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Supervised and unsupervised learning algorithms
Views: 62647 Nathan Kutz

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A tutorial about classification and prediction in Data Mining .
Views: 28303 Red Apple Tutorials

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Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation http://www.studyyaar.com/index.php/module-video/watch/53-data-mining
Views: 85823 StudyYaar.com

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Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 20343 nptelhrd

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Views: 12786 Educate Motivate

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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.

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Views: 780 Algorin Technical

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Learn the basics of Machine Learning with R. Start our Machine Learning Course for free: https://www.datacamp.com/courses/introduction-to-machine-learning-with-R First up is Classification. A *classification problem* involves predicting whether a given observation belongs to one of two or more categories. The simplest case of classification is called binary classification. It has to decide between two categories, or classes. Remember how I compared machine learning to the estimation of a function? Well, based on earlier observations of how the input maps to the output, classification tries to estimate a classifier that can generate an output for an arbitrary input, the observations. We say that the classifier labels an unseen example with a class. The possible applications of classification are very broad. For example, after a set of clinical examinations that relate vital signals to a disease, you could predict whether a new patient with an unseen set of vital signals suffers that disease and needs further treatment. Another totally different example is classifying a set of animal images into cats, dogs and horses, given that you have trained your model on a bunch of images for which you know what animal they depict. Can you think of a possible classification problem yourself? What's important here is that first off, the output is qualitative, and second, that the classes to which new observations can belong, are known beforehand. In the first example I mentioned, the classes are "sick" and "not sick". In the second examples, the classes are "cat", "dog" and "horse". In chapter 3 we will do a deeper analysis of classification and you'll get to work with some fancy classifiers! Moving on ... A **Regression problem** is a kind of Machine Learning problem that tries to predict a continuous or quantitative value for an input, based on previous information. The input variables, are called the predictors and the output the response. In some sense, regression is pretty similar to classification. You're also trying to estimate a function that maps input to output based on earlier observations, but this time you're trying to estimate an actual value, not just the class of an observation. Do you remember the example from last video, there we had a dataset on a group of people's height and weight. A valid question could be: is there a linear relationship between these two? That is, will a change in height correlate linearly with a change in weight, if so can you describe it and if we know the weight, can you predict the height of a new person given their weight ? These questions can be answered with linear regression! Together, \beta_0 and \beta_1 are known as the model coefficients or parameters. As soon as you know the coefficients beta 0 and beta 1 the function is able to convert any new input to output. This means that solving your machine learning problem is actually finding good values for beta 0 and beta 1. These are estimated based on previous input to output observations. I will not go into details on how to compute these coefficients, the function lm() does this for you in R. Now, I hear you asking: what can regression be useful for apart from some silly weight and height problems? Well, there are many different applications of regression, going from modeling credit scores based on past payements, finding the trend in your youtube subscriptions over time, or even estimating your chances of landing a job at your favorite company based on your college grades. All these problems have two things in common. First off, the response, or the thing you're trying to predict, is always quantitative. Second, you will always need input knowledge of previous input-output observations, in order to build your model. The fourth chapter of this course will be devoted to a more comprehensive overview of regression. Soooo.. Classification: check. Regression: check. Last but not least, there is clustering. In clustering, you're trying to group objects that are similar, while making sure the clusters themselves are dissimilar. You can think of it as classification, but without saying to which classes the observations have to belong or how many classes there are. Take the animal photo's for example. In the case of classification, you had information about the actual animals that were depicted. In the case of clustering, you don't know what animals are depicted, you would simply get a set of pictures. The clustering algorithm then simply groups similar photos in clusters. You could say that clustering is different in the sense that you don't need any knowledge about the labels. Moreover, there is no right or wrong in clustering. Different clusterings can reveal different and useful information about your objects. This makes it quite different from both classification and regression, where there always is a notion of prior expectation or knowledge of the result.
Views: 37315 DataCamp

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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: 71748 Udacity

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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 75900 MIT OpenCourseWare

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The k-means algorithm
Views: 18486 Nathan Kutz

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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 322821 Last moment tuitions

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Classification in Data Mining with classification algorithms. Explanation on classification algorithm the decision tree technique with Example.

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Presentasi tugas matakuliah Data Mining kelompok 4, Mahasiswa Semester 5 Teknik Informatika Universitas Yudharta Pasuruan. semoga bermanfaat...

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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 33064 MIT OpenCourseWare

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How KNN algorithm works with example: K - Nearest Neighbor, Classifiers, Data Mining, Knowledge Discovery, Data Analytics
Views: 119449 shreyans jain

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The k-nearest neighbors algorithm
Views: 12175 Nathan Kutz

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Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 480704 Victor Lavrenko

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Supervised and unsupervised learning algorithms
Views: 7833 Nathan Kutz

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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: 9080 Udacity

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Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters.
Views: 8250 Red Apple Tutorials

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K-Means Clustering Algorithm – Solved Numerical Question 1(Euclidean Distance)(Hindi) Data Warehouse and Data Mining Lectures in Hindi

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Classification consists of predicting a certain outcome based on a given input. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome, usually called goal or prediction attribute. The algorithm tries to discover relationships between the attributes that would make it possible to predict the outcome. Next the algorithm is given a data set not seen before, called prediction set, which contains the same set of attributes, except for the prediction attribute – not yet known. The algorithm analyses the input and produces a prediction.
Views: 32234 Nina Canares

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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-

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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .

