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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: 98174 LearnEveryone
Data Mining with Weka (1.1: Introduction)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 1: Introduction http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 114917 WekaMOOC
DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 7 Smoothing Methods Part 2
 
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https://www.coursera.org/learn/text-retrieval
Views: 118 Ryo Eng
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
 
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So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 324155 CrashCourse
Job Roles For DATA ENTRY OPERATOR – Entry Level,DataBase,Arts,Science,WPM, Data Management
 
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Job Roles For DATA ENTRY OPERATOR : Know more about job roles and responsibility in DATA ENTRY . Coming to DATA ENTRY OPERATOR opportunities for freshers in India,Visit http://www.freshersworld.com?src=Youtube for detailed information,Job Opportunities,Education details and Career growth of DATA ENTRY OPERATOR. No matter what your educational background is, data entry operator jobs are available for all fresh candidates. People usually do not seek this position thinking that this is a low-level job. As a matter of fact, it is not lower than any other entry level position in corporate world. The main job of a data entry operator is to update, add and maintain data in a system or managing databases. The data entry operator is expected to insert or add data related to the company (both text and numerical) from a source file provided by the company. The candidate should also verify and sort the information as per given instruction. Other operational work includes generating routine reports and filing documents related to their work. Mostly, freshers with bachelor degree in arts and science are sought for this position. Even diploma candidates are opted for this position by many companies. Usually, candidates with professional degree, master degree or doctorate would not be sought for this position. The basic requirements are a) Knowledge and savvy in computer operation b) Expertise in MS-Office and other related software c) High typing speed – minimum market requirement is 40 WPM with 95% accuracy. d) Basic communication skills in English Usually, the candidates with good computer skill would be sought without regards to their educational background. Rotational shifts are rare and both male and female are sought. This job is also available in working-from-home option in some companies. There are short terms courses with certification for data entry offered by many institutions. Though it is not an essential certification, it would give a competitive edge over other candidates. Those who have working knowledge of Tally are sought for accountancy related data entry with a slightly higher pay. The same goes for those with commerce related educational background. With an increase in growth of BPO industry in India, there is a very high demand for data entry specialists. With one to three years experience in data entry, one can apply for jobs related to data management, document imaging, data mining, data processing and other related fields. If you want to grow in the same field, with three or more years of experience in data entry job, you can apply for senior data entry position or data analyzer positions. With more experience, you can apply for managerial positions like transaction processor, document processor and many others. Your scope is not restricted to back office operations. Candidates with a few years of experience in data entry can take up operational related jobs in KPO and customer service department. Yet, they would be considered as fresher in the new department. This job is for those who do not have a fancy degree and yet, want to take up corporate job. With this job, entry into corporate world becomes easy for all kinds of candidates. The academic excellence is not an important qualification for this job. Thus, candidates with backlog and those with moderate communication skill can apply for this position if, their typing skill is excellent. For more jobs & career information and daily job alerts, subscribe to our channel and support us. You can also install our Mobile app for govt jobs for getting regular notifications on your mobile. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com?src=Youtube - - ***Disclaimer: This is just a career guidance video for fresher candidates. The name, logo and properties mentioned in the video are proprietary property of the respective companies. The career and job information mentioned are an indicative generalised information. In no way Freshersworld.com, indulges into direct or indirect recruitment process of the respective companies.
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 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 Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 322848 edureka!
Data Mining with Weka (1.3: Exploring datasets)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Exploring datasets http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 72840 WekaMOOC
Data Structures and Algorithms Complete Tutorial Computer Education for All
 
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Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
Mod-01 Lec-04 Clustering vs. Classification
 
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Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 19094 nptelhrd
R Programming For Beginners | R Language Tutorial | R Tutorial For Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Programming Tutorial For Beginners (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R and will help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Variables 2. Data types 3. Operators 4. Conditional Statements 5. Loops 6. Strings 7. Functions Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 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 Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 205907 edureka!
Lecture 01 - The Learning Problem
 
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The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 786538 caltech
Statistical Aspects of Data Mining (Stats 202) Day 1
 
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Google Tech Talks June 26, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 212330 GoogleTechTalks
Data Warehousing & Data Mining
 
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Heyy guys here is some information on Data Warehousing and Data Mining! ========================= Background Song: Disfigure - Blank [NCS Release] Song Artist: No Copyright Sounds
Views: 500 Brandon The Crab
9. Modeling and Discovery of Sequence Motifs
 
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MIT 7.91J Foundations of Computational and Systems Biology, Spring 2014 View the complete course: http://ocw.mit.edu/7-91JS14 Instructor: Christopher Burge This lecture by Prof. Christopher Burge covers modeling and discovery of sequence motifs. He gives the example of the Gibbs sampling algorithm. He covers information content of a motif, and he ends with parameter estimation for motif models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 9368 MIT OpenCourseWare
How Bitcoin Works in 5 Minutes (Technical)
 
