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Data Mining E-Learning Www.CreateSmiths.Com
 
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Data Mining E-Learning at Www.CreateSmiths.Com by Blair Smith
Views: 244 site3e
Final Year Projects | E - Learning
 
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Final Year Projects | E - Learning More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.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:+91 967-774-8277, +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: 6164 ClickMyProject
Active Tutorship in Adaptive e-learning process using data mining tools
 
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Presentation at ICERI 2013 - Sevilla 18-20 November 2013 - Slides available: http://prezi.com/mypvz2pnomdt/active-tutorship-in-adaptive-e-learning-process-using-data-mining-tools/
Views: 202 Alessandro Pagano
classroom visualization through the eras + educational data  mining and E-learning
 
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visualization to a teacher situation through the eras of education, in order to illustrate the impact and the importance of using educational data mining technique
Views: 57 norahalhajri
Data Mining E-Learning at Www.CreateSmiths.Com
 
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Data Mining illustrates how students find information and record it on Google Sites. E-Learning at Www.CreateSmiths.Com by Blair Smith
Views: 159 site3e
Decision Tree 1: how it works
 
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Full lecture: http://bit.ly/D-Tree A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Each split corresponds to a node in the. Splitting stops when every subset is pure (all elements belong to a single class) -- this can always be achieved, unless there are duplicate training examples with different classes.
Views: 428001 Victor Lavrenko
Bob Hughes - Data mining elearning artefacts: the example of an IT module
 
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Visual Recording of parallel session at University of Brighton Research Conference 04/02/2011. Bob Hughes showing the results from analysing student submission and grade data across an online module. He looks at the implications, the difficulties and the ethics of using this kind of data. see: http://www.brighton.ac.uk/clt/pedagogic-research-conference-registration-page.html#RH Created using Brushes on the iPad, converted to 8fps Quicktime.
Views: 255 Katie Piatt
Data Mining Using R: Introduction to Data Mining Techniques | Machine Learning - ExcelR
 
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ExcelR Data Mining Tutorial for Beginners 2018 - Introduction to various Data mining unsupervised techniques namely Clustering, Dimension Reduction, Association Rules, Recommender System or Collaborative filtering, Network Analytics. Things you will learn in this video 1)What is DataMining 2)DataMining in Nutshell 3)Types of methods 4)DataMining process 5)Approaches 6)Types of Clustering Algorithms To buy eLearning course on DataScience click here https://goo.gl/oMiQMw To enroll for the virtual online course click here https://goo.gl/m4MYd8 To register for classroom training click here https://goo.gl/UyU2ve SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For Introduction to Clustering Analysis clicks here https://goo.gl/wuXN48 For Introduction to K-mean clustering click here https://goo.gl/PYqXRJ #ExcelRSolutions #DataMining#clusteringTechniques #datascience #datasciencetutorial #datascienceforbeginners #datasciencecourse ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Data Mining Techniques for Detecting Behavioral Patterns of Gifted Students in Online Learning
 
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The paper entitled "Data Mining Techniques for Detecting Behavioral Patterns of Gifted Students in Online Learning Environment (Case Study)" will be presented in the framework of the fourth edition of the international conference "The Future of Education" that will be held in Florence on 12 - 13 June 2014
Views: 221 PixelConference
eLearning: e-Learning Predictions for 2018
 
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Over the past year, the education sector has been buoyed by several key factors, including a growing recognition that as big data restructures work, there is an urgent need to rethink how education is delivered. Here are 5 specific e-learning areas and e-learning applications that will grow in 2018 https://news.elearninginside.com/elearning-predictions-for-2018/
Views: 407 eLearning Inside
Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning
 
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Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. Support us on Patreon, so we can bring you more cool Machine and Deep Learning Content :) https://www.patreon.com/ArduinoStartups ------------------------------------------------------------ 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 :)
Views: 27261 Augmented Startups
Why E-Learning?
 
