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09:19
Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 377687 APMonitor.com

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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis. About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.
Views: 50888 MATLAB

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Data Analysis and Visualization is a Rich-full series Directed to all students interested in Analyzing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesiers in University of Western Australia, and Contains the main flowing Topics: 1 Data Visualization in Excel 2 Array Formula in Excel 3 2D Array Formula in Excel 4 Excel Macros 5 Why Matlab 6 Problem Solving in MATLAB 7 MATLAB Orientation - Data Types and Expressions 8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions 9 Matrices in MATLAB 10 MATLAB Scripts and Functions 11 Random Numbers, Gaussian Random Numbers, Complex Numbers 12 An Examples of Script and Function Files 13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure, 14 MATLAB Loops, Nested Loops, Repetition, while, For, 15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions, 16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence, 17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all, 18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets, 19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array 20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot 21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot, 22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots, 23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid, 24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp, 25 Arrays vs. Matrix Operations, 26 Dot Products, Example Calculating Center of Mass, Center of Gravity, 27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products, 28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors 29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws, 30 Symbolic Differentiation, sym, syms, diff 31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference, 32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule, 33 Monte Carlo Integration, 34 Introduction to ODE in System Biology 35 Introduction to System Biology, Gene Circuits, 36 Solving ODEs in Matlab, Repressilator, Programming steps 37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional, 38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres, 39 Introduction to Mathematica, 40 Programming in Mathematica 41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica 42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters, 43 Mathematica Rule Based Programming, Functional Programming, 44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules, 45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle, 46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation, 47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,
Views: 107 TO Courses

01:00:42
Learn how MATLAB can supplement the capabilities of Microsoft Excel. Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Many technical professionals find that they run into limitations using Excel for their data analysis applications. This webinar will show you how MATLAB can supplement the capabilities of Microsoft Excel by providing access to thousands of pre-built mathematical and advanced analysis functions, versatile visualization tools, and the ability to automate your analysis workflows. With MATLAB, you can efficiently explore, analyze, and visualize your data. Through product demonstrations, you will see how to: -Access data from files and Excel spreadsheets -Visualize data and customize figures -Perform statistical analysis and fitting -Generate reports and automate workflows -Share analysis tools as standalone applications or Excel add-ins This session is intended for people who are new to MATLAB. Experienced users may also benefit from the session, as the engineer will be showing capabilities from recent releases of MATLAB including the new ways to store and manage data commonly found in spreadsheets.
Views: 57909 MATLAB

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Use MATLAB® to analyze news sentiment with data from RavenPack®, including retrieving historical data and real-time data. Also, create trading rules based on news sentiment score. To Request a trial of Datafeed Toolbox, visit: https://www.mathworks.com/programs/trials/trial_request.html?prodcode=DF&s_iid=main_trial_DF_tb&s_eid=PEP_12669 Datafeed Toolbox™ provides access to current, intraday, historical, and real-time market data from leading financial data providers. By integrating these data feeds into MATLAB®, you can perform analyses, develop models, and create visualizations that reflect current financial and market behaviors. The toolbox also provides functions to export MATLAB data to some data service providers. You can establish connections from MATLAB to retrieve historical data or subscribe to real-time streams from data service providers. With a single function call, the toolbox lets you customize queries to access all or selected fields from multiple securities over a specified time period. You can also retrieve intraday tick data for specified intervals and store it as time-series data. Supported data providers include Bloomberg®, FactSet®, FRED®, Haver Analytics®, Interactive Data™, IQFEED®, Kx Systems®, SIX Financial Information, Thomson Reuters®, and Yahoo!® Finance.
Views: 2311 MATLAB

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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- An increasing number of applications require the joint use of signal processing and machine learning techniques on time series and sensor data. MATLAB can accelerate the development of data analytics and sensor processing systems by providing a full range of modelling and design capabilities within a single environment. In this webinar we present an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We introduce common signal processing methods in MATLAB (including digital filtering and frequency-domain analysis) that help extract descripting features from raw waveforms, and we show how parallel computing can accelerate the processing of large datasets. We then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) both programmatically and interactively. Finally, we demonstrate the use of automatic C/C++ code generation from MATLAB to deploy a streaming classification algorithm for embedded sensor analytics.
Views: 15680 MATLAB

