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Predicting Instructor Performance Using Data Mining Techniques in Higher Education
 
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Predicting Instructor Performance Using Data Mining Techniques in Higher Education -- Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student's performance instead of instructors' performance. One of the common tools to evaluate instructors' performance is the course evaluation questionnaire to evaluate based on students' perception. In this paper, four different classication techniquesdecision tree algorithms, support vector machines, articial neural networks, and discriminant analysisare used to build classier models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specicity performance metrics. Although all the classier models show comparably high classication performances, C5.0 classier is the best with respect to accuracy, precision, and specicity. In addition, an analysis of the variable importance for each classier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors' success based on the students' perception mainly depends on the interest of the students in the course. The ndings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these ndings may be used to improve the measurement instruments. Articial neural networks, classication algorithms, decision trees, linear discriminant analysis, performance evaluation, support vector machines. -- For More Details Contact Us -- S.Venkatesan Arihant Techno Solutions Pudukkottai www.arihants.com Mobile: +91 75984 92789
data mining techniques
 
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This video describes data mining tasks or techniques in brief. Each technique requires a separate explanation as well. #datamining #techniques #weka Data mining tutorial in hindi Weka tutorial in hindi Data mining tutorial
Views: 5033 yaachana bhawsar
DataMiningVideo2013
 
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It is a short video regarding Data Mining Applications in Higher Education
Applications of Predictive Analytics in Legal | Litigation Analytics, Data Mining & AI | Great Lakes
 
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#PredictiveAnalytics | 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 #LegalTech #LegalAnalytics #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube
Views: 839 Great Learning
High Dimensional Data
 
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Match the applications to the theorems: (i) Find the variance of traffic volumes in a large network presented as streaming data. (ii) Estimate failure probabilities in a complex systems with many parts. (iii) Group customers into clusters based on what they bought. (a) Projecting high dimensional space to a random low dimensional space scales each vector's length by (roughly) the same factor. (b) A random walk in a high dimensional convex set converges rather fast. (c) Given data points, we can find their best-fit subspace fast. While the theorems are precise, the talk will deal with applications at a high level. Other theorems/applications may be discussed.
Views: 2292 Microsoft Research
Data Mining in Higher Education
 
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Table of Contents: 00:04 - Better predict each student's Performance by taking into account More than grades 00:12 - Better manage marketing dollars for recruitment. 00:16 - Better understanding of factors related to struggling students, ultimately to increase retention. 00:22 - An understanding of support programs' effectiveness. 00:26 - Better understanding demographic and other factors 00:32 - Determine which non-need based packages attract the best students. 00:39 - What factors lead to student retention, especially at-risk students? 00:45 - Predict which students are likely to default on their student loans. 00:50 - Comment!
Views: 305 Salford Systems
A Systematic Review on Educational Data Mining | Final year Projects 2016 - 2017
 
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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/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 459 Clickmyproject
SPS2017: Educational Data Mining Software
 
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The video is giving details about research software developed using WEKA (Open source Data Mining tool) and JAVA (Programming Language). The first version is developed in 2017. Anyone having the link can download this software and directly use this software without any installation. All the instructions are given in 'README.txt' file in a downloaded zip folder. Any suggestions and questions are invited in the comment section below. Feel free to add below. Music Credits: Youtube Audio Library
Views: 76 Prabhjot Kaur
Ethics of Data Mining and Predictive Analytics in Higher Education
 
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Presented at the Rocky Mountain Association for Institutional Research Conference Laramie, Wyoming | October 5, 2012 Data mining and predictive analytics are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining's outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.
Views: 756 Jeff Johnson
How to Build a Winning Machine Learning FOREX Strategy in Python: Getting & Plotting Historical Data
 
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In this video we are going learn how about the various sources for historical FOREX data. Primarily, we will be using data from Dukascopy bank. There are many other FOREX historical data sources that you need to pay for that will be of much higher quality. However, I feel that the data available for free at Dukascopy will be quite sufficient for our uses. Additionally, I give a demonstration on how to set up a plot that, in my opinion, is much easier/better looking than a plot using Matplotlib. Plotly is very flexible and can be used to generate many different plot types; I strongly recommend it. In the next video we will begin to construct functions that will return financial indicators. Down the road, we will use the indicators to train a machine learning algorithm to make binary price predictions. We will use a test dataset to backtest our strategy and adjust parameters to our liking. Useful links: **** DUKASCOPY HISTORICAL DATA FEED **** https://www.dukascopy.com/swiss/english/marketwatch/historical/ **** PYTHON FFN: FINANCIAL FUNCTIONS **** http://pmorissette.github.io/ffn/ **** PLOTLY **** https://plot.ly/ **** PANDAS DOCUMENTATION **** http://pandas.pydata.org/pandas-docs/stable/ **** TRADING VIEW **** https://www.tradingview.com/
Views: 13671 PythonParseltongue
Data Mining in Education
 
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I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 688 stlgretchen
Mining Social Media Data for Understanding Students’ Learning Experiences
 
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Abstract—Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences—opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi-label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from about 35,000 tweets streamed at the geo-location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences. Index Terms—Education, computers and education, social networking, web text analysis
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: 108781 LearnEveryone
Big Data, the Science of Learning, Analytics, and Transformation of Education
 