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visit http://elearningpinas.biz
Views: 3178 Elearning Pilipinas
Machine Learning #81 Frequent Itemset Mining
 
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Machine Learning #81 Frequent Itemset Mining In this lecture of machine learning we are going to see frequent itemset mining. In frequent itemset mining tutorial we will see some examples of frequent itemset mining algorithm. Frequent itemset mining is a branch of data mining works by looking at sequences of events or action, for example the order in which a normal human being get dressed. Usually Shirt first? Pants first? Socks may be the second item or second shirt if its winter? In frequent itemset mining, the base data takes the form of sets of transactions that each has a number of items. Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE) Best Programming Courses @ https://goo.gl/MVVDXR Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if MATLAB Tutorials @ https://goo.gl/EiPgCF
Views: 288 Xoviabcs
Difference Between Data Mining and Machine Learning
 
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Difference between machine learning and data mining . , . . . . Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly here are some more compilation of topics and latest discussions relates to this video, which we found thorough the internet. Hope this information will helpful to get idea in brief about this. These are aspects of data science that are closest to machine learning. Is a nice bit about the difference between ml and data mining on machine learning data mining is an area that has taken much of its inspiration and techniques from machine learning (and some, also, from statistics), but is put below information will help you to get some more though about the subject i am new to this area. In my image,. Data mining means to retrieve also, data mining is often considered a sub field of machine learning machine learning and data mining are research areas of computer science whose quick development is due the major difference between oltp and olap what is the difference between artificial intelligence, machine learning, statistics, and data mining. Posted by shakthydoss on june th, . Few anyway if you want for more info, you would better continue reading. Over time, we will see deeper connection between data mining and machine learning. Could they become twins one day? only time will tell chandrabhanurastogi utc #. I am very much confused in understanding machine learning, data analysis, data mining, data science to search this space of possibilities, machine learning techniques are correct use of term data mining is that it is part of process concerned another important difference look for causal relationships between environment and disease . When talking about artificial intelligence and machine learning, public a quick education on the difference between data mining, artificial machine learning is sometimes conflated with data mining, although that focuses the difference between the two fields arises from the goal of generalization the process of machine learning is similar to that of data mining. Both systems search the difference between machine learning and statistics in data mining discover the difference between machine learning and statistics and find out how generalization as search can be a data mining tool. Learn about the bias of the what are the differences between data science, data mining, machine learning, statistics, operations research, and so on? here i compare or spam (unwanted email), and the algorithms learn to distinguish between them automatically. Machine learning is a diverse and exciting field, and there are . From quora what are some good jokes in the machine learning community? what is the difference between statistics, machine learning, ai and data mining?. What's the difference between machine learning, deep learning, big data, statistics, decision & risk analysis, probability, fuzzy logic, and all the what's the difference between machine learning, deep learning, big data, statistics, decision & risk analysis, probability, fuzzy logic, and all the Most Discuss Difference between machine learning and data mining more interesting heading about this are what is the difference between data analytics, data analysis, data what is the difference between data mining, statistics, machine below topics also shows some interset as well analytics difference between data mining and machine learning m
Views: 17000 James Aldwin
K-Fold Cross Validation - Intro to Machine Learning
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 121079 Udacity
Machine Learning | Volume 3| Data Mining
 
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Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
Views: 41 Tarah Technologies
Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial
 
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Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Hey guys and welcome to another fun and easy machine tutorial on Eclat. Today we are going to be analyzing what video games get sold more frequently using an associated rule algorithm called Eclat. The Eclat algorithm which is an acronym for Equivalence CLAss Transformation is used to perform itemset mining. Itemset mining let us find frequent patterns in data like if a consumer buys Halo, he also buys Gears of War. This type of pattern is called association rules and is used in many application domains such as recommender systems. In the previous lecture we discussed the Apriori Algorithm. Eclat is one of the algorithms which is meant to improve the Efficiency of Apriori. Eclat is a depth-first search algorithm using set intersection. It is a naturally elegant algorithm suitable for both sequential as well as parallel execution with locality-enhancing properties. It was first introduced by Zaki, Parthasarathy, Li and Ogihara in a series of papers written in 1997. Support us on Patreon, so we can bring you more cool Machine and Deep Learning Content :) https://www.patreon.com/ArduinoStartups ------------------------------------------------------------ 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 :)
Views: 2962 Augmented Startups
Curse of Dimensionality - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-666010252/m-672718832 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 11912 Udacity
Linear Regression - Machine Learning Fun and Easy
 
