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Search results “Data mining independent variables examples”

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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 6314 5 Minutes Engineering

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Description of categorical variables and a comparison to quantitative and ordinal variables.
Views: 24960 Stephanie Glen

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Views: 124826 Augmented Startups

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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 848576 Dr Nic's Maths and Stats

09:18

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This video discusses the role of the Adjusted R-Squared in helping us determine which variables should be used in multiple regression models. TABLE OF CONTENTS: 00:00 Introduction 00:10 The Variable Selection Problem 00:53 Strategies for Variable Selection 01:38 Data for Example: Vehicle’s MPG 02:48 Strategy 1 03:48 Model 1: Using All Variables 04:32 Model 2: Removing Acceleration 05:04 Model 3: Removing Acceleration and Cylinders 05:27 Problems with Strategy 1 06:18 Objectives of Regressions 08:06 Fixing or Adjusting the R2 08:48 The Adjusted R-Squared 09:57 Model 1: Using All Variables 10:38 Model 2: Removing Acceleration 11:06 Model 3: Removing Acceleration and Cylinders 11:38 Model 2 is our Best Model with the Max. R2 11:48 You are not done…
Views: 32860 dataminingincae

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Learn how to make predictions using Simple Linear Regression. To do this you need to use the Linear Regression Function (y = a + bx) where "y" is the dependent variable, "a" is the y intercept, "b" is the slope of the regression line, and "x" is the independent variable. This video also shows you how to determine the slope (b) of the regression line, and the y intercept (a). In order to determine the slope of a line you will need to first determine the Pearson Correlation Coefficient - this is described in a separate video (https://www.youtube.com/watch?v=2SCg8Kuh0tE).
Views: 438938 Eugene O'Loughlin

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Categorical Data Vs. Numerical Data
Views: 27944 TheWybrary

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Decision Tree (CART) - Machine Learning Fun and Easy ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression (CART). So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 125215 Augmented Startups

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Provides an example of student college application for carrying out logistic regression analysis with R. Data: https://goo.gl/VEBvwa R File: https://goo.gl/PdRktk Machine Learning videos: https://goo.gl/WHHqWP Includes, - use of a categorical binary output variable - data partition - logistic regression model - prediction - equation for prediction - misclassification errors for training and test data - confusion matrix for training and test data - goodness-of-fit test R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 22374 Bharatendra Rai

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Overview of multiple regression including the selection of predictor variables, multicollinearity, adjusted R-squared, and dummy variables. If you find these videos useful, I hope that you will consider signing up for my online statistics workshop on Udemy, which contains additional videos and lots of problems to help you apply and reinforce the important concepts: https://www.udemy.com/statshelp/?couponCode=coefficient
Views: 173617 George Ingersoll

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Provides perturbation analysis with r, and includes, - linear model and vif - Perturbation Analysis with Numerical Independent Variables - Perturbation Analysis with Numerical & Categorical Independent Variables - Making Estimates Repeatable - Reclassification Probabilities R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 1130 Bharatendra Rai

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This video will show you how to make a simple scatter plot. Remember to put your independent variable along the x-axis, and you dependent variable along the y-axis. For more videos please visit http://www.mysecretmathtutor.com
Views: 194727 MySecretMathTutor

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Views: 329811 NurseKillam

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Dr. Manishika Jain in this lecture explains the meaning of variables and explain the 28 types of variables: Attribute or Quality Differ in magnitude Control of Variables IV, DV, Mediating Variable IV: Characteristic of experiment that is manipulated DV: Variable measured Mediating/intervening – hypothetical concept explain relation b/w variables (Parent’s status - child’s status by education) Confounding – extra variable (effect of activity on obesity – AGE) Dig! Dig! Effect of noise on test score IQ varies with age Quantitative vs. Qualitative Quantitative: Numbers (Interval/ratio) Qualitative: attitude (good or bad) – can be compared not measured (nominal/ordinal) Variables based on Scaling Continuous, Discrete & Categorical Variable Absolute vs. Relative Absolute: Meaning doesn’t imply reference to property of others Relative: Relationship b/w persons and objects Global, Relational & Contextual Global: Only to the level at which they are defined Relational: Relationship of a unit Contextual: Super-unit (all at lower level receive same value) – disaggregation Analytical & Structural: From lower level data – aggregation Active: Can be manipulated – experimental Attribute: Pre-existing quality Binary/Dichotomous: pass/fail (0 - 1) Endogenous Exogenous Dummy – record categorical variable in series of binary variable Latent – cannot be observed (intelligence) Manifest – indicates presence of latent (IQ score) Polychotomous – with 2 or more possible values For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm Variables @0:16 Control of Variables @0:38 IV, DV, Mediating Variable @4:26 Quantitative vs. Qualitative @6:28 Quantitative @6:31 Qualitative @6:53 Variables based on Scaling @7:16 Continuous, Discrete & Categorical Variable @11:10 Absolute vs. Relative @12:28 Global, Relational & Contextual @13:05 #Contextual #Categorical #Categorical #Scaling #Measured #Confounding #Hypothetical #Mediating #Independent #Variables #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm
Views: 72822 Examrace