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A short introduction to how Bitcoin Works. Want more? Check out my new in-depth course on the latest in Bitcoin, Blockchain, and a survey of the most exciting projects coming out (Ethereum, etc): https://app.pluralsight.com/library/courses/bitcoin-decentralized-technology Lots of demos on how to buy, send, store (hardware, paper wallet). how to use javascript to send bitcoin. How to create Ethereum Smart Contract, much more. Written Version: http://www.imponderablethings.com/2014/04/how-bitcoin-works-in-5-minutes.html Less technical version: https://www.youtube.com/watch?v=t5JGQXCTe3c Donation address: 1K7A6wsyxj6fThtMYcNu6X8bLbnNKovgtP Germain caption translation provided by adi331 : 19s6rqRfHa19w7wcgwtCumPs1vdLDj1VVo (thanks!!)
Views: 5560537 CuriousInventor
7 minute micro-teaching on Predictive Analytics and Big Data
 
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This is a 7 minute micro-teaching lecture introducing the concepts of predictive analytics and big data. The presentation was offered to a class of students from various backgrounds. This was a part of the Graduate Teaching Certification (tier 2) program - Sayanti Mukherjee.
Views: 47 Sayanti Mukherjee
Lecture 1 - Artificial Intelligence
 
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Learn about AI, the future of data science and a brief introduction to Python in our first ever DataSoc Lecture. Speakers: Saksham and Daniel
Ontologies
 
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Dr. Michel Dumontier from Stanford University presents a lecture on "Ontologies." Lecture Description Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery. View slides from this lecture: https://drive.google.com/open?id=0B4IAKVDZz_JUVjZuRVpMVDMwR0E About the Speaker Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
research papers on data mining in healthcare
 
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Get 15% Discount: https://goo.gl/TIo1T2?27022
Data Mining with Weka (1.5: Using a filter )
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: Using a filter http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 59622 WekaMOOC
Building Ontologies: An Introduction for Engineers (Part 1)
 
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Begins with some historical background on the growth of ontology as a discipline on the borderlines of computer science, data science and philosophy. Sketches the development of the Semantic Web and the use of ontologies in the biomedical domain. Concludes with some reflections on the problems associated with the idea of 'linked open data'. Lecture presented at the Swiss Federal Institute of Technology (EPFL), Lausanne, January 30, 2017
Views: 7553 Barry Smith
Environmental Impact Assessment - Analyzing Benefits and Actions (Examrace)
 
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Dr. Manishika Jain in this lecture explains the concept of Environmental Impact Assessment (EIA) and difference between EIA and Strategic EIA. Tool to identify environmental, social and economic impacts of a project prior to decision-making – UNEP In India, Started in 1978-79 by river valley projects EIA has now been made mandatory under the Environmental Protection Act, 1986 for 29 categories of developmental activities that involves investments of Rs. 50 crores & more EIA – Definition @0:07 Stages Involved in EIA @4:51 Which Projects fall under EIA? @6:16 What to Address? @7:59 Benefits of EIA @9:19 Procedure @10:12 Follow Up @11:56 Polluter’s Pay Principle @12:07 Precautionary Principle @12:24 Strategic EIA @13:24 Environment Impact Assessment @14:09 Strategic Environment Assessment @14:19 #Implementation #Effluents #Concentration #Hazardous #Cumulatively #Screening #Compliance #Enforcement #Developmental #Investments #Manishika #Examrace Stages Involved in EIA Screening Scoping Assessment & Evaluation Report EIA: Non-technical summary for the general audience Review EIS Decision Making: Whether to approve project or not Monitoring, Compliance, Enforcement Environmental Auditing Which projects fall under EIA? Which can significantly alter the landscape, land use pattern & lead to concentration of working population Which need upstream development activity like assured mineral and forest products supply Which need downstream industrial process development Those involving manufacture, handling and use of hazardous materials Those sited near ecologically sensitive areas, urban centers, hill resorts, places of scientific and religious importance Industrial Estates which could cumulatively cause significant environmental damage What to Address? Meteorology and air quality Hydrology and water quality Site and its surroundings Occupational safety and health Details of the treatment and disposal of effluents and the methods of alternative uses Transportation of raw material and details of material handling Control equipment and measures proposed to be adopted Benefits of EIA Environmental benefits Economic benefits Reduced cost and time of project implementation and design Avoided treatment Clean-up costs Impacts of laws and regulations Procedure Follow Up Precautionary Principle: If an action or policy has a suspected risk of causing harm to the public, or environment, in the absence of scientific consensus, the burden of proof falls on those taking the action. Part of Rio Declaration & Kyoto Protocol. Polluter’s Pay Principle: To make the party responsible for producing pollution responsible for paying for the damage done to the natural environment. Support from OECD and European Community. Strategic EIA Formalized, systematic & comprehensive process to identify & evaluate environmental consequences of proposed policies, plans or programs Ensure full inclusion Address at earliest possible stage of decision-making on a par with economic & social considerations Can be applied to entire sector For NET Paper 1 material refer - http://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm Examrace is number 1 education portal for competitive and scholastic exam like UPSC, NET, SSC, Bank PO, IBPS, NEET, AIIMS, JEE and more. We provide free study material, exam & sample papers, information on deadlines, exam format etc. Our vision is to provide preparation resources to each and every student even in distant corders of the globe. Dr. Manishika Jain served as visiting professor at Gujarat University. Earlier she was serving in the Planning Department, City of Hillsboro, Hillsboro, Oregon, USA with focus on application of GIS for Downtown Development and Renewal. She completed her fellowship in Community-focused Urban Development from Colorado State University, Colorado, USA. For more information - https://www.examrace.com/About-Examrace/Company-Information/Examrace-Authors.html
Views: 100323 Examrace
17. Learning: Boosting
 