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Linear Regression - Machine Learning Fun and Easy https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. Dependent Variable – Variable who’s values we want to explain or forecast Independent or explanatory Variable that Explains the other variable. Values are independent. Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents. And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways Used for 2 Applications To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables- • To see how increase in sin tax has an effect on how many cigarettes packs are consumed • Sleep hours vs test scores • Experience vs Salary • Pokemon vs Urban Density • House floor area vs House price Forecast new observations – Can use what we know to forecast unobserved values Here are some other examples of ways that linear regression can be applied. • So say the sales of ROI of Fidget spinners over time. • Stock price over time • Predict price of Bitcoin over time. Linear Regression is also known as the line of best fit The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x You most likely learnt this in school. So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis. M is your slope or gradient, if you change this, then your line rotates along the intercept. Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series. So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e 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: 82033 Augmented Startups
What is Learning Analytics | Levels of Learning Analytics | Who are the Key Beneficiaries
 
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What is Learning Analytics,4 Levels of Learning Analytics, e-learning Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. A related field is educational data mining. The 4 Levels of Learning Analytics: Descriptive : What has happened? Look at facts, figures, and other data that give you a detailed picture. Diagnostic: Why did it happen? Examining the descriptive elements allows you to critically assess why an outcome happened. Predictive : What will happen? Given the same or different elements, what would the outcome be? Prescriptive : What should I do? How can a specific outcome be achieved through use a specific elements? 5 Reasons Why Learning Analytics Are Important For e-Learning: Helps to predict learners’ performance Provides learners with a personalized e-Learning experience Increased learners’ retention rates Helps to improve future e-Learning courses Boost in cost efficiency ....... Our Mantra: Information is Opportunity. Knowledge is Power. Be Informed - Be Powerful! SUPPORT US: SUBSCRIBE / LIKE / SHARE / COMMENT :) Subscribe Link: https://goo.gl/qbyzFb ....... CONNECT US: Website: http://www.simplyinfo.net Facebook: https://www.facebook.com/SimplyInfo.net Twitter: https://twitter.com/SimplyInfo9 YouTube: https://www.youtube.com/c/SimplyInfo9 Slideshare: https://www.slideshare.net/SimplyInfo9 Pinterest: https://in.pinterest.com/SimplyInfo9/ Instagram: https://www.instagram.com/simplyinfo9/ YouTube Subscribe Link: https://goo.gl/qbyzFb ....... OTHER PLAYLISTS TO EXPLORE: Games & Sports: https://goo.gl/uTXRWB Jobs & Career Info: https://goo.gl/cbCDXy Business Management: https://goo.gl/1sDjfW Information Technology: https://goo.gl/nWYpK8 Physics Concepts: https://goo.gl/FnLmes Education & Learning: https://goo.gl/54TR8A Filmmaking Concepts: https://goo.gl/RQL5qn Psychology Concepts: https://goo.gl/oYNNKA Indian Law Concepts: https://goo.gl/m98pWn Economics Concepts: https://goo.gl/yymX98 ....... About Simplyinfo.net: We provide the best info bytes videos in a very simple and effective way to learn, to revise and to master micro-content information. We simplify information in a wide variety of categories. Contact Us: [email protected] Be Blessed with Love, Health & Happiness. Cheers & Have Fun :) Team SimplyInfo.net P.S. CLICK BELOW LINK TO SUBSCRIBE FOR UPDATES. SUBSCRIBE LINK: https://goo.gl/qbyzFb
Views: 20 SimplyInfo
Advanced Machine Learning for E-Learning Application
 
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Advanced Machine Learning for E-Learning Application (2) -- Code : https://github.com/sasimijournal/advancedmachine/
Views: 104 Samuel Matthew
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: 324824 CrashCourse
23: Mahalanobis distance
 
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Multivariate distance with the Mahalanobis distance. Using eigenvectors and eigenvalues of a matrix to rescale variables.
Views: 43507 Matthew E. Clapham
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 65737 WekaMOOC
Machine Learning - Supervised VS Unsupervised Learning
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
Views: 48528 Cognitive Class
Linear Regression Algorithm | Linear Regression in R | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka Linear Regression tutorial will help you understand all the basics of linear regression machine learning algorithm along with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial: 1) Introduction to Machine Learning 2) What is Regression? 3) Types of Regression 4) Linear Regression Examples 5) Linear Regression Use Cases 6) Demo in R: Real Estate Use Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LinearRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 53226 edureka!
R Machine Learning Algorithms in Tableau - Data Science Online Instructor Led Course
 