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A quick look at the differences between continuous data and discrete data including examples.
Views: 130820 Fast Math

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Hello everyone, In this video, we will be discussing the types of algorithms in Machine Learning. There are many algorithms in Machine Learning and many more are yet being developed. But all of the Machine Learning algorithms can be classified into 3 main categories. And they are 1) Supervised Learning 2) Un Supervised Learning, and 3) Reinforcement Learning Supervised learning: Supervised Learning algorithms are used when the given data has both independent variable/s and target variable. And the task of the supervised learning algorithm is to find the relationship independent variable/s and the target variable. Let's try to understand this with a simple example. Let's assume we have some data about the hours studied by students and the marks scored by them in the exam. Here the hours studied is the independent variable and marks scored is the target variable. We are required to find what kind of relationship exists between the hours studied and marks scored. If we just plot the given data on the graph, we can see that the marks scored increases as the number of hours studied increases. Also we can see that there is linear relationship between the input and target variables. Thus the given data has a linear relationship which is of the form y = mx +c In Supervised learning, again there are two categories: 1) Algorithms for Regression type problems 2) Algorithms for Classification type problems When the target variables have continuous values, Regression type algorithms can be used. Example, the data of hours studied and marks scored. Here the marks scored is a continuous value like 30, 42.5, 53 etc., Thus, here regression type algorithms should be used. When the target variables have discrete or fixed set of values, we can use classification type algorithms. For example, if we have the data of hours studied and passed in exam or not, the target variable passed in exam or not has only 2 values pass or fail. Thus, we can use classification type algorithms here. Some of the popular supervised learning algorithms are linear regression, logistic regression, k nearest neighbours, support vector machines, decision trees, random forests and naïve bayes. Unsupervised Learning: Unsupervised Learning algorithms can be used when there are no target variables. In this case we are not trying to predict anything. We are just trying to find the pattern in the data if there are any. Let's take a simple example to understand this. We have the data which has two independent variables. One is Gender and other is Interested in either beauty products or gaming products. We don’t have any target variable in this case. If we can visualize this data, this is how it looks. We can observe some sort of pattern here. Many males are interested in gaming products. Many females are interested in beauty products. Very few females are interested in gaming products and very few males are interested in the beauty products. Basically when we feed this data to an unsupervised learning algorithm, the algorithm will simply find this pattern and output four clusters from this data and also given some random names to these clusters like cluster 1 , cluster 2 , cluster 3 and cluster 4. Once the pattern is ready, it can be very helpful. For example, let's assume we have a beauty product for men. We can easily target this product for cluster 4 instead of targeting on all the people. This will reduce the advertising costs significantly. Some of the popular unsupervised learning algorithms are KMeans Clustering, Heirarchical clustering and Single Linkage Clustering. Reinforcement learning: Reinforcement learning is all about letting an agent to interact in an environment. The agent will be rewarded for doing positive work and punished for doing negative work in the environment. Lets assume you are training your dog to do certain trick. Whenever your dog does the trick right, you will reward it by giving some treat. If the dog does the trick wrong, you will punish it by not giving any treat. The dog will understand this and tries to perform the trick correctly so as to maximize the positive reward.
Views: 511 Art of Engineer

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Full lecture: http://bit.ly/D-Tree Decision trees are interpretable, they can handle real-valued attributes (by finding appropriate thresholds), and handle multi-class classification and regression with minimal changes.
Views: 48752 Victor Lavrenko

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An explanation of how to compute the chi-squared statistic for independent measures of nominal data. For an explanation of significance testing in general, see http://evc-cit.info/psych018/hyptest/index.html There is also a chi-squared calculator at http://evc-cit.info/psych018/chisquared/index.html
Views: 926330 J David Eisenberg