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MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston Can multiple weak classifiers be used to make a strong one? We examine the boosting algorithm, which adjusts the weight of each classifier, and work through the math. We end with how boosting doesn't seem to overfit, and mention some applications. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 149766 MIT OpenCourseWare
Math for Big Data, Lecture 18,  Intro. Data Mining, Ahn& Choo,
 
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Math for Big Data, Lecture 18, Intro. Data Mining, Ahn& Choo,
Views: 67 Sang-Gu Lee
Applications of Predictive Analytics in Legal | litigation Analytics,  data mining and AI
 
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Learn the prediction of outcome or treatment of a case by legal courts of Appeals based on historical data using predictive analytics. Watch the video to understand analytics in legal using case study on real-life data set. How litigation analytics can flourish with the use of data mining and AI. Know more about our analytics Program: PGP- Business Analytics: https://goo.gl/V9RzVD PGP- Big Data Analytics: https://goo.gl/rRyjj4 Business Analytics Certification Program: https://goo.gl/7HPoUY
Views: 565 Great Learning
Data Mining with Weka (5.3: Data mining and ethics)
 
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Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 3: Data mining and ethics http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 10238 WekaMOOC
Mining Your Logs - Gaining Insight Through Visualization
 
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Google Tech Talk (more info below) March 30, 2011 Presented by Raffael Marty. ABSTRACT In this two part presentation we will explore log analysis and log visualization. We will have a look at the history of log analysis; where log analysis stands today, what tools are available to process logs, what is working today, and more importantly, what is not working in log analysis. What will the future bring? Do our current approaches hold up under future requirements? We will discuss a number of issues and will try to figure out how we can address them. By looking at various log analysis challenges, we will explore how visualization can help address a number of them; keeping in mind that log visualization is not just a science, but also an art. We will apply a security lens to look at a number of use-cases in the area of security visualization. From there we will discuss what else is needed in the area of visualization, where the challenges lie, and where we should continue putting our research and development efforts. Speaker Info: Raffael Marty is COO and co-founder of Loggly Inc., a San Francisco based SaaS company, providing a logging as a service platform. Raffy is an expert and author in the areas of data analysis and visualization. His interests span anything related to information security, big data analysis, and information visualization. Previously, he has held various positions in the SIEM and log management space at companies such as Splunk, ArcSight, IBM research, and PriceWaterhouse Coopers. Nowadays, he is frequently consulted as an industry expert in all aspects of log analysis and data visualization. As the co-founder of Loggly, Raffy spends a lot of time re-inventing the logging space and - when not surfing the California waves - he can be found teaching classes and giving lectures at conferences around the world. http://about.me/raffy
Views: 24908 GoogleTechTalks
How to start a Business Analytics Career in India ? - Skills required, Pay scale, Job opportunities
 