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Currell: Scientific Data Analysis. Minitab analysis for Figs 9.6 and 9.7 http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press

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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r In this webinar, you will learn how to use MATLAB for data analysis from data access through visualization and modeling. Using measured wind data for wind farm siting, MathWorks engineers will demonstrate the use of MATLAB and data analysis products for the entire data analysis and modeling process. Webinar highlights include: • Importing measured data recorded from a data logger • Performing data quality assurance tests for erroneous and missing data • Exploratory data analysis and visualization, including wind rose plots and velocity histograms • Turbine performance estimation • Automating repetitive data analysis and reporting tasks This webinar is for scientists and engineers in industry and academia needing to accelerate their data analysis and modeling tasks.
Views: 5191 MATLAB

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This is a short video of how to use the classification app in Matlab. In addition using the classifier to predict the classification of new data is given/shown. Demo of deep tree,various support vector machine , nearest neighbour, trees techniques.
Views: 26083 Anselm Griffin

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Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer. This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.

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Real Time Implementation of the paper title Mining Sensor Data in Cyber Physical system. Data Mining: In Matlab Physical System: Android Cyber System: ThingSpeak
Views: 211 rupam rupam

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In this public webinar you will get an introduction to FOREX Data Mining with WEKA using several algorithms and sample data.

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Views: 43473 Ali Thaeer

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Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer. This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.

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Views: 39036 Simplilearn

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A common task in data science is to analyze data from an external source that may be in a text or comma separated value (CSV) format. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. This demonstrates how to import data, perform a basic analysis such as average values, trend the results, save the figure, and export the results to another text file.
Views: 42089 APMonitor.com

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Choose between various algorithms to train and validate regression models. After training multiple models, compare their validation errors side-by-side, and then choose the best model. To help you decide which algorithm to use, see Train Regression Models in Regression Learner App.
Views: 3687 Anselm Griffin

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Support Vector Machine (SVM) - Fun and Easy Machine Learning ►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 ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ 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: 172554 Augmented Startups

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Views: 29452 Krishma Punjabi

12:33
This tutorial video teaches about training a neural network in Matlab .....( Download Matlab Code Here: http://www.jcbrolabs.org/matlab-codes)
Views: 63737 sachin sharma

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This video helps to understand the neural networks modeling in the MATLAB. The nntool is GUI in MATLAB. To use it you dont need any programming knowledge. This tool is very useful for biology people who wants to use ANN for their data.

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This work build a model from 5 years data. Data is divided into classes based on general weathers like "Begining of Summer", Summer, Start of Rainfall, Mansoon, End of Rainfall, Begining of Winter, Winter Rainfall and so on. This is performed automatically using Fuzzy clustering technique ( not part of Video). It then trains NN and SVM with weather properties like Temperature, Humidity, Rainfall etc with the classes. When a specific year and day of the year is given as input for weather prediction, the system finds out the exact class and then aggregates the value of that class as predictive value. It also builds a decision support system to check if on specific week, there is a possibility of plant disease or not which can be used as precautionary measure for pesticides and so on.
Views: 23224 rupam rupam

12:43
Our Excel training videos on YouTube cover formulas, functions and VBA. Useful for beginners as well as advanced learners. New upload every Thursday. For details you can visit our website: http://www.familycomputerclub.com You can scrape, pull or get data from websites into Excel by performing a few simple steps. 1. record a macro to find out how one or many tables or data can be scraped from the website 2. Study the code carefully 3. Create an Excel sheet containing the links that get you the data from the appropriate web pages 4. Automate the process using a loop that creates a) New worksheets b) Automatically changes the link to the web pages that have the required data You can view the complete Excel VBA code here: http://www.familycomputerclub.com/scrpae-pull-data-from-websites-into-excel.html http://www.familycomputerclub.com/get-web-page-data-int-excel-using-vba.html Interesting Links: http://www.tushar-mehta.com/publish_train/xl_vba_cases/vba_web_pages_services/index.htm Get the book Excel 2016 Power Programming with VBA: http://amzn.to/2kDP35V If you are from India you can get this book here: http://amzn.to/2jzJGqU
Views: 523002 Dinesh Kumar Takyar