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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 4: Fast attribute selection using ranking http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 15235 WekaMOOC

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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 1: Attribute selection using the "wrapper" method http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 15350 WekaMOOC

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Which type of data is best? Attribute (Pass/fail) or Variable data. This video explains...
Views: 2253 Paul Allen

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Ml full notes rupees 200 only ML notes form : https://goo.gl/forms/7rk8716Tfto6MXIh1 Machine learning introduction : https://goo.gl/wGvnLg Machine learning #2 : https://goo.gl/ZFhAHd Machine learning #3 : https://goo.gl/rZ4v1f Linear Regression in Machine Learning : https://goo.gl/7fDLbA Logistic regression in Machine learning #4.2 : https://goo.gl/Ga4JDM decision tree : https://goo.gl/Gdmbsa K mean clustering algorithm : https://goo.gl/zNLnW5 Agglomerative clustering algorithmn : https://goo.gl/9Lcaa8 Apriori Algorithm : https://goo.gl/hGw3bY Naive bayes classifier : https://goo.gl/JKa8o2
Views: 32144 Last moment tuitions

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Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: 1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php
Views: 466301 Bozeman Science

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Also known as a "Goodness of Fit" test, use this single sample Chi-Square test to determine if there is a significant difference between Observed and Expected values. This video shows a step-by-step method for calculating Chi-square.
Views: 385410 Eugene O'Loughlin

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Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation. 0:00 Introduction to bivariate correlation 2:20 Why does SPSS provide more than one measure for correlation? 3:26 Example 1: Pearson correlation 7:54 Example 2: Spearman (rhp), Kendall's tau-b 15:26 Example 3: correlation matrix I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation. Watch correlation and regression: https://youtu.be/tDxeR6JT6nM ------------------------- Correlation of 2 rodinal variables, non monotonic This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative. Good luck
Views: 506769 Phil Chan

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Predict who survives the Titanic disaster using Excel. Logistic regression allows us to predict a categorical outcome using categorical and numeric data. For example, we might want to decide which college alumni will agree to make a donation based on their age, gender, graduation date, and prior history of donating. Or we might want to predict whether or not a loan will default based on credit score, purpose of the loan, geographic location, marital status, and income. Logistic regression will allow us to use the information we have to predict the likelihood of the event we're interested in. Linear Regression helps us answer the question, "What value should we expect?" while logistic regression tells us "How likely is it?" Given a set of inputs, a logistic regression equation will return a value between 0 and 1, representing the probability that the event will occur. Based on that probability, we might then choose to either take or not take a particular action. For example, we might decide that if the likelihood that an alumni will donate is below 5%, then we're not going to ask them for a donation. Or if the probability of default on a loan is above 20%, then we might refuse to issue a loan or offer it at a higher interest rate. How we choose the cutoff depends on a cost-benefit analysis. For example, even if there is only a 10% chance of an alumni donating, but the call only takes two minutes and the average donation is 100 dollars, it is probably worthwhile to call.
Views: 174840 Data Analysis Videos

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Introduction Heart Diseases remain the biggest cause of deaths for the last two epochs. Recently computer technology develops software to assistance doctors in making decision of heart disease in the early stage. Diagnosing the heart disease mainly depends on clinical and obsessive data. Prediction system of Heart disease can assist medical experts for predicting heart disease current status based on the clinical data of various patients. In this project, the Heart disease prediction using classification algorithm Naive Bayes, and Random Forest is discussed. Naive Bayes Algorithm The Naive Bayes classification algorithm is a probabilistic classifier. It is based on probability models that incorporate strong independence assumptions. Naive Bayes is a simple technique for constructing classifiers models that assign class labels to problem instances. It assume that the value of a particular feature is independent of the value of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 10 cm in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of any possible correlations between the color, roundness, and diameter features. Random Forest Technique In this technique, a set of decision trees are grown and each tree votes for the most popular class, then the votes of different trees are integrated and a class is predicted for each sample. This approach is designed to increase the accuracy of the decision tree, more trees are produced to vote for class prediction. This approach is an ensemble classifier composed of some decision trees and the final result is the mean of individual trees results. Follow Us: Facebook : https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/ Twitter: https://twitter.com/e2matrix_lab/ LinkedIn: https://www.linkedin.com/in/e2matrix-thesis-jalandhar/ Instagram: https://www.instagram.com/e2matrixresearch/