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From the mediaX Conference “Platforms for Collaboration and Productivity”, Candace Thille, with the Stanford Graduate School of Education highlights the power of platform tools and technologies to transform observation and data collection. This process enables researchers from industry and academia to know their user better – as consumers, as producers, and as learners.
Views: 9242 Stanford
A Review on Mining Students’ Data for Performance Prediction  | Final Year Projects 2016 - 2017
 
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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/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 700 Clickmyproject
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: 12712 Microsoft Research
APPLICATION OF BIG DATA IN EDUCATION DATA MINING
 
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APPLICATION OF BIG DATA IN EDUCATION DATA MINING
Views: 304 Chennai Sunday
Student Learning Evaluation - Predicting Student Performance
 
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Predicting Instructor Performance Using Data Mining Techniques in Higher Education -- Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. Generally, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’ perception. In this study, four different classification techniques, –decision tree algorithms, support vector machines, artificial neural networks, and discriminant analysis– are used to build classifier models. Their performances are compared over a dataset composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specificity performance metrics. Although all the classifier models show comparably high classification performances, C5.0 classifier is the best with respect to accuracy, precision, and specificity. In addition, an analysis of the variable importance for each classifier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors’ success based on the students’ perception mainly depends on the interest of the students in the course. The findings of the study indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these findings may be used to improve measurement instruments. Artificial neural networks, classification algorithms, decision trees, linear discriminant analysis, performance evaluation, support vector machines -- For More Details Contact Us -- S.Venkatesan Arihant Techno Solutions Pudukkottai www.arihants.com Mobile: +91 75984 92789
12. Clustering
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 79900 MIT OpenCourseWare
Scanner: Efficient Video Analysis at Scale (SIGGRAPH 2018)
 
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http://scanner.run/ http://graphics.stanford.edu/papers/scanner/scanner_sig18.pdf Scanner is a system for developing applications that efficiently process large video datasets. Scanner applications can run on a multi-core laptop, a server packed with multiple GPUs, or a large number of machines in the cloud. Scanner has been used for: * Labeling and data mining large video collections: Scanner is in use at Stanford University as the compute engine for visual data mining applications that detect people, commercials, human poses, etc. in datasets as big as 70,000 hours of TV news (12 billion frames, 20 TB) or 600 feature length movies (106 million frames). * VR Video synthesis: scaling the Surround 360 VR video stitching software, which processes fourteen 2048x2048 input videos to produce 8k stereo video output. To learn more about Scanner, see the documentation below or read the SIGGRAPH 2018 Technical Paper: “Scanner: Efficient Video Analysis at Scale” by Poms, Crichton, Hanrahan, and Fatahalian.
Views: 1822 Will Crichton
Analyzing Big Data in less time with Google BigQuery
 
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Most experienced data analysts and programmers already have the skills to get started. BigQuery is fully managed and lets you search through terabytes of data in seconds. It’s also cost effective: you can store gigabytes, terabytes, or even petabytes of data with no upfront payment, no administrative costs, and no licensing fees. In this webinar, we will: - Build several highly-effective analytics solutions with Google BigQuery - Provide a clear road map of BigQuery capabilities - Explain how to quickly find answers and examples online - Share how to best evaluate BigQuery for your use cases - Answer your questions about BigQuery
Views: 66931 Google Cloud Platform
HPLC chromatography
 
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HPLC chromatography lecture - This lecture explains about the HPLC chromatography technique in a nutshell by Suman Bhattacharjee. HPLC is performed to separate organic and biological compounds using solid stationary phase. High Performance Liquid Chromatography (HPLC) is a form of column chromatography that pumps a sample mixture or analyte in a solvent which is known as the mobile phase at high pressure through a column with chromatographic packing material known as stationary phase. The sample is carried by a moving carrier gas stream of helium or nitrogen. HPLC has the ability to separate, and identify compounds that are present in any sample that can be dissolved in a liquid in trace concentrations as low as parts per trillion. Because of this versatility, HPLC is used in a variety of industrial and scientific applications, such as pharmaceutical, environmental, forensics, and chemicals. Sample retention time will vary depending on the interaction between the stationary phase, the molecules being analyzed, and the solvent, or solvents used. As the sample passes through the column it interacts between the two phases at different rate, primarily due to different polarities in the analytes. Analytes that have the least amount of interaction with the stationary phase or the most amount of interaction with the mobile phase will exit the column faster. This lecture explains the following things about Hplc chromatography - 1. Hplc chromatography principle 2. Hplc chromatography instrumentation 3. Hplc chromatography types High-Performance Liquid Chromatography - Other HPLC Types Ultra High Performance Liquid Chromatography (uHPLC): Where standard HPLC typically uses column particles with sizes from 3 to 5µm and pressures of around 400 bar, uHPLC use specially designed columns with particles down to 1.7µm in size, at pressures in excess of 1000 bar. The main advantage of an uHPLC is speed. These systems are faster, more sensitive, and rely on smaller volumes of organic solvents than standard HPLC, resulting in the ability to run more samples in less time. Article source: http://hiq.linde-gas.com/en/analytical_methods/liquid_chromatography/high_performance_liquid_chromatography.html For more information, log on to- http://www.shomusbiology.com/ Get Shomu's Biology DVD set here- http://www.shomusbiology.com/dvd-store/ Download the study materials here- http://shomusbiology.com/bio-materials.html Remember Shomu’s Biology is created to spread the knowledge of life science and biology by sharing all this free biology lectures video and animation presented by Suman Bhattacharjee in YouTube. All these tutorials are brought to you for free. Please subscribe to our channel so that we can grow together. You can check for any of the following services from Shomu’s Biology- Buy Shomu’s Biology lecture DVD set- www.shomusbiology.com/dvd-store Shomu’s Biology assignment services – www.shomusbiology.com/assignment -help Join Online coaching for CSIR NET exam – www.shomusbiology.com/net-coaching We are social. Find us on different sites here- Our Website – www.shomusbiology.com Facebook page- https://www.facebook.com/ShomusBiology/ Twitter - https://twitter.com/shomusbiology SlideShare- www.slideshare.net/shomusbiology Google plus- https://plus.google.com/113648584982732129198 Thank you for watching HPLC lecture
Views: 511868 Shomu's Biology
Mining Social Media Data for Understanding Students’ Learning Experiences
 