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Get the latest interview tips,Job notifications,top MNC openings,placement papers and many more only at Freshersworld.com(www.freshersworld.com?src=Youtube). The major role of a BA is – data mining, statistical analysis, predictive modelling and multivariate test. Business Analytics career in India has emerged as the preferred role of choice in IT and ITES industries. The Business Analyst is also required to support decision making roles with real-time analysis. Business Analysts also work closely with the senior management and provide support in data-driven decision making that impacts matters related to product development to marketing. There is a continued strong demand for business analysts especially in India where candidates can land opportunities in IT and ITES industries. According to one article, India has at least 1.2 million business analysts and by 2020 India is pegged to have the highest number of business analysts. Some of the major sectors where Business Analysts have a promising start are retail, banking, healthcare, ecommerce, hospitality, manufacturing etc. Kick-start a Business Analytics career in India One of the most promising career paths in IT today, the job description for business analysts sometimes converge analytics and project management roles. If you are interested in a Business Analyst role, a background in mathematics and engineering is a must, backed by good analytical and communication skills. However, there are those who believe candidates with a business background can also make a career in BA. By upskilling themselves with short online courses, these candidates can also get a good grip on business analytics and start a career. Skills required by Business Analytics candidate: A Business Analyst should be proficient in applied statistics, have knowledge of statistical suite such as SAS, R, SPSS, should know SQL, Hive, knowledge of testibg framework and a working knowledge of BI tools such as Qlik, Tableau, Spot fire among others. Skills may vary depending on the organization’s requirement. However, this is a basic knowledge framework required for making the cut. How can candidates with a business background start a Business Analytics career? While most engineers gravitate towards the data engineering and information management field, candidates with a business background can easily transition into the Business Analyst role. MBA holders can sharpen their skills by a) enrolling in analytics courses, b) participating in mentoring sessions and boot camps to lands their dream job. And though companies don’t expect deep knowledge of tools, a basic understanding can help in landing the right job. Job opportunities for Business Analysts: From leading financial institutions, to consultancies such as Deloitte, E&Y, and global retailers Target, Walmart and online leader Amazon require Business Analytics professionals. Top Employers: Some of the top Business Analytics companies to work for are: • Tata Consultancy Services • Cognizant • Accenture • GENPACT • Wipro • Infosys • IBM • Deloitte • HPE Some of the startups that provide an excellent opportunity to pursue Business Analytics career are Fractal Analytics, Mu Sigma Analytics and Absolut Data. Pay scale: One of the most sought after jobs, business analysts have a rare blend of business and analytical skills and are rewarded with good pay packages. The average salary of senior business analyst is INR 8,59,025 per year. The average salary of BA is INR 6,44,857 per year. Download our app today to manage recruitment whenever and where ever you want : Link :https://play.google.com/store/apps/details?id=com.freshersworld.jobs&hl=en ***Disclaimer: This is just a training video for candidates and recruiters. The name, logo and properties mentioned in the video are proprietary property of the respective organizations. The Preparation tips and tricks are an indicative generalized information. In no way Freshersworld.com, indulges into direct or indirect promotion of the respective Groups or organizations.
Data Analyst HandsOn SQL Beginner Training (Week1)
 
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This is the first session for Data Analysts Career path for 21st May 2016 batch. Understanding the core of RDBMS and SQL programming is one of the skills that make a good analyst. This session is focused on introducing students to IT and Relational Database Management System (RDBMS) By the end of the week you would have finished learning, and achieving the below • Getting familiar with Information Technology • Background and historical knowledge of SQL • Configuring the server environment • Connecting to the server • Creating a database If you will like to get real hands-on with SQL and Data Analytics, join other hundreds of learners in our community. Send us an email [email protected]
Views: 30711 Dare Olufunmilayo
sqit3033 knowledge acquisition in decision making
 
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sqit3033 data mining class
Views: 1032 izwan nizal nize
Decision Tree (CART) - Machine Learning Fun and Easy
 
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Decision Tree (CART) - Machine Learning Fun and Easy https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression (CART). So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node. To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :) -------------------------------------------------- Support us on Patreon http://bit.ly/PatreonArduinoStartups --------------------------------------------------
Views: 76188 Augmented Startups
BADM 1.1: Data Mining Applications
 
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This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 2019 Galit Shmueli
BADM 1.2: Data Mining in a Nutshell
 
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What is Data Mining? How is it different from Statistics? This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Networks: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 857 Galit Shmueli
Big Data and Hadoop 1 | Hadoop Tutorial 1 | Big Data Tutorial 1 | Hadoop Tutorial for Beginners - 1
 