12:45
understanding how the input flows to the output in back propagation neural network with the calculation of values in the network. the example is taken from below link refer this https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ for full example
Views: 119158 Naveen Kumar

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Views: 289168 parag paija

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Date: February 24, 2015 Speaker: Loren Shure, MathWorks Related materials: http://www.mathworks.com/matlabcentral/fileexchange/49813-matlab%C2%AE-for-analyzing-and-visualizing-geospatial-data Accessing and visualizing data is a critical requirement for researchers trying to gain information about and insight from earthquakes. However, sometimes just connecting to and processing the data to prepare it for visualization and analysis can result in big hurdles and time sinks. MATLAB has many capabilities for working with and visualizing data, including multiple new features that make handling and viewing geospatial data much easier and require much less coding. During this webinar, Dr. Loren Shure, a geophysicist by training and a MATLAB expert by day, will use MATLAB to demonstrate two different earthquake case studies. The case studies will demonstrate how you can: - Access geospatial data from public sources - Display web map data with layers superposed using the Mapping Toolbox - Work with big data - Reproduce and compare to results of previous research in the field - Speed up your MATLAB code with parallel and GPU computing using the Parallel Computing Toolbox, including accessing HPC resources at your site - Use the new MATLAB 2014b features, such as the new graphics system, date-times, and more (http://www-test1.mathworks.com/products/matlab/whatsnew.html) In addition, Loren will show you how to find resources within the MATLAB, IRIS, and related seismic, geodetic, and broader geoscience communities, including where to get - Sample code, such as irisfetch.m - Case studies - Technical answers from the MATLAB geoscience community, and - Webinars, videos and product information for learning more about MATLAB capabilities

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Logistic Regression - Fun and Easy Machine Learning ►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 Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for classification problems. Despite the name “logistic regression” this is not an algorithm for regression Logistic Regression is a little bit similar to Linear Regression in the sense that both have the goal of estimating the values for the parameters/coefficients, so the at the end of the training of the machine learning model we got a function that best describe the relationship between the known input and the output values... ------------------------------------------------------------ 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: 61148 Augmented Startups

09:09
Extract data from the Facebook Graph API using the facepager tool. Much easier for those of us who struggle with API keys ;) . Blog Post: http://davidsherlock.co.uk/using-facepager-find-comments-facebook-page-posts/
Views: 203992 David Sherlock

09:07
www.MetaNeural.com This video explains how to input the collected data into the Neural Network engine The MetaNeural EA is the first highly customizable neural network expert advisor available to retail traders. It works with Metatrader 4 and can create, train, test, and use cutting-edge neural networks for automated trading.
Views: 15032 MetaNeural

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We have launched Study Focus and Sleep Music Android Mobile App. *** No Ads at All *** https://play.google.com/store/apps/details?id=com.mbreath.sleeptherapynew Focus on your study and work while listening to these beautiful melodies ****************************************************************** learn basics of matlab here and subscribe for more videos Keywords: ecg data analysis, ecg data analysis matlab, ecg data analysis on cloud using aneka ecg data, ecg data analysis, ecg data in .txt format, ecg data analysis matlab, ecg database free download, ecg database for matlab, ecg data file, ecg data matlab, ecg database .mat file, ecg data acquisition, ecg data in excel, ecg data analysis on cloud using aneka, ecg data compression, ecg data for matlab, ecg data in matlab data smoothing, data smoothing in excel, data smoothing matlab, data smoothing techniques excel, data smoothing in r, data smoothing methods, data smoothing in data mining, data smoothing pandas, data smoothing function in excel, data smoothing techniques in data mining, data smoothing labview, data smoothing techniques, data smoothing excel
Views: 528 KGP Talkie