00:54
Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences—opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from tweets streamed at the geo-location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences.
The Data Science Economy - DataEDGE 2015
 
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The Data Science Economy Vijay K. Narayanan Friday, May 8, 2015 http://dataedge.ischool.berkeley.edu/2015/schedule/data-science-economy In this talk, I will present three distinct aspects of the data science economy: 1. data, algorithms, systems and humans as the four main drivers of the data science economy 2. a marketplace of intelligent APIs hosted on the cloud that can be easily consumed to build higher level intelligent applications 3. data enabled applications on the cloud in traditional industries. Vijay K. Narayanan Director, Algorithms and Data Science Solutions Microsoft Vijay K Narayanan leads the Algorithms and Data Science efforts in the Information Management and Machine Learning group in Microsoft, where he works on building and leveraging machine learning platforms, tools and solutions to solve analytic problems in diverse domains. Earlier, he worked as a Principal Scientist at Yahoo! Labs, where he worked on building cloud based machine learning applications in computational advertising, as an Analytic Science Manager in FICO where he worked on launching a product to combat identify theft and application fraud using machine learning, as a Modeling Researcher at ACI Worldwide, and as a Sloan Digital Sky Survey research fellow in Astrophysics at Princeton University where he co-discovered the ionization boundary and the four farthest quasars in the universe. He received a Bachelor of Technology degree from IIT, Chennai and a PhD in Astronomy from The Ohio State University. Narayanan has authored or coauthored approximately 55 peer-reviewed papers in astrophysics, 10 papers in machine learning and data mining techniques and applications, and 15 patents (filed or granted). He is deeply interested in the theoretical, applied, and business aspects of large scale data mining and machine learning, and has indiscriminate interests in statistics, information retrieval, extraction, signal processing, information theory, and large scale computing.
01 - Application to Clustering (12 min)
 
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Description
Views: 39 xind xrci
Demo: IBM Big Data and Analytics at work in Banking
 
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Visit http://ibmbigdatahub.com for more industry demos. Banks face many challenges as they strive to return to pre-2008 profit margins including reduced interest rates, unstable financial markets, tighter regulations and lower performing assets. Fortunately, banks taking advantage of big data and analytics can generate new revenue streams. Watch this real-life example of how big data and analytics can improve the overall customer experience. To learn more about IBM Big Data, visit http://www.ibm.com/big-data/us/en/ To learn more about IBM Analytics, visit http://www.ibm.com/analytics/us/en/
Views: 95241 IBM Analytics
▶️$168,361 How To Get Higher Limit Credit Cards, Learn the SECRET Learn About CARDMATCH
 
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▶️Learn to Leverage your credit and make your credit it work for you. $168,361 How To Get Higher Limit Credit Cards, Learn the SECRET Learn About CARDMATCH Check out CreditCards.com for CARDMATCH How to Remove Negative Credit Items / Collections + Credit Inquiries + Sample Letters PROVIDED, FREE DYI CREDIT REPAIR Link to Free Federal Credit Reports www.annualcreditreport.com Credit Repair Letter Provided by RandomFix https://drive.google.com/file/d/0B8YhYO1fFwFlM3RISnFKMEJXaG8/view?usp=sharing Credit Inquiry Removal by RandomFix https://drive.google.com/file/d/0B8YhYO1fFwFlYU5BU2JFSzRJMVU/view?usp=sharing Cool information about credit score A credit score is a numerical expression based on a level analysis of a person's credit files, to represent the creditworthiness of an individual. A credit score is primarily based on a credit report information typically sourced from credit bureaus. Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt. Lenders use credit scores to determine who qualifies for a loan, at what interest rate, and what credit limits. Lenders also use credit scores to determine which customers are likely to bring in the most revenue. The use of credit or identity scoring prior to authorizing access or granting credit is an implementation of a trusted system. Credit scoring is not limited to banks. Other organizations, such as mobile phone companies, insurance companies, landlords, and government departments employ the same techniques. Digital finance companies such as online lenders also use alternative data sources to calculate the creditworthiness of borrowers. Credit scoring also has much overlap with data mining, which uses many similar techniques. These techniques combine thousands of factors but are similar or identical. Give the Gift of Prime https://goo.gl/YJTEMn Thanks for your support. God Bless -RandomFIX www.RandomFIXWorld.com **If the video was helpful, remember to give it a and consider subscribing. New videos every Monday** How to get high limit credit cards fast good credit equal high credit limit cards
Views: 5157 RANDOMFIX
ICPR 2018: Probabilistic Sparse Subspace Clustering Using Delayed Association
 