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( Hadoop Training: https://www.edureka.co/hadoop ) Watch our New and Updated Hadoop Tutorial For Beginners: https://goo.gl/xeEV6m Check our Hadoop Tutorial blog series: https://goo.gl/LFesy8 This is Part 1 of 8 week Big Data and Hadoop course. The 3hr Interactive live class covers What is Big Data, What is Hadoop and Why Hadoop? We also understand the details of Hadoop Distributed File System ( HDFS). The Tutorial covers in detail about Name Node, Data Nodes, Secondary Name Node, the need for Hadoop. It goes into the details of concepts like Rack Awareness, Data Replication, Reading and Writing on HDFS. We will also show how to setup the cloudera VM on your machine. More details below: Welcome, Let's Get Going on our Hadoop Journey... - - - - - - - - - - - - - - How it Works? 1. This is a 8 Week Instructor led Online Course. 2. We have a 3-hour Live and Interactive Sessions every Sunday. 3. We have 3 hours of Practical Work involving Lab Assignments, Case Studies and Projects every week which can be done at your own pace. We can also provide you Remote Access to Our Hadoop Cluster for doing Practicals. 4. 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. 5. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course. - - - - - - - - - - - - - - Course Objectives After the completion of the Hadoop Course at Edureka, you should be able to: Master the concepts of Hadoop Distributed File System. Understand Cluster Setup and Installation. Understand MapReduce and Functional programming. Understand How Pig is tightly coupled with Map-Reduce. Learn how to use Hive, How you can load data into HIVE and query data from Hive. Implement HBase, MapReduce Integration, Advanced Usage and Advanced Indexing. Have a good understanding of ZooKeeper service and Sqoop. Develop a working Hadoop Architecture. - - - - - - - - - - - - - - Who should go for this course? This course is designed for developers with some programming experience (preferably Java) who are looking forward to acquire a solid foundation of Hadoop Architecture. Existing knowledge of Hadoop is not required for this course. - - - - - - - - - - - - - - Why Learn Hadoop? BiG Data! A Worldwide Problem? According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, It is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop, which is not less than a panacea for all those companies working with BIG DATA in a variety of applications and has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. - - - - - - - - - - - - - - Some of the top companies using Hadoop: The importance of Hadoop is evident from the fact that there are many global MNCs that are using Hadoop and consider it as an integral part of their functioning, such as companies like Yahoo and Facebook! On February 19, 2008, Yahoo! Inc. established the world's largest Hadoop production application. The Yahoo! Search Webmap is a Hadoop application that runs on over 10,000 core Linux cluster and generates data that is now widely used in every Yahoo! Web search query. Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! Happy Hadooping! Please write back to us at [email protected] or call us at +91 88808 62004 for more information.
Views: 1072728 edureka!
What is Economics?
 
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http://economicsdetective.com/ The typical first-year student walks into his first economics class with very little idea of what economics is. He might have heard something like, "economics is the study of money", or "economics is another word for accounting", or "economics is hard, don't take that class", but none of those are true. "Economics is the study of the use of scarce resources that have alternative uses." That's the classic definition of economics. Basically, there are people, and people need resources to fulfil their desires. These resources cannot be infinite, but the desires can be, so people need to make choices about how to use their scarce resources. Economists study these choices. All economic questions fall into one of two categories: positive and normative. Positive economics describes "what is" and normative economics argues for what ought to be, so a question like, "why do people use money?" is a positive question and "should people use money?" is a normative question. A general rule of thumb is that if your economic model has no value judgements, it's positive economics, and if it does have value judgements it's normative economics, since to tell someone what he ought to do, you first have to judge what is best for him. Economics is also divided into microeconomics and macroeconomics. Microeconomics studies the behaviour of individual agents and markets, while macroeconomics studies the behaviour of the entire economy. Economists also have their own branch of statistics called "econometrics" that's specialized to analyzing economic data. Since economic data usually comes from the real world, and not from controlled experiments, econometrics faces mathematical challenges that other fields might not. The tools economists have developed to study human behaviour have broad uses outside of what we would traditionally consider economics. Economists study not only markets, but things like crime, war, the family, religion, culture, politics, law, and even genetics. That's why it's not unusual to see papers by psychologists, sociologists, criminologists, political scientists, anthropologists, biologists, neuroscientists, or legal scholars being co-authored by economists.
Views: 659235 The Economics Detective
Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 5844 Microsoft Research
ROC Curves and Area Under the Curve (AUC) Explained
 
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Transcript and screenshots: http://dataschool.io/roc-curves-and-auc-explained/ Visualization: http://www.navan.name/roc/ Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). == LET'S CONNECT! == Blog: http://www.dataschool.io Newsletter: http://www.dataschool.io/subscribe/ Twitter: https://twitter.com/justmarkham GitHub: https://github.com/justmarkham
Views: 234978 Data School
MSBI Tutorials for Beginners | Business Intelligence Tutorial | Learn MSBI | MSBI Training | Edureka
 