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Views: 93037 Siraj Raval

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RCCB circuit breaker working principle, & how RCCB trip works are explained in Hindi in the video. What is Residual current Circuit Breaker (RCCB)? RCCB is a current sensing electro-mechanical device that breaks an electric circuit in case of the earth fault. It operated, whenever the difference between line and neutral current (called residual current) becomes more than the set value. How RCCB works, RCD residual current device, ELCB is explained in Hindi. ELCB Earth leakage circuit breaker concept is explained in Hindi on electricity & electrical engineering tutorial. RCCB Types AB, A & B and also poles, the test method of RCCB tripping are explained in Hindi lecture. Educational video tutorial on electrical engineering in Hindi 144 by G K Agrawal (Gopal Krishna Agrawal). The lecture is given by a person with industrial experience and useful for electrical, electronic communication and power electronics engineering students, project work and also Physics students. Watch हिंदी में MCB and RCCB circuit breaker difference in Hindi https://www.youtube.com/watch?v=f5Jp5NCGFzY https://www.youtube.com/watch?v=gzLSCIfwOAY https://www.youtube.com/watch?v=hLTJbi75u0E https://www.youtube.com/watch?v=gzLSCIfwOAY https://www.youtube.com/watch?v=pFo3C-0vgBw https://www.youtube.com/watch?v=BTOvi-hV_Z4 The RCCB working principle in Hindi. What is Residual current? RCCB Circuit Breaker, ELCB Earth leakage circuit breaker concept explained. RCCB in Hindi. RCCB connection. What is RCCB? GK Agrawal electrical video in Hindi. RCCB tripping. How RCCB trips. how rccb trips work? #gkagrawal #ElectricalEngineering #CircuitBreaker #RCCB #Electrical #Agrawal #GkAgrawalVideo #ShortCircuit #ElectricalShock
Views: 150622 G K Agrawal

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Confusion Matrix for Multiple Classes www.imperial.ac.uk/people/n.sadawi

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Views: 10951 MATLAB Solutions

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It Explains Random Forest Method in a very simple and pictorial way --------------------------------- Read in great detail along with Excel output, computation and R code ---------------------------------- https://www.udemy.com/decision-tree-theory-application-and-modeling-using-r/?couponCode=Ad_Try_01
Views: 114595 Gopal Malakar

07:55
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: 180771 Data Analysis Videos

15:37
Data Analysis and Visualization is a Rich-full series Directed to all students interested in Analyzing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesiers in University of Western Australia, and Contains the main flowing Topics: 1 Data Visualization in Excel 2 Array Formula in Excel 3 2D Array Formula in Excel 4 Excel Macros 5 Why Matlab 6 Problem Solving in MATLAB 7 MATLAB Orientation - Data Types and Expressions 8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions 9 Matrices in MATLAB 10 MATLAB Scripts and Functions 11 Random Numbers, Gaussian Random Numbers, Complex Numbers 12 An Examples of Script and Function Files 13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure, 14 MATLAB Loops, Nested Loops, Repetition, while, For, 15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions, 16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence, 17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all, 18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets, 19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array 20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot 21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot, 22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots, 23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid, 24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp, 25 Arrays vs. Matrix Operations, 26 Dot Products, Example Calculating Center of Mass, Center of Gravity, 27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products, 28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors 29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws, 30 Symbolic Differentiation, sym, syms, diff 31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference, 32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule, 33 Monte Carlo Integration, 34 Introduction to ODE in System Biology 35 Introduction to System Biology, Gene Circuits, 36 Solving ODEs in Matlab, Repressilator, Programming steps 37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional, 38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres, 39 Introduction to Mathematica, 40 Programming in Mathematica 41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica 42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters, 43 Mathematica Rule Based Programming, Functional Programming, 44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules, 45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle, 46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation, 47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,
Views: 34 TO Courses

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A step by step guide of how to run k-means clustering in Excel. Please note that more information on cluster analysis and a free Excel template is available at http://www.clusteranalysis4marketing.com
Views: 92956 MktgStudyGuide