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International Conference on Pattern Recognition 2018: Probabilistic Sparse Subspace Clustering Using Delayed Association Abstract—Discovering and clustering subspaces in high- dimensional data is a fundamental problem of machine learning with a wide range of applications in data mining, computer vision, and pattern recognition. Earlier methods divided the problem into two separate stages of finding the similarity matrix and finding clusters. Similar to some recent works, we integrate these two steps using a joint optimization approach. We make the following contributions: (i) we estimate the reliability of the cluster assignment for each point before assigning a point to a subspace. We group the data points into two groups of “certain” and “uncertain”, with the assignment of latter group delayed until their subspace association certainty improves. (ii) We demonstrate that delayed association is better suited for clustering subspaces that have ambiguities, i.e. when subspaces intersect or data are contaminated with outliers/noise. (iii) We demonstrate experimentally that such delayed probabilistic association leads to a more accurate self-representation and final clusters. The proposed method has higher accuracy both for points that exclusively lie in one subspace, and those that are on the intersection of subspaces. (iv) We show that delayed association leads to huge reduction of computational cost, since it allows for incremental spectral clustering. Maryam Jaberi, Department of Computer Science, University of Central Florida, Orlando, FL, USA Marianna Pensky, Department of Mathematics, University of Central Florida, Orlando, FL, USA Hassan Foroosh, Department of Computer Science, University of Central Florida, Orlando, FL, USA https://arxiv.org/abs/1808.09574
Views: 29 maryam9586
BIG Data and Hadoop Applications in Education
 
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Register here for FREE ACCESS to our BIG Data & Hadoop Training Platform: http://promo.skillspeed.com/big-data-hadoop-developer-certification-course/ This is a short video presentation on Applications of Big Data & Hadoop in Education. BIG Data & Hadoop in Education is revolutionizing how students learn, especially in terms of generating memorable and engaging experiences. Education industries use Big Data analytics for higher education analytics, to increase student’s engagement and also to improve bookstore effectiveness. The agenda of the session is as follows ✓ Introduction to BIG Data & Hadoop ✓ BIG Data & Hadoop Use Cases in Education ✓ Examples – Course Smart & iParadigms ✓ Deriving Insights from BIG Data ---------- What is BIG Data & Hadoop? Big Data refers to the vast amounts of unstructured data generated in today’s internet driven world which cannot be tapped, manipulated and utilized via traditional data harness tools. Apache Hadoop is an open-source JAVA based framework, which is used to harness & process BIG Data sets. It facilitates distributed parallel processing via cluster nodes to ensure a secure, scalabe & accurate data service solution. The framework consists of Hadoop Distributed File System (HDFS), Hive, Sqoop, Flume, Hbase, Pig, Yarn & ZooKeeper. This video will decipher Big Data in Education & Hadoop in Education. ---------- Examples of BIG Data & Hadoop in Education Course Smart Course Smart embeds analytics directly into digital textbooks. These analytics provide an “engagement index score,” which measures how much students are interacting with their etextbook. This ranges from tracking events such as text-highlighting, page-views, time spent on each page etc. The resultant engagement score index carries out the accurate predictions of student learning path outcomes. iParadigms It was founded by graduate student researchers at UC Berkeley who created software to monitor the recycling of papers in their large undergraduate classes. Encouraged by the interest from their peers, they assembled a team of instructors, mathematicians, and computer scientists to create the world's leading cloud-based plagiarism detection and online grading service. iParadigms products serve more than 10,000 institutions in 126 countries and process over 80 million submissions annually. ---------- Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance. Email: [email protected] Website: https://www.skillspeed.com Number: +91-90660-20904 Facebook: https://www.facebook.com/SkillspeedOnline Linkedin: https://www.linkedin.com/company/skillspeed
Views: 792 Skillspeed
Next in (Data) Science | Part 1 | Radcliffe Institute
 
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The Next in Science Series provides an opportunity for early-career scientists whose innovative, cross-disciplinary research is thematically linked to introduce their work to one another, to fellow scientists, and to nonspecialists from Harvard and the greater Boston area. This year’s program focuses on innovative applications of data science to a wide range of disciplines. The speakers’ talks demonstrate how data science approaches have become critical to a variety of fields, including social media, the movie industry, public health, and the study of the origins of our universe. Welcome and Introduction Alyssa A. Goodman, faculty codirector of the science program, Radcliffe Institute for Advanced Study, and Robert Wheeler Willson Professor of Applied Astronomy, Faculty of Arts and Sciences, Harvard University (5:55) “Uncovering Online Censorship and Propaganda in China” Jennifer Pan, assistant professor of communication and, by courtesy, of political science and sociology, Stanford University (31:16) “Hollywood Data Science: The Role of Inference and Prediction” Nathan Sanders, vice president of quantitative analytics, Legendary Entertainment
Views: 6271 Harvard University
The Data-Mining Revolution: MUM prepares students for the skills and jobs of the future
 
