark to measure the performance of our Cloud FTP. This case study illustrates the potential benefits and technical issues associated with the migration of the traditional applications to the clouds.
Views: 307 jpinfotechprojects
MonogDB Tutorial For Beginners | What is MongoDB | MongoDB Installation On Linux | Simplilearn
 
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This MongoDb Tutorial for beginners will expliain what is MongoDB, MongoDB structure, MongoDb as a document databse, features of MongoDB, datatypes, core servers of MongoDB, MongoDB tools along with a demo on installing MongoDB on Linux 64 bit. MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemas. MongoDB is developed by MongoDB Inc., and is published under a combination of the GNU Affero General Public License and the Apache License. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=BigData-MongoDB-S3D5suhZ4bs&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-MongoDB-S3D5suhZ4bs&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - 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: 2185 Simplilearn
Data Collection and Preprocessing | Lecture 6
 
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Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07 Highlights: Garbage-in, Garbage-out Dataset Bias Data Collection Web Mining Subjective Studies Data Imputation Feature Scaling Data Imbalance #deeplearning #machinelearning
Views: 1512 Leo Isikdogan
NATURAL SKIN CARE FAVOURITES OF 2014!
 
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Views: 269483 holistichabits
Experion PKS - Lundin Norway Edvard Grieg | Honeywell Case Study
 
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Honeywell’s Experion® PKS is still all about connecting people with processes and assets with one unified architecture and a common HMI across the enterprise. It is the world’s most advanced, open, and cyber secure control system on the market today. It further optimizes LEAP™ project execution with Automated Device commissioning, enabling late binding of devices with loop configuration created in the cloud. Honeywell’s advancements in open system integration have led to new capabilities including applying Experion for electrical system control and management, multivariable APC in the controller, wireless HART, and automated skid integration via SCADA. Learn more at http://www.honeywellprocess.com/experion See the full list of Lundin Norway case study videos: http://hwll.co/lundin Honeywell Process Solutions is offering Lundin full support for the Edvard Grieg Project by providing the latest technology and processes. Find out more about Honeywell’s input throughout the project by viewing the main case study and the rest of the videos that go more in-depth on each of the products / solutions / services implemented in this project. Lundin Petroleum is an independent oil and gas exploration and production company whose main focus is on operations in Norway. Lundin’s Edvard Grieg field, situated in the Utsira High area of the central North Sea, was developed with a steel jacket platform that rests on the seabed, and has a full process facility. Learn more about Honeywell Process Solutions: http://www.honeywellprocess.com Subscribe on YouTube: http://www.youtube.com/channel/UCeqTN5THQ-08qcPlIBXQdJg?sub_confirmation=1 Follow us on Twitter: http://twitter.com/hwusers Follow us on LinkedIn: http://www.linkedin.com/company/honeywell-process-solutions Honeywell helps industrial customers around the world operate safe, reliable, efficient, sustainable and more profitable facilities. We offer leading technologies, in-depth training and comprehensive services that allow faster unit start-ups and more uptime. We have pioneered process automation control for more than 40 years. We have the right resources to serve the oil & gas, refining, pulp & paper, industrial power generation, chemicals and petrochemicals, biofuels, life sciences, and metals, minerals and mining industries. Our broad portfolio of products and services can be tailored to our customers’ process automation needs, from production and supply chain management to project management services, control systems and field devices. Honeywell is inventing technologies that address some of the world’s toughest challenges in energy efficiency, clean energy generation, safety and security, globalization and customer productivity. With approximately 132,000 employees worldwide, including more than 22,000 engineers and scientists, we have an unrelenting focus on performance, quality, delivery, value and technology in everything we make and do. We welcome comments and feedback on our videos. All we ask is that you respect our social media community guidelines https://www.honeywellprocess.com/en-US/Pages/social-media.aspx http://www.youtube.com/Honeywell
Become Data Mining Expert in few steps
 
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Data Mining is the process of extracting information from a large data set and transforms this extracted information into understandable data structure. The data mining is frequently applied to many information processing such as collection, extraction, warehousing, statistics and analysis as well as the applications of computer decision support systems such as artificial intelligence, business intelligence and machine learning. There are many large companies uses this technology to focus on the most important information in their data warehouses
Views: 118 Dinesh Gupta