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This 10 hour course will teach you all the required skills needed to do the data analytics in Tableau using R. We'll use R Algorithm in Tableau for prediction, classification and sentiment analysis. Course fee is INR 5000 and first 15 participants will get 50% discount. For any queries contact [email protected] HURRY UP!!! BUY THE COURSE NOW from the link below!!! http://datantools.com/trainings/ Course Timining and Fees This Course will run from 24 to 28 Sep and from 9 AM to 11 AM IST. After the end of the course you'll get 3 projects to complete in two weeks and after successful evaluation, you'll get certificate of achievement. Course fees is INR 5000. (First 15 participants will get 50% discount)​ Who is it for? The course is aimed at anyone who wants to use R Algorithms in Tableau for doing the data mining work. I'll cover how you can call R's Regression, Time Series, Classification and Sentiment Analysis algorithms in Tableau and creating stunning dashboard for your end users.​
Machine Learning :  Introduction (in Hindi)
 
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Machine Learning Machine learning is a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions. Besides CS and AI, it has strong ties to statistics and optimization, which deliver both methods and theory to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning, data mining, and pattern recognition are sometimes conflated. Machine learning tasks can be of several forms. In supervised learning, the computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. Spam filtering is an example of supervised learning. In unsupervised learning, no labels are given to the learning algorithm, leaving it on its own to groups of similar inputs (clustering), density estimates orprojections of high-dimensional data that can be visualised effectively. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end. Topic modeling is an example of unsupervised learning, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics. In reinforcement learning, a computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Definition In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”. Tom M. Mitchell provided a widely quoted, more formal definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”. This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in Turing's paper “Computing Machinery and Intelligence” that the question “Can machines think?” be replaced with the question “Can machines do what we (as thinking entities) can do?” Generalization: A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows: 1. Machine learning focuses on prediction, based on known properties learned from the training data. 2. Data Mining focuses on the discovery of (previously)unknown properties in the data. This is the analysis step of Knowledge Discovery in Databases. The two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. On the other hand, machine learning also employs data mining methods as “unsupervised learning” or as a preprocessing step to improve learner accuracy. Human Interaction Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine
Views: 22003 sangram singh
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Machine Learning Algorithms Tutorial shall teach you what machine learning is, and the various ways in which you can use machine learning to solve a problem! Towards the end, you will learn how to prepare a dataset for model creation and validation and how you can create a model using any machine learning algorithm! In this Machine Learning Algorithms Tutorial video you will understand: 1) What is an Algorithm? 2) What is Machine Learning? 3) How is a problem solved using Machine Learning? 4) Types of Machine Learning 5) Machine Learning Algorithms 6) Demo Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #MachineLearningAlgorithms #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 128260 edureka!
Valentina Poggioni – Machine learning: ma come fanno le macchine ad imparare?
 
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Ricercatrice presso il Dipartimento di Matematica e Informatica dell’Università degli Studi di Perugia. Si occupa d’intelligenza artificiale e data mining. Ѐ docente presso il corso di Laura Magistrale in Informatica Basi di dati su larga scala e Data Mining. Partecipa a progetti europei su tecniche di didattica innovativa e creatività digitale. Data e luogo dell'evento Data: 30/04/2017 ore 17:15 Luogo: Monastero Sant’Anna - Sala Beata Angelina - Foligno
Where to use training vs. testing data - Intro to Machine Learning
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 12704 Udacity
SAS Visual Data Mining and Machine Learning – Interactive Interface
 
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http://www.sas.com/dmml Boost analytical productivity and solve your most complex problems faster with a single, integrated in-memory environment that's both open and scalable. SAS® VISUAL DATA MINING AND MACHINE LEARNING An intuitive programming environment. Innovative algorithms. Fast, in-memory processing. SAS Visual Data Mining and Machine Learning shatters barriers related to data volume and variety, limited analytical depth and computational bottlenecks. That means greater productivity – and faster, deeper insight. http://www.sas.com/dmml SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 4702 SAS Software
Introduction to Marketing Analytics | Online Course | E-Learning University of Athens
 
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This programme addresses current issues on marketing analytics while ensuring an understanding of the basic notions and principles of business intelligence. In particular the course aims to present the most basic data mining techniques that are utilized for the classification and the displacement of a company’s target customers. After completing the course, the student will be aware of the useful data that should be collected for costumers while he/she will become familiar with up to date directed and non-directed data mining methodologies. Music: Reggae Rasta Guitar by Abet is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/3.0/#) http://freesound.org/people/abett/sounds/320241/
More Data Mining with Weka (5.3: Learning curves)
 