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This Edureka MSBI Tutorial video will help you learn the basics of MSBI. This powerful suite is composed of tools which helps in providing best solutions for Business Intelligence and Data Mining Queries.This video will help you master MSBI concepts such as SSIS, SSAS and SSRS along with demo using SQL Server and Data Tools. Below are the topics covered in this tutorial: 1. Why Business Intelligence? 2. What is BI? 3. BI tools 4. Why Microsoft BI? 5. What is Microsoft BI? 6. Microsoft BI Architecture 7. Tools & Utilities of Microsoft BI 8. Demo Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Microsoft BI playlist here: https://goo.gl/Vo6Klo #MicrosoftBI #MicrosoftBItutorial #MicrosoftBIcourse How it Works? 1. This is a 30 Hours of Online Live Instructor-Led Classes. Weekend Class : 10 sessions of 3 hours each. Weekday Class : 15 sessions of 2 hours each. 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 Microsoft BI Certification Course is designed to provide insights on different tools in Microsoft BI Suite (SQL Server Integration Services, SQL Server Analysis Services, SQL Server Reporting Services). Get expertise in SSIS , SSAS & SSRS concepts and master them. The course will give you the practical knowledge on Data Warehouse concepts and how these tools help in developing a robust end-to-end BI solution using the Microsoft BI Suite. --------------------------------------------------- Who should go for this course? Microsoft BI Certification Course at Edureka is designed for professionals aspiring to make a career in Business Intelligence. Software or Analytics professionals having background/experience of any RDBMS, ETL, OLAP or reporting tools are the key beneficiaries of this MSBI course. You can check a blog related to Microsoft BI – Why You Need It For A Better Business Intelligence Career!! Also, once your Microsoft BI training is over, you can check the Microsoft Business Intelligence Interview Questions related edureka blog. --------------------------------------------------- Why learn Microsoft BI ? As we move from experience and intuition based decision making to actual decision making, it is increasingly important to capture data and store it in a way that allows us to make smarter decisions. This is where Data warehouse/Business Intelligence comes into picture. There is a huge demand for Business Intelligence professionals and this course acts as a foundation which opens the door to a variety of opportunities in Business Intelligence space. Though there are many vendors providing BI tools, very few of them provide end to end BI suite and huge customer base. Microsoft stands as leader with its user-friendly and cost effective Business Intelligence suite helping customers to get a 360 degree view of their businesses. Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Website: https://www.edureka.co/microsoft-bi Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Amit Vij, HRSSC HRIS Senior Advisor at DLA Piper, says "I am not a big fan of online courses and also opted for class room based training sessions in past. Out of surprise, I had a WoW factor when I attended first session of my MSBI course with Edureka. Presentation - Check, Faculty - Check, Voice Clarity - Check, Course Content - Check, Course Schedule and Breaks - Check, Revisting Past Modules - Awesome with a big check. I like the way classes were organised and faculty was far above beyond expectations. I will recommend Edureka to everyone and will personally revisit them for my future learnings."
Views: 29373 edureka!
What is E-Commerce in Hindi  (Basic Information for Beginners)
 
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Types of E-Commerce : https://youtu.be/m7x6zYEBYEM What is EDI in eCommerce ?: https://youtu.be/zN237-EpFQI ------------------------------------------------------------------------- What is E-Commerce in Hindi what is ecommerce meaning in hindi ecommerce explained e commerce means in hindi ecommerce means introduction to ecommerce in hindi ecommerce theory -------------------------------------------------------------- This is my Blog: http://mystudymafia.blogspot.in/2018/02/e-commerce-stands-for-electronic.html
Views: 114164 STUDY Mafia
Introduction to Probability and Statistics 131A. Lecture 1. Probability
 
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UCI Math 131A: Introduction to Probability and Statistics (Summer 2013) Lec 01. Introduction to Probability and Statistics: Probability View the complete course: http://ocw.uci.edu/courses/math_131a_introduction_to_probability_and_statistics.html Instructor: Michael C. Cranston, Ph.D. License: Creative Commons CC-BY-SA Terms of Use: http://ocw.uci.edu/info More courses at http://ocw.uci.edu Description: UCI Math 131A is an introductory course covering basic principles of probability and statistical inference. Axiomatic definition of probability, random variables, probability distributions, expectation. Recorded on June 24, 2013 Required attribution: Cranston, Michael C. Math 131A (UCI OpenCourseWare: University of California, Irvine), http://ocw.uci.edu/courses/math_131a_introduction_to_probability_and_statistics.html. [Access date]. License: Creative Commons Attribution-ShareAlike 3.0 United States License. (http://creativecommons.org/licenses/by-sa/3.0/deed.en_US)
Views: 186541 UCI Open
Person Entities: Lessons learned by a data provider
 
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Talk by John W. Chapman (OCLC, Inc.). Title: Person Entities: Lessons learned by a data provider Abstract: Continuing the longstanding research program by OCLC in the field of linked data, recent projects have focused on creating sets of entities of high interest for any organization wanting to utilize linked data paradigms. Through intensive mining and clustering of WorldCat bibliographic data, name and subject authority files, and other related data sets, OCLC has produced over 300 million entity representations. These clusters pull together and represent creative works, and persons related to those works. OCLC has engaged with a number of libraries and organizations to create and experiment with this data. A pilot project during October 2015-February 2016 to explore new methods of providing access to Person entities provided a number of new directions and insights. The core purpose of the work is to understand how these entities might best be leveraged to make library workflows more efficient, and to improve the quality of metadata produced in the library sector. This presentation will provide a background on data used in the project, as well as the development of services and APIs to provision the data. It will address challenges and opportunities in the area of creating and managing entities, and ways in which they could be improved and enriched over time. SWIB16 Conference, 28 - 30 November 2016, Bonn, Germany http://swib.org/swib16/ #swib16 Licence: CC-BY-SA https://creativecommons.org/licenses/by-sa/3.0/
Views: 64 SWIB
Machine Learning  - Linear Algebra Basics
 