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http://www.mum.edu Prof. Anil Maheshwari, Ph.D., discusses the new immersion program Maharishi University of Management has just launched to train students in the next wave of data-mining software. In today's data-driven economy there is an urgent need for more sophisticated software programs to mine and better utilize data coming in over multiple platforms from diverse sectors of the economy, not only for business, but also for higher education. To help Maharishi University of Management students build essential skills in analytics technology, we recently joined the IBM Academic Initiative, which offers participating schools no-charge access to IBM software, discounted hardware, course materials, training and curriculum development—over 6,000 universities and 30,000 faculty members worldwide are members of the program. "We are using industrial strength tools such as IBM SPSS Modeler," Dr. Maheshwari said, "along with open-source tools, to provide our students a strong data-mining toolkit to engage with Big Data, and generate interesting insights and new knowledge." Students will learn more than just how to operate the software, but how to use it effectively as a business tool. Dr. Maheshwari said, "Our students will have end-to-end skills to discern what is the business problem, what is the data being generated, how do I mine the data, how do I generate intelligence out of it and feed it back to the business so the business can actually benefit from it. That whole cycle is what we're training, not just the tool itself." Industry analysts have identified predictive analytics as the fastest growing software category for company spending. They also expect that the need for staff with these capabilities will outpace available skill sets in many organizations. This will mean that expertise in data mining and predictive analytics will be highly sought after for years to come. "Having this kind of software suite on their resumes can be a big advantage for our students headed for IT/management jobs," said Dr. Maheshwari. For more videos about MUM, visit our Video Café: http://www.mum.edu/video-cafe At MUM, Consciousness-Based education connects everything you learn to the underlying wholeness of life. So each class becomes relevant, because the knowledge of that subject is connected with your own inner intelligence. You study traditional subjects, but you also systematically cultivate your inner potential developing your creativity and learning ability. Your awareness expands, improving your ability to see the big picture, and to relate to others. Maharishi University of Management (MUM) offers undergraduate and graduate degree programs in the arts, sciences, business, and humanities. The University is accredited through the doctoral level by the Higher Learning Commission. Founded in 1971 by Maharishi Mahesh Yogi, the University features Consciousness-Based education to develop students' inner potential. All students and faculty practice the Transcendental Meditation technique, which extensive published research has found boosts learning ability, improves brain functioning, and reduces stress. Maharishi University uses the block system in which each student takes one course at a time. Students report they learn more without the stress of taking 4-5 courses at once. The University has a strong focus on sustainability and natural health, and serves organic vegetarian meals. The B.S. in Sustainable Living is MUM's most popular undergraduate major. Maharishi University of Management: http://www.mum.edu Consciousness-Based education: http://www.mum.edu/cbe BS Sustainable Living: http://www.mum.edu/sustainable_living/ Transcendental Meditation: http://www.mum.edu/tm Research: http://www.mum.edu/tm_research Block system: http://www.mum.edu/cbe/block Sustainability: http://www.mum.edu/sustainability Natural health: http://www.mum.edu/cbe/natural_health Organic veg meals: http://www.mum.edu/campus/dining
Introduction to Data Mining: Euclidean Distance & Cosine Similarity
 
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In this Data Mining Fundamentals tutorial, we continue our introduction to similarity and dissimilarity by discussing euclidean distance and cosine similarity. We will show you how to calculate the euclidean distance and construct a distance matrix. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M8m0 See what our past attendees are saying here: https://hubs.ly/H0f8Lts0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 19724 Data Science Dojo
A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data
 
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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: 101 Clickmyproject
Performing Sentiment Analysis
 
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Ever wonder how to analyze people’s words to gauge sentiment? In this video, we’ll show you how! You’ll learn: - How to gather sentiment data - How to clean and structure it - How to perform the analysis in Tableau
Views: 8603 Tableau Software
Statistics intro: Mean, median, and mode | Data and statistics | 6th grade | Khan Academy
 
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This is a fantastic intro to the basics of statistics. Our focus here is to help you understand the core concepts of arithmetic mean, median, and mode. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/e/calculating-the-mean?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/v/mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/histograms/v/interpreting-histograms?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1885251 Khan Academy
Experiments in datamining ...  by Ekta Grover
 
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Talk at Python India 2013
Views: 572 Python India
Helping Higher Education Institutions Raise the Bar
 
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Blackbaud is committed to higher education institutions, helping them drive results and raise support through integrated cloud software, services, data intelligence, and over three decades of expertise. Discover how we support colleges and universities in making greater impacts through smart solutions and strong partnership.
Views: 654 Blackbaud
Master Innovation Research Informatics - Data Mining and Business Intelligence - FIB
 