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Gaussian Mixture Models (GMM)
Views: 4120 Nathan Kutz

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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-

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Hierarchical Clustering - Fun and Easy Machine Learning with Examples ►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 Hierarchical Clustering Looking at the formal definition of Hierarchical clustering, as the name suggests is an algorithm that builds hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left. The results of hierarchical clustering can be shown using Dendogram as we seen before which can be thought of as binary tree Difference between K Means and Hierarchical clustering Hierarchical clustering can’t handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n2). In K Means clustering, since we start with random choice of clusters, the results produced by running the algorithm multiple times might differ. While results are reproducible in Hierarchical clustering. K Means is found to work well when the shape of the clusters is hyper spherical (like circle in 2D, sphere in 3D). K Means clustering requires prior knowledge of K i.e. no. of clusters you want to divide your data into. However with HCA , you can stop at whatever number of clusters you find appropriate in hierarchical clustering by interpreting the Dendogram. ------------------------------------------------------------ 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: 26164 Augmented Startups

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Here are some of the most commonly used classification algorithms -- Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest and Support Vector Machine. https://analyticsindiamag.com/7-types-classification-algorithms/ -------------------------------------------------- Get in touch with us: Website: www.analyticsindiamag.com Contact: [email protected] Facebook: https://www.facebook.com/AnalyticsIndiaMagazine/ Twitter: http://www.twitter.com/analyticsindiam Linkedin: https://www.linkedin.com/company-beta/10283931/ Instagram: https://www.instagram.com/analyticsindiamagazine/

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KNN Classification– Solved Numerical Question in Hindi(Numerical 1) K-Nearest Neighbour Classification Solved Numerical Problem Data Warehouse and Data Mining Lectures in Hindi

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This video is part of the Analyzing and Visualizing Data with Power BI course available on EdX.  To sign up for the course, visit: http://aka.ms/pbicourse. To read more: Power BI service https://aka.ms/pbis_gs Power BI Desktop https://aka.ms/pbid_gs Power BI basic concepts tutorial: https://aka.ms/power-bi-tutorial To submit questions and comments about Power BI, please visit community.powerbi.com. To submit questions and comments about Power BI, please visit community.powerbi.com.
Views: 20861 Microsoft Power BI

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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: 74112 Cognitive Class

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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 18491 Last moment tuitions

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Definitions,Comparison,Issues
Views: 10535 Dr.Anamika Bhargava

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Demonstrating how to do Bayesian Classification, Nearest Neighbor, K means Clustering using WEKA . Generating data set and Probability Density Function using MATLAB. Important links: To know more about .arff formats go to: http://www.cs.waikato.ac.nz/ml/weka/arff.html Data sets: http://repository.seasr.org/Datasets/UCI/arff/ Online matlab: http://octave-online.net/
Views: 32229 Niranjan Singh

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Introduction Data Mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases. Crime analyses is one of the important application of data mining. Data mining contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to identify crimes faster and help to make faster decisions. The main objective of crime analysis is to find the meaningful information from large amount of data and disseminates this information to officers and investigators in the field to assist in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is used for crime data analysis. Kmeans Algorithm The algorithm is composed of the following steps: It randomly chooses K points from the data set. Then it assigns each point to the group with closest centroid. It again recalculates the centroids. Assign each point to closest centroid. The process repeats until there is no change in the position of centroids. Example of KMEANS Algorithm Let’s imagine we have 5 objects (say 5 people) and for each of them we know two features (height and weight). We want to group them into k=2 clusters. Our dataset will look like this: First of all, we have to initialize the value of the centroids for our clusters. For instance, let’s choose Person 2 and Person 3 as the two centroids c1 and c2, so that c1=(120,32) and c2=(113,33). Now we compute the Euclidean distance between each of the two centroids and each point in the data.

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KNIME is very helpful tool for Data Mining tasks like Clustering, Classification, Standard Deviation and Mean
Views: 29099 Sania Habib

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Data Warehouse and Mining For more: http://www.anuradhabhatia.com

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Views: 1160 Ryo Eng

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Views: 447 Galit Shmueli

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How to work with images in Orange, what are image embeddings and how do perform clustering with embedded data. For more information on image clustering, read the blog: [Image Analytics: Clustering] https://blog.biolab.si/2017/04/03/image-analytics-clustering/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 16908 Orange Data Mining

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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 389825 Thales Sehn Körting

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Views: 21866 Prabhudev Konana

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