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Explained K means Clustering Algorithm With Best Example In Quickest And Easiest way Ever in Hindi. GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING
Views: 11722 5 Minutes Engineering

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This video looks at the difference between discrete and continuous variables. It includes 6 examples.
Views: 25055 Marty Brandl

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Machine Learning - Part 1 - UI5CN Core https://www.ui5cn.com/courses/project-core Machine Learning Algorithms can be classified into 3 types Supervised Learning, Unsupervised Learning and Reinforcement Learning. In Machine Learning we can solve 5 types of different problems: 1. Classification 2. Anomaly Detection 3. Regression 4. Clustering 5. Reinforcement Learning 1. Classification In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example would be assigning a given email into "spam" or "non-spam" classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition. 2. Anomaly Detection Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the dataset are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labelled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behaviour from a given normal training dataset and then testing the likelihood of a test instance to be generated by the learnt model. 3. Regression Regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modelling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. 4.Clustering Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is the main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. 5. Reinforcement Learning Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, reinforcement learning is called approximate dynamic programming, The approach has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with learning or approximation.
Views: 1686 UI5 Community Network

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Views: 75461 Siraj Raval

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If you found this useful, look for my ebook on Amazon, Straightforward Statistics using Excel and Tableau. Mutliple regression with a dummy variable as an independent variable. Uses Excel. Converts a categorical variables into a dummy coded [0,1] using Excel's =if() tool. Interpretation of estimated regression equation
Views: 163205 Stephen Peplow

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Provides illustration of healthcare analytics using multinomial logistic regression and cardiotocographic data. R file: https://goo.gl/ty2Jf2 Data: https://goo.gl/kMAh8U Includes, - steps for preparing data for the analysis - use of nnet package in r - calculation of probabilities using coefficients from the model - estimating probabilities using the model - developing confusion matrix - calculation of misclassification error Logistic regression is an important tool for developing classification or predictive analytics models related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 46972 Bharatendra Rai

09:04
Thanks for watching Covariance of two variable x and y in Hindi, Cov(x,y) in Hindi, Subscribe! Share and Like Important Book: UGC CSIR NET/SET (JRF & LS) Mathematical Sciences by Arihant Publications AMAZON SHOP LINK: http://amzn.to/2BdqwjN FOLLOW MY FOLLOWING LINK: AMMATHS TUTORIALS (Subscribe): https://goo.gl/PZxFAC Follow Twitter: https://goo.gl/68tZpg GOOGLE+: https://goo.gl/y7nEES FACEBOOK: https://goo.gl/VKgwrb BLOGER: https://goo.gl/GA9o5N http://www.ammathstutorial.com *Ammaths Tutorials by Maurya A.K.*
Views: 2771 AMMATHS TUTORIALS

04:13
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-313488098/m-641939067 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: 14798 Udacity

46:21
Views: 2842 Simplilearn

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This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. 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: 49214 Udacity

11:48

04:24
UI5CN CORE Machine Learning - Part 1 https://www.ui5cn.com/courses/project-core Machine Learning Algorithms can be classified into 3 types Supervised Learning, Unsupervised Learning and Reinforcement Learning. In Machine Learning we can solve 5 types of different problems: 1. Classification 2. Anomaly Detection 3. Regression 4. Clustering 5. Reinforcement Learning 1. Classification In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example would be assigning a given email into "spam" or "non-spam" classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition. 2. Anomaly Detection Three broad categories of anomaly detection techniques exist.Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model. 3. Regression Regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. 4.Clustering Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is the main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. 5. Reinforcement Learning Reinforcement learning (RL) is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, reinforcement learning is called approximate dynamic programming, The approach has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with learning or approximation.
Views: 8179 UI5 Community Network

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Views: 4896 Simplilearn

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Sahulat - Social Sciences Research Workshop - Data Analysis vs Data Mining (19 of 60)

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This AZTech training course is designed for all people involved in data mining and data analysis, as well as planning and forecasting, and especially the ones who are inclined to the use of graphical software interface, and their busy schedules are not allowing them to learn or apply programming languages like JAVA, R or Python. Read More: http://aztechtraining.com/course/spss-essentials Visit our Website: http://aztechtraining.com/ Senior Consultant: Sasa Kocic

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