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FIB Master's Degrees are official university studies within the framework of the European Higher Education Area (EHEA). Your degree is acknowledged all across the globe and it meets EU’s requirements. More information at: http://masters.fib.upc.edu/ The master empowers graduates with solid knowledge and hands-on experience on the techniques to manage, analyze and extract hidden knowledge from Big Data ensembles, either structured and unstructured, and to build adaptive Analytic systems able to exploit that knowledge in modern organizations. In particular the master addresses the new challenges of the smart society bloom: fraud detection, bioinformatics, extracting information from open linked data, real time analysis of sensor data and social networks, and customer relationship management,
Views: 2017 mediafib
Building Enigma / The largest Ethereum Mining Facility
 
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https://genesis-mining.com/pricing https://ethereum.org Ethereum is the first ‘world computer’. It is a decentralized network that can be used by anyone and is capable of running applications with no possibility of downtime, censorship or fraud. It's native currency, Ether is one of the fastest growing cryptocurrencies next to Bitcoin. Just a few months ago, the price was $1, then it shot up to $13 and today it has settled at just under $10. This rapid growth excited investors who were eager not to miss out on another hyper-growth investment opportunity. While some choose to invest in Ethereum directly, many are turning to Cloud Mining to enter the market. Our Enigma Farm is a computation cluster built for exactly this venture. If you are as fascinated by the Ethereum project as we are and want to participate, head over to our website and become a part of the project!
Views: 2018669 Genesis Mining
IS DISCORD REALLY THE BEST? - Voice Chat Platform Showdown
 
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The voice chat scene has long been dominated by TeamSpeak, Mumble, and Skype. Discord aims to change that, but is it really up to snuff? Freshbooks sponsor link: For your unrestricted 30 days free trial, go to https://www.freshbooks.com/techtips and enter in “Linus Tech Tips” in the how you heard about us section. bequiet sponsor link: Check out bequiet's white Dark Base Pro 900: http://geni.us/ltfG0ve Buy Microphones Amazon: http://geni.us/0Pqgh Newegg: http://geni.us/N16HC Discuss on the forum: https://linustechtips.com/main/topic/853977-is-discord-really-the-best-voice-chat-platform-showdown/ Download the recordings here: http://geni.us/wOMLuW Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/75969-linus-tech-tips-affiliates-referral-programs-and-sponsors Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/LinusTechTips/ Linus Tech Tips posters at http://crowdmade.com/linustechtips Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxmFU3lWY iTunes Download Link: https://itunes.apple.com/us/album/supernova/id936805712 Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnirvana Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 2207082 Linus Tech Tips
RITMO: Reinventing Urban Transportation and Mobility
 
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The RITMO project is led by Pascal Van Hentenryck, Seth Bonder Collegiate Professor of Industrial and Operations Engineering, and is funded by the Michigan Institute of Data Science and aims at reinventing urban transportation and mobility. It builds on two key enablers, connected and automated vehicles, and leverages the tremendous progress in data science to design and operate a new generation of on-demand urban transit systems. RITMO assembles a multi-disciplinary team of researchers, from computer science, industrial and operations engineering, medicine, the school of information, urban planning, and the transportation research institute. RITMO carries basic research in data science, from descriptive to predictive and prescriptive analytics, spanning research in data mining, machine learning, optimization, computational economics, marketing, and urban planning. RITMO also aims at deploying its results on significant case studies through the development of mobile applications and high-performance analytics over massive data sets. The project is partnering with the UM Parking and Transportation Services, the UM Information and Technology Services,, the UM advanced research computing technology services for the deployment of our technologies, and the Mobility Transformation Center. A first deployment on the UM campus should take place in 2017.
Business Analytics Course | A Roadmap to Business Analytics - Tools, Techniques & Applications
 
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#BusinessAnalyticsCourse | Business Analytics, the methodical exploration of organization’s data with an emphasis on statistical analysis, is a better career opportunity to earn more and give your career the right direction for success. Great Learning uploads videos that show you – a Roadmap to Business analytic – tools, techniques & applications. Learn a lot more about business analytics courses and its potential. Our videos are uploaded by industry’s experts after their experience and ways of learning more. Subscribe our channel and get videos on business analytics courses. #BusinessAnalytics #BusinessAnalyticsTutorial #GreatLearning #GreatLakes Visit https://greatlearningforlife.com our learning portal for more videos introducing you to business analytics, data science, machine learning and AI as well as full tutorials on advanced topics. A roadmap to Business Analytics. Learn about various tools and techniques in Business Analytics, supervised and unsupervised learning techniques, and which one to use for different variables. Know More about our analytics programs: PGP-Business Analytics: https://goo.gl/QEcWgw PGP-Big Data Analytics: https://goo.gl/Gr6DJR Business Analytics Certificate Program: https://goo.gl/x6MdH1 Dr. P K Viswanathan, Professor at Great Lakes Institute of Management shares a roadmap to Business Analytics. He talks about the supervised and unsupervised learning techniques, and which one to use for different kind of variables. About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube
Views: 127409 Great Learning
SAE J1939 Explained - A Simple Intro (2018)
 