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More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 3: Learning curves http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 6876 WekaMOOC
Machine learning with Python and sklearn - Hierarchical Clustering (E-commerce dataset example)
 
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In this Machine Learning & Python video tutorial I demonstrate Hierarchical Clustering method. Hierarchical Clustering is a part of Machine Learning and belongs to Clustering family: - Connectivity-based clustering (hierarchical clustering) - Centroid-based clustering (K-Means Clustering) - https://www.youtube.com/watch?v=iybATqk6LNI - Distribution-based clustering - Density-based clustering In data mining and statistics, Hierarchical Clustering also called hierarchical cluster analysis or HCA is a method of cluster analysis which seeks to build a hierarchy of clusters. In this video I demonstrate how Agglomerative Hierarchical Clustering is working. Must know for Hierarchical Clustering is knowing Dendrograms. Dendrogram helps you to decide the optimal number of clusters for your dataset. For executing task in Python I used: - sklearn library that is for Machine Learning algorithms. - ward method that means Minimum Variance Method. If you are interesting more in Hierarchical Clustering, read my article on LinkedIn where I described my experiment about combining Machine Learning (Hierarchical Clustering) in GIS (Geographical Information System). - https://www.linkedin.com/pulse/machine-learning-gis-hierarchical-clustering-urban-bielinskas Data-set for this example is taken from https://www.kaggle.com. There you can find many dataset for very different Machine Learning tasks. Hierarchicaal Clustering is very usable in solving Data Analysis, Data Mining and Statistics problems. If you have any question or comments please write below. Do not forget to subscribe me if want to follow my new videos about Machine Learning, Data Science, Python programming and relative issues. Follow me on LinkedIn: https://www.linkedin.com/in/bielinskas/
Views: 2007 Vytautas Bielinskas
Publish an eLearning Article on eLearning Industry
 
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At eLearning Industry we can connect you with an audience that is interested in your ideas. With over 600,000 unique monthly visitors, we can ensure that you not only add value to the community but also get exposure in the most relevant and organic way. We’ve published thousands of articles, written by hundreds of authors, many of whom are global eLearning Industry leaders. By publishing their articles and sharing their expertise, they’ve been able to promote their business and their brand. And they’re not just adding value to the eLearning field, they’re also getting amazing ROI from their published articles. Results that you can also enjoy by becoming one of our Top eLearning Authors. Find more at https://elearningindustry.com/post-here
Views: 1061 eLearning Industry
Difference between Classification and Regression - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-313488098/m-674518790 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 60603 Udacity
Machine Learning with Weka - regression and clustering
 
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This is a walkthrough of the IBM weka tutorials covering regression and clustering https://www.ibm.com/developerworks/library/os-weka1/ https://www.ibm.com/developerworks/library/os-weka2/ https://www.ibm.com/developerworks/library/os-weka3/
Views: 6037 jengolbeck
Data and Laptop Security E-learning
 
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Planet Wise, Data & Laptop Security elearning course summary. Promotional Video. Visit www.planetwisegroup.com for more information.
Views: 201 PlanetWisetv
Detecting Phishing Websites using Machine Learning Technique
 
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Get this project at http://nevonprojects.com/detecting-phishing-websites-using-machine-learning/ In order to detect and predict phishing website, we proposed an intelligent, flexible and effective system that is based on using classification Data mining algorithm
Views: 8773 Nevon Projects
Multiple Regression in Hindi under E-Learning Program
 
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It covers in details the meaning of Multiple Regression, various methods of framing Multiple Regression Equations and Standard Error of Estimate in Multiple Regression. Lecture by: Prof. Rajinder Kumar Arora, Head of Department (Commerce & Management)
Interview with a Data Analyst
 
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This video is part of the Udacity course "Intro to Programming". Watch the full course at https://www.udacity.com/course/ud000
Views: 267408 Udacity
Introducción a Big Data, Machine Learning y Data Mining
 
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#SesiónBs ¡Compartimos nuestra primera sesión con ustedes! Programa Desarrollo de Aplicaciones Big data y Machine Learning: Primera sesión "Introducción a Big Data, Machine Learning y Data mining". Más Información: https://goo.gl/GNhhJD
Views: 785 BS Grupo
Advanced Data Mining with Weka (2.5: Classifying tweets)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Classifying tweets http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3198 WekaMOOC
Data Warehousing Tutorial - 1 | Data Warehousing Tutorial for Beginners - 1 | Edureka
 