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In this video you will learn Linear Algebra Basics. Linear algebra is a cornerstone because everything in machine learning is a vector or a matrix. For more information visit Ivy Professional School - http://ivyproschool.com/our-courses/big-data-and-analytics/machine-learning-with-python-certification/
Views: 6235 IvyProSchool
Week 9 Machine Learning versus classical statistics
 
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Sign up for the newsletter here : http://tinyletter.com/jhudatascience Ask a question here: https://docs.google.com/forms/d/e/1FAIpQLScE3bgLFd5u6kibWPYMalLenh8wdRnMw2tieHUXxzjVFporyg/viewform?usp=sf_link Executive Data Science https://www.coursera.org/specializations/executive-data-science Data Science in Real Life https://www.coursera.org/learn/real-life-data-science NSSDs https://soundcloud.com/nssd-podcast Effort Report https://twitter.com/theeffortreport?lang=en If you'd like to donate to the Data Science Lab at Johns Hopkins, click here: https://secure.jhu.edu/form/jhsph (click "Other" then fill in Data Science Lab in the Department of Biostatistics).
Views: 2424 Brian Caffo
Learning Classifier Systems in a Nutshell
 
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This video offers an accessible introduction to the basics of how Learning Classifier Systems (LCS), also known as Rule-Based Machine Learning (RBML), operate to learn patterns and make predictions. To simplify these concepts, we have focused on a generic ‘Michigan-style LCS’ algorithm architecture designed for supervised learning. The example algorithm described in this video is probably closest to the UCS algorithm described by Bernadó-Mansilla and Garrell-Guiu in their 2003 publication. However, the modern concept of the LCS algorithm is the result of founding work by John Henry Holland (https://en.wikipedia.org/wiki/John_Henry_Holland) While this video focuses on how the algorithm itself works, here we provide a brief background on why LCS algorithms are valuable and unique compared to other machine learning strategies. LCSs are a family of advanced machine learning algorithms that learn to represent patterns of association in a distributed, piece-wise fashion. These systems break down associations between independent and dependent variables into simple ‘IF:THEN’ statements. This makes them very flexible and adaptive learners that can approach data in a model free and assumption free manner. Research and development of LCS algorithms was initially focused on reinforcement learning problems such as behavior modeling, but in the last decade, the advantages of applying these systems as supervised learners has become clear. In particular LCS algorithms have been demonstrated to perform particularly well on the detection, modeling and characterization of complex, multi-variate, epistatic, or heterogeneous patterns of association. Additionally, LCS algorithms are naturally multi-objective (accuracy, and generality), niche learners, and can easily be thought of as implicit ensemble learners. Furthermore, LCSs can be adapted to handle missing data values, imbalanced data, discrete and continuous features, as well as binary class, multi-class, and regression learning/prediction. The flagship benchmark problem for these systems has traditionally been the n-bit multiplexer problem. The multiplexer is a binary classification problem that is both epistatic and heterogeneous where no single feature is predictive of class on its own. This benchmark can be scaled up in dimensional complexity to include the 6-bit, 11-bit, 20-bit, 37-bit, 70-bit, and 135-bit variations. Most other machine learners struggle, in particular, with heterogeneous relationships. As of 2016, our own LCS algorithm, called ‘ExSTraCS’ was still the only algorithm in the world to report having the ability to solve the 135-bit multiplexer problem directly. For a complete introduction, review, and roadmap to LCS algorithms, check out my review paper from 2009: http://dl.acm.org/citation.cfm?id=1644491 The first introductory textbook on LCS algorithms (authored by Will Browne and myself) will be published by 'Springer' this fall: (link will be found here once it's available) To follow research and software developed by Ryan Urbanowicz PhD on rule-based machine learning methods or other topics, check out the following links. http://www.ryanurbanowicz.com https://github.com/ryanurbs To follow research and software development by Jason H. Moore PhD, and his Computation Genetics Lab at the University of Pennsylvania’s Institute for Biomedical Informatics, check out the following links. http://epistasis.org/ http://upibi.org/
Views: 5496 ryan urbanowicz
Final Year Projects | Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis
 
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Final Year Projects | Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis More Details: Visit http://clickmyproject.com/horizontal-aggregations-in-sql-to-prepare-data-sets-for-data-mining-analysis-p-124.html Including Packages ======================= * 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:+91 967-778-1155 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us : [email protected]
Views: 2555 ClickMyProject
Google's Deep Mind Explained! - Self Learning A.I.
 