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NEW: You can now get our updated J1939.DBC file below: https://www.csselectronics.com/screen/product/j1939-dbc-file-pgn-spn Looking for a simple intro to J1939? In this video we go through the basics, applications, PGNs, SPNs and more! For the written article go to: http://www.csselectronics.com/screen/page/simple-intro-j1939-explained SAE J1939 is the standard communications network for ECUs within commercial vehicles. This includes in particular heavy duty applications such as trucks, buses, foresting, mining, agriculture etc. J1939 is a higher layer protocol based on CAN bus and specifies e.g. how to handle multi-packet messages - and how to interpret raw data. In particular, J1939 defines standard Parameter Group Numbers (PGNs) and Suspect Parameter Numbers (SPNs). Armed with a J1939 data logger and the J1939-71 standard, one is able to go from raw J1939 CAN bus data to scaled engineering values on e.g. vehicle speed, RPM and more. In this intro, we run through the basics of the SAE J1939 standard incl. applications, message interpretation and considerations when choosing a J1939 data logger or J1939 interface. We also briefly touch upon J1939 DBC files, the J1939 request message and J1939 multi packet messages. For more articles like this, check out our INTEL page: http://www.csselectronics.com/screen/page/can-bus-articles-tools-cases ___________________________________________ At CSS Electronics, we offer powerful, simple and affordable CAN bus analyzers. Our CLX000 series doubles as both a powerful CAN logger with 8GB SD card and a CAN interface integrating with Wireshark. Features include advanced configuration options, DBC file data conversion support (incl. for J1939), real-time graphical plots - and much more. Pricing starts at 169 EUR with free shipping and 100% free software. For more details, check out http://www.csselectronics.com ! Music credits: PC ONE Monachine (Instrumental Acoustic)
Views: 49759 CSS Electronics
BCA COURSE DETAIL | SALARY AFTER BCA | After 12th BCA Information | Scope of BCA in India [HINDI]
 
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BCA COURSE DETAIL | SALARY AFTER BCA | After 12th BCA Information | Scope of BCA in India [HINDI] In this Video you will be going to see complete BCA Course detail in 2018-19 you will get to know is bca degree right choice for you . So do watch this video Completely if you are planning to do BCA . JOBS RELATED COURSES VIDEOS :- #Data Entry Typing Jobs - https://www.youtube.com/watch?v=Ys4R5y1GwmY&t=1s #How to Find Jobs in India - https://www.youtube.com/watch?v=epXQHiVjcG8&t=1s #Banking Jobs Details - https://www.youtube.com/watch?v=1DhtN43KGhs #How to get Job in Bank after 12th - https://www.youtube.com/watch?v=sA1KA25PG6w #Digitize India Platform video - https://www.youtube.com/watch?v=xGkyQsEQ8nk #Youtube Education Series - https://www.youtube.com/playlist?list=PLZDpJlybwPQ7zZidfjrx6QeQh1RADMZ5k WATCH OUR BEST VIDEOS RELATED TO EDUCATION ►Everything About MBA in India - https://goo.gl/wGD8NM ✔ ►Top College Rankings - https://goo.gl/LEFzun ✔ ►All about Investment Banking - https://goo.gl/Hk1rix ✔ ►Financial Certification in Hindi - https://goo.gl/sKPqod ✔ ►Internship and Apprenticeship Video in Hindi - https://goo.gl/RCBqBY ✔ ►MCA Course Detail Hindi - https://goo.gl/bxntn3 ✔ ►After 12th Best Courses for Science, Commerce and Arts - https://goo.gl/rVMcTR ✔ ►BCA Course Related Video in Hindi - https://goo.gl/wsCM2G ✔ ►BTech Course Related Video in Hindi - https://goo.gl/DqvfGF ✔ ►Fastest and Easiest Way to Learn English - https://www.youtube.com/watch?v=GF5OHAZcW0k ✔ ► How to Get Education LOAN in INDIA - https://www.youtube.com/watch?v=DoluUHBZ1zw ✔ ► MBA INDIA VS MBA ABROAD - WHICH IS BEST ? - https://www.youtube.com/watch?v=ufgd8pkvtjE ✔ ► Highest Paying Careers in India - https://www.youtube.com/watch?v=GF5OHAZcW0k ✔ WATCH OUR BEST VIDEOS RELATED TO INTERESTING FACTS & OPINIONS [HINDI] ►Padmavati controversy in 5 minutes :- https://www.youtube.com/watch?v=ar_orIiwQqU&t=2s ✔ ►North Korea vs USA Nuclear War :- https://www.youtube.com/watch?v=HrJkIqc3lB8&t=1s ✔ ►Kamlesh Viral Video Truth :- https://www.youtube.com/watch?v=J-t0u81Tpt0&t=2s ✔ ►Dangal Girl Zaira Wasim Issue :- https://www.youtube.com/watch?v=j9z7MHSKG14&t=28s ✔ ►Countries where Education is free for Indians - https://www.youtube.com/watch?v=qDNG6H5qRA0&t=2s ✔ BUY OUR RECORDING GEAR AT DISCOUNTED PRICES:- External Recording Blue Mic -http://amzn.to/2ynJOSn My Nikon Dslr d5600 - http://amzn.to/2ynN7sV My Collar Mic- http://amzn.to/2x3LAEf ABOUT US :- Praveen Dilliwala is a youth oriented Review Channel Where you will get Videos related to Education,Opinions, Jobs, Motivational, Interesting Facts and also I will share my experience about these things. Our Motto is to provide unbiased and right information so that you make informed decision. Follow us on : Facebook - https://www.facebook.com/PraveenDilliwala Twitter - https://twitter.com/praveendiliwala Instagram - https://www.instagram.com/praveendilliwala Subscribe Here- https://www.youtube.com/PraveenDilliwala
Views: 381752 Praveen Dilliwala
Graph Clustering Algorithms (September 28, 2017)
 