34:25
PyData London 2016 Data from smartphone sensors can be used to learn from and analyse our daily behaviours. In this talk, I'll discuss processing and learning from sensor data with Python. I'll focus on accelerometers - a triaxial sensor that measures motion - starting with an overview pre-processing the data and ending with supervised and unsupervised learning applications and visualisations. Our smartphones are increasingly being built with sensors, that can measure everything from where we are (GPS, Wi-Fi) to how we move (accelerometers) and other aspects of our environments (e.g., temperature, humidity). Many apps are now being designed to collect and leverage this data, in order to provide interesting context-aware services and quantify our daily routines. In this talk, I'll give an overview of collecting sensor data from an Android app and processing the data with Python. I'll focus on accelerometers - a triaxial sensor that measures the device's motion - which is now being used in apps that detect what you are doing (cycling, running, riding a train); if we have enough time I'll also briefly cover a similar example with Wi-Fi/location data. Using an open-sourced Android app and iPython notebook, I'll discuss the following questions: What does the raw data look like? There are a number of trade-offs when collecting sensor data: most notably, data collection needs to be balanced against battery consumption. Plotting the raw data gives a view of how the data was sampled and how it changes across activities. How can I pre-process and extract features from this data? Three kinds of features can be extracted from acceleromter data: statistical, time-series, and signal-based. Most of these are readily available in well-known Python libraries (scipy, numpy, statsmodels). How can these features be used to analyse behaviours? I'll show an example of using accelerometer data to cluster users into groups, based on how active they are. How can these features be used to detect behaviours? I'll show an example of training a supervised learning algorithm (using scikit-learn) to detect walking vs. running vs. standing. I'll close by discussing how these techniques are being applied in novel smartphone apps for health monitoring. GitHub Repo: https://github.com/nlathia/pydata_2016
Views: 3791 PyData

01:07:57
[recording cut short due to technical issues] Pengcheng Zhou, Columbia University In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. We developed a new matrix factorization approach, named CNMF-E, to accurately separate the background and then simultaneously demix and denoise the neuronal signals of interest (https://arxiv.org/abs/1605.07266). The method has been thoroughly compared against widely- used independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data. In this tutorial, we will briefly review existing methods in extracting neurons from raw calcium imaging video data, and then focus on our method CNMF-E. We will give a thorough tutorial on analyzing microendoscopic data using the MATLAB version of CNMF-E (https://github.com/zhoupc/CNMF_E). The method is specialized for, but not limited to, 1-photon microendoscopic data. In practice, we have also successfully applied the method to 2-photon imaging data. You are welcome to bring your own datasets and we will try to make CNMF-E work on them. After the tutorial, slides and resources will be posted on the computational tutorials stellar page. slides, references, and exercises: https://stellar.mit.edu/S/project/bcs-comp-tut/materials.html videos: http://cbmm.mit.edu/videos?field_video_grouping_tid[0]=781

13:01
Presentasi kelompok 5 data mining fasilkom ui

12:29
Views: 293 GKMC datamining

02:42
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: 159475 Udacity

10:03
In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com
Views: 198857 Influxity

09:57
Dataset: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing# Overview: The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. This dataset consists of client information of a bank; 41188 records with 20 inputs, ordered by date (from May 2008 to November 2010). Aim: The classification goal is to predict if the client will subscribe (yes/no) a term deposit. The data includes information about the clients and marketing calls. Together with this data there is a record of whether the clients are currently enrolled for a term deposit. All of the variables should be considered and modeled to produce classification to accurately predict an entry for a client. Attribute Information: Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown') # related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. # other attributes: 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') # social and economic context attributes 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')
Views: 426 Gaurang Panchal

14:28
Data Mining Algorithm and intrusion detection system IDS algorithm is being tested in NSL-KDD data-set. I have applied an adaptive learning technique to optimise the output this time... For making our system more efficient and able to generate more accurate result, it is necessary to improve the performance of SVM classifier. Because all the result’s accuracy depends upon data which is generated by SVM Classifier. So when the performance of SVM classifier will improve then our results will be closer to the facts automatically. For any further help contact us at [email protected] visit us at http://www.researchinfinitesolutions.com/ Direct at :: +91-8146105825 Whatsapp at :: +91-8146105825
Views: 6384 Fly High with AI

01:05
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Views: 812 PG Embedded Systems