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***** Data Warehouse & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** A data warehouse is a central location where consolidated data from multiple locations are stored. It usually contains historical data derived from transaction data but it can include data from other sources. Topics covered in the Video: 1.What is Datawarehouse? 2.Data warehouse Architecture 3.Why Data warehouse is used? 4.What is ETL? 5.What all you will learn in Data warehousing and ETL course? 6.Hands on Watch the sample class recording: http://www.edureka.co/data-warehousing-and-bi?utm_source=youtube&utm_medium=referral&utm_campaign=datawarehouse Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Introduction to Dataware Housing’ have been covered in our course ‘Datawarehousing‘. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004"
Views: 205525 edureka!
productronica 2017 - Process Optimizing through Data Mining and Machine Learning
 
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Florian Schwarz: "A warm welcome to productronica 2017. The special shows here are a big highlight – because that's where you can experience electronics manufacturing live!" "Founded in April 2017: the research fab Microelectronics Germany. This is where research capacities all over the country are bundled together and connected, to give the fab more weight internationally as a centre for microelectronics."   "Ah, Dr. Olowinsky. Hello!"    "Laser microwelding. What exactly are we looking at here?"   Dr. Alexander Olowinsky: "Laser microwelding is an established method in electronics and precision engineering for creating electrical and mechanical connections.Here you can see a laser beam melting material – and that's what creates the connection. In this particular version, the laser head contains the beam guidance, beam forming and mechanical pressing combined, for a flexible manufacturing process."   Florian Schwarz: "And what are the areas of application?"   Dr. Alexander Olowinsky: "What you see here: classic battery technology, production of battery modules and of battery packs, production of electrical connections,all the way to printed circuit board technology, because we need to create connections there too."   Florian Schwarz: "Dr. Olowinsky, thanks a lot!" Florian Schwarz: "From microelectronics to the special show devoted to hardware data mining.With me now is Ulf Oestermann, business developer at Fraunhofer IZM.Good morning!"   FlorianSchwarz: "Mr. Oestermann, what's the connection between microelectronics and hardware data mining?"   Ulf Oestermann: "The research fab Microelectronics Germany supposed to develop technologies and processes for the future. And they then have to be ported into mass production and scaled, so that they're ready to use there. That's exactly what hardware data mining is all about – showing what data records accumulate at what location in the individual process steps, and how robust they have to be in order to be used."   Florian Schwarz: "So we're talking about 'digging' data? Can we take a closer look?"   Ulf Oestermann: "Sure. No problem."   Ulf Oestermann: "Based on the data matrix code, you can immediately establish when this subassembly was manufactured, at what temperature, and in what humidity, and then conclusions can be drawn about possible errors."   Florian Schwarz: "I guess it helps save on resources – only having to replace individual components?"   Ulf Oestermann: "It's showing how thick wire is bonded. A very, very large number of wires are needed to get a high current density in the contact."   Florian Schwarz: "Mr. Oestermann, thanks very much for the tour. Hardware data mining. I'm going to the VDMA now to see what's being done with the data. And you? Back to work?"   Ulf Oestermann: "That's right!"   Florian Schwarz: "Ok - thanks. Ciao! We've just mined and collected the data. The data has to go somewhere, it has to be processed. And that brings me to the special show of the VDMA: "Smart-Data-Future Manufacturing."   "With me now is Mr. Müller from the VDMA. I've just taken a look round your stand. There's a lot of data being generated here. What's going to be done with it?"    Daniel Müller: "In the next stage, it's simply stored in various cloud systems, to make the long-term data actually usable. For models, for instance – like predictive maintenance."   Florian Schwarz: "Smart Data. How do you see the future of that?"   Daniel Müller: "A very exciting future topic is machinelearning - where companies try to make machines learn. So they can avoid errors, or correct them, all by themselves."   Florian Schwarz: "Wow. Thank you very much, Mr. Müller! Smart Data Future Manufacturing – it's a topic we're going to keep a close eye on. Well, that's all from productronica 2017. I'm already looking forward to 2019! Goodbye!"
Views: 298 productronica
Mining Industry Skills Centre - eLearning
 
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These adult training materials have been developed for the Mining Industry Skills Centre. The project encompassed refresher courses and training resources for a variety of mine types, from Coal Surface to Metalliferous Underground mining. Combining elements of video, animation and photography, the application is a fully interactive learning experience. http://www.liquidinteractive.com.au
Views: 322 LiquidInteractive1