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Tselil Schramm (Simons Institute, UC Berkeley) One of the greatest advantages of representing data with graphs is access to generic algorithms for analytic tasks, such as clustering. In this talk I will describe some popular graph clustering algorithms, and explain why they are well-motivated from a theoretical perspective. ------------------- References from the Whiteboard: Ng, Andrew Y., Michael I. Jordan, and Yair Weiss. "On spectral clustering: Analysis and an algorithm." Advances in neural information processing systems. 2002. Lee, James R., Shayan Oveis Gharan, and Luca Trevisan. "Multiway spectral partitioning and higher-order cheeger inequalities." Journal of the ACM (JACM) 61.6 (2014): 37. ------------------- Additional Resources: In my explanation of the spectral embedding I roughly follow the exposition from the lectures of Dan Spielman (http://www.cs.yale.edu/homes/spielman/561/), focusing on the content in lecture 2. Lecture 1 also contains some additional striking examples of graphs and their spectral embeddings. I also make some imprecise statements about the relationship between the spectral embedding and the minimum-energy configurations of a mass-spring system. The connection is discussed more precisely here (https://www.simonsfoundation.org/2012/04/24/network-solutions/). License: CC BY-NC-SA 4.0 - https://creativecommons.org/licenses/by-nc-sa/4.0/
2018 IEEE Transaction Paper on Data Mining: Easy Paper to implement set 2
 
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Paper 21: Weak Classifier for Density Estimation in Eye Localization and Tracking. Paper 22: Segmentation- and Annotation-Free License Plate Recognition With Deep Localization and Failure Identification. Paper 23: Features Classification Forest: A Novel Development that is Adaptable to Robust Blind Watermarking Techniques. Paper 24: Single Image Super-Resolution via Adaptive Transform-Based Nonlocal Self-Similarity Modeling and Learning-Based Gradient Regularization. Paper 25: Steganography with Multiple JPEG Images of the Same Scene. Paper 26: Affine Non-local Means Image Denoising. Paper 27: Higher Order Dynamic Conditional Random Fields Ensemble for Crop Type Classification in Radar Images. Paper 28: Single Image Rain Streak Decomposition Using Layer Priors. Paper 29: Fractional Krawtchouk transform with an application to image watermarking. Paper 30: Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images. Paper 31: A Hierarchical Approach for Rain or Snow Removing in A Single Color Image. Paper 32: Contrast Enhancement Based on Intrinsic Image Decomposition.
Views: 139 Ashwini Cly
Big Data and Education | PennX on edX
 
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Enroll now: https://www.edx.org/course/big-data-education-pennx-bde1x Learn the methods and strategies for using large-scale educational data to improve education and make discoveries about learning. Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning. In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in the educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You’ll also gain experience with standard data mining methods frequently applied to educational data. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications. The course will discuss how to use each method to answer education research questions, and to drive intervention and improvement in educational software and systems. Methods will be covered at a theoretical level, and in terms of learning how to apply them using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results. What you'll learn Key methods for educational data mining How to apply methods using standard tools such as RapidMiner How to use methods to answer practical educational questions
Views: 1688 edX
Using Data to Analyze Learning
 
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Introduction to Educational Data Mining, Dr. Luc Paquette
Data Mining : Visualization with Tableau
 
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Home Assignment, the 2nd video.
Views: 196 Ahram Kang
But what *is* a Neural Network? | Deep learning, chapter 1
 
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Subscribe to stay notified about new videos: http://3b1b.co/subscribe Support more videos like this on Patreon: https://www.patreon.com/3blue1brown Or don't. It's your call really, no pressure. Special thanks to these supporters: http://3b1b.co/nn1-thanks Additional funding provided by Amplify Partners. For any early-stage ML entrepreneurs, Amplify would love to hear from you: [email protected] Full playlist: http://3b1b.co/neural-networks Typo correction: At 14:45, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy There are two neat things about this book. First, it's available for free, so consider joining me in making a donation Nielsen's way if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning! https://github.com/mnielsen/neural-networks-and-deep-learning I also highly recommend Chris Olah's blog: http://colah.github.io/ For more videos, Welch Labs also has some great series on machine learning: https://youtu.be/i8D90DkCLhI https://youtu.be/bxe2T-V8XRs For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville. Also, the publication Distill is just utterly beautiful: https://distill.pub/ Lion photo by Kevin Pluck If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. Music by Vincent Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 3742248 3Blue1Brown