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Subscribe here: https://goo.gl/9FS8uF Become a Patreon!: https://www.patreon.com/ColdFusion_TV Visual animal AI: https://www.youtube.com/watch?v=DgPaCWJL7XI Hi, welcome to ColdFusion (formally known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere. Sources: Why AlphaGo is NOT an "Expert System": https://googleblog.blogspot.com.au/2016/01/alphago-machine-learning-game-go.html “Inside DeepMind” Nature video: https://www.youtube.com/watch?v=xN1d3qHMIEQ “AlphaGo and the future of Artificial Intelligence” BBC Newsnight: https://www.youtube.com/watch?v=53YLZBSS0cc http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html http://www.ft.com/cms/s/2/063c1176-d29a-11e5-969e-9d801cf5e15b.html http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html#tables https://www.technologyreview.com/s/533741/best-of-2014-googles-secretive-deepmind-startup-unveils-a-neural-turing-machine/ https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1 https://www.deepmind.com/ www.forbes.com/sites/privacynotice/2014/02/03/inside-googles-mysterious-ethics-board/#5dc388ee4674 https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1#.4yt5o1e59 http://www.theverge.com/2016/3/10/11192774/demis-hassabis-interview-alphago-google-deepmind-ai https://en.wikipedia.org/wiki/Demis_Hassabis https://en.wikipedia.org/wiki/Google_DeepMind //Soundtrack// Disclosure - You & Me (Ft. Eliza Doolittle) (Bicep Remix) Stumbleine - Glacier Sundra - Drifting in the Sea of Dreams (Chapter 2) Dakent - Noon (Mindthings Rework) Hnrk - fjarlæg Dr Meaker - Don't Think It's Love (Real Connoisseur Remix) Sweetheart of Kairi - Last Summer Song (ft. CoMa) Hiatus - Nimbus KOAN Sound & Asa - This Time Around (feat. Koo) Burn Water - Hide » Google + | http://www.google.com/+coldfustion » Facebook | https://www.facebook.com/ColdFusionTV » My music | t.guarva.com.au/BurnWater http://burnwater.bandcamp.com or » http://www.soundcloud.com/burnwater » https://www.patreon.com/ColdFusion_TV » Collection of music used in videos: https://www.youtube.com/watch?v=YOrJJKW31OA Producer: Dagogo Altraide Editing website: www.cfnstudios.com Coldfusion Android Launcher: https://play.google.com/store/apps/details?id=nqr.coldfustion.com&hl=en » Twitter | @ColdFusion_TV
Views: 2850095 ColdFusion
MDM-L01T04: Medical Data Mining \ Introduction & Scientific Background - [Health Care]
 
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Views: 32 Health Care
what is micro & macro economics in hindi
 
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Explain Micro and Macro Economics with example. व्यष्टि और समष्टि अर्थशास्त्र क्या है ?उदाहरण के साथ l Microeconomics is the study of particular markets, and segments of the economy. It looks at issues such as consumer behaviour, individual labour markets, and the theory of firms. Macro economics is the study of the whole economy. It looks at ‘aggregate’ variables, such as aggregate demand, national output and inflation.
Views: 265045 Know Economics
Tetiana Ivanova - How to become a Data Scientist in 6 months a hacker’s approach to career planning
 
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PyData London 2016 This talk outlines my journey from complete novice to machine learning practitioner. It started in November 2015 when I left my job as a project manager, and by April 2016 I was hired as a Data Scientist by a startup developing bleeding edge deep learning algorithms for medical imagery processing. SHORT INTRO Who I am, my background and short summary of my story. Here I will list the steps I personally took to achieve the goal I had. HOW DID I DO IT? Why I chose a “hacky” way to enter this career path. First mover advantage, why getting a degree doesn’t always improve your career prospects. Possibly a rant on the signalling function of formal education and how that is rarely aligned with a relevant practical skill set. Some stats to back it up (best career success predictors). Examples of hacking bureaucracies/social hierarchies from my experience and elsewhere. List of things not to do and common cognitive pitfalls. Networking for nerds - how to do it right. Time management for chronic procrastinators - how to plan a self-guided project. Some notes on psychology of time discounting and need for external reinforcement, with autobiographical examples. CONCLUSION You don’t need a PhD or even a masters to do machine learning. On taking calculated risks and especially calculated exits from one’s comfort zone. Some notes on soul searching and how to choose a career that is also a passion. Reading list. Slides available here: https://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months
Views: 185170 PyData