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Dimensionality reduction Methods in Hindi | Machine Learning Tutorials
 
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visit our website for full course www.lastmomenttuitions.com 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: 24252 Last moment tuitions
Introduction to Data Mining: Dimensionality Reduction
 
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In this Data Mining Fundamentals tutorial, we discuss the curse of dimensionality and the purpose of dimensionality reduction for data preprocessing. When dimensionality increases, data becomes increasingly sparse in the space that it occupies. Dimensionality reduction will help you avoid this. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCpBK0 Watch the latest video tutorials here: https://hubs.ly/H0hCpgW0 See what our past attendees are saying here: https://hubs.ly/H0hCrT10 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 6923 Data Science Dojo
Data Mining & Business Intelligence | Tutorial #17 | Data Reduction - Dimensionality Reduction
 
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Order my books at 👉 http://www.tek97.com/ #DataReduction #DimensionalityReduction Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj This video explains what is Dimensionality Reduction in Data Mining as a Data Reduction step. Watch Now! يشرح هذا الفيديو ما هو Dimensionality Reduction في Data Mining كخطوة لخفض البيانات. شاهد الآن ! Este video explica qué es la reducción de la dimensionalidad en la minería de datos como un paso de reducción de datos. Ver ahora ! В этом видео объясняется, что такое уменьшение размера в интеллектуальном анализе данных как шаг сокращения данных. Смотри ! Cette vidéo explique ce qu'est la réduction de la dimension dans l'exploration de données en tant qu'étape de réduction des données. Regarde maintenant ! In diesem Video wird erklärt, was Dimensionality Reduction in Data Mining als Datenreduktionsschritt bedeutet. Schau jetzt ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 4289 Ranji Raj
Lecture 46 — Dimensionality Reduction - Introduction | Stanford University
 
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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. .
Dimensionality Reduction - The Math of Intelligence #5
 
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Most of the datasets you'll find will have more than 3 dimensions. How are you supposed to understand visualize n-dimensional data? Enter dimensionality reduction techniques. We'll go over the the math behind the most popular such technique called Principal Component Analysis. Code for this video: https://github.com/llSourcell/Dimensionality_Reduction Ong's Winning Code: https://github.com/jrios6/Math-of-Intelligence/tree/master/4-Self-Organizing-Maps Hammad's Runner up Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Self%20Organizing%20Maps%20for%20Data%20Visualization Please Subscribe! And like. And comment. That's what keeps me going. I used a screengrab from 3blue1brown's awesome videos: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw More learning resources: https://plot.ly/ipython-notebooks/principal-component-analysis/ https://www.youtube.com/watch?v=lrHboFMio7g https://www.dezyre.com/data-science-in-python-tutorial/principal-component-analysis-tutorial https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/ http://setosa.io/ev/principal-component-analysis/ http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html https://algobeans.com/2016/06/15/principal-component-analysis-tutorial/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 83711 Siraj Raval
Basics Of Principal Component Analysis Explained in Hindi ll Machine Learning Course
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) 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: 30439 5 Minutes Engineering
Curse Of Dimensionality Explained with Examples in Hindi ll Machine Learning Course
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) 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: 7515 5 Minutes Engineering
Lecture 14.4 —  Dimensionality Reduction | Principal Component Analysis Algorithm — [ Andrew Ng ]
 
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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. .
Dimensionality Reduction
 
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This video is part of the Udacity course "Introduction to Computer Vision". Watch the full course at https://www.udacity.com/course/ud810
Views: 4122 Udacity
StatQuest: Principal Component Analysis (PCA), Step-by-Step
 
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Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it can tell you how accurate your new understanding of the data actually is. In this video, I go one step at a time through PCA, and the method used to solve it, Singular Value Decomposition. I take it nice and slowly so that the simplicity of the method is revealed and clearly explained. There is a minor error at 1:47: Points 5 and 6 are not in the right location If you are interested in doing PCA in R see: https://youtu.be/0Jp4gsfOLMs For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest
Machine Learning - Dimensionality Reduction - Feature Extraction & Selection
 
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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 #MachineLearning 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: 25675 Cognitive Class
Lecture 48 — Dimensionality Reduction with SVD | Stanford University
 
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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. .
Dimensionality Reduction: High Dimensional Data, Part 1
 
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Data Science for Biologists Dimensionality Reduction: High Dimensional Data Part 1 Course Website: data4bio.com Instructors: Nathan Kutz: faculty.washington.edu/kutz Bing Brunton: faculty.washington.edu/bbrunton Steve Brunton: faculty.washington.edu/sbrunton
Views: 10520 Data4Bio
Dimensionality reduction method
 
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DATA VISALIZATION WITH PROCESSING
Views: 96 Paul Rosero
Lec-33 Dimensionality reduction Using PCA
 
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Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 29648 nptelhrd
Dimensionality Reduction - تقليص الأبعاد
 
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Link to the first part of the tutorial https://www.youtube.com/watch?v=050QtXXNV_g&feature=youtube_gdata_player
Views: 2219 Ibrahim Almosallam
Dimensionality Reduction: Principal Components Analysis, Part 1
 
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Data Science for Biologists Dimensionality Reduction: Principal Components Analysis Part 1 Course Website: data4bio.com Instructors: Nathan Kutz: faculty.washington.edu/kutz Bing Brunton: faculty.washington.edu/bbrunton Steve Brunton: faculty.washington.edu/sbrunton
Views: 77603 Data4Bio
Dimensionality Reduction: Introduction and Basic Concepts
 
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A very general overview of Dimensionality Reduction with several examples. Video by Michael Lin Music: "F*ck that," "Runway Y'" --Death Grips
Views: 3722 Complex Objects
Principal Component Analysis (PCA)
 
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This Lecture Describes Principal Component Analysis (PCA) with the help of an easy example.
Views: 114775 Saurabh Singh
CompX: Transforming data and dimension reduction with Jono & Lewis
 
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Computational Thinking and Big Data is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets. Enrol now! http://bit.ly/2rfZXSz
Lecture 14.1 — Dimensionality Reduction Motivation I | Data Compression — [ Andrew Ng ]
 
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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. .
Dimensionality Reduction: Eigenpets, Part 1
 
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Data Science for Biologists Dimensionality Reduction: Eigenpets Part 1 Course Website: data4bio.com Instructors: Nathan Kutz: faculty.washington.edu/kutz Bing Brunton: faculty.washington.edu/bbrunton Steve Brunton: faculty.washington.edu/sbrunton
Views: 6694 Data4Bio
1. Dimensionality Reduction and Clustering Example
 
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Book: Introduction to Statistical Learning - with Applications in R http://www-bcf.usc.edu/~gareth/ISL/
Views: 860 MachineLearningGod
SKlearn PCA, SVD Dimensionality Reduction
 
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#ScikitLearn #DimentionalityReduction #PCA #SVD #MachineLearning #DataAnalytics #DataScience Dimensionality reduction is an important step in data pre processing and data visualisation specially when we have large number of highly correlated features. In this tutorial, we apply Principal Component Analysis and Singular Value decomposition to boston housing and MNIST handwriting dataset and observe the effects of dimensionality reduction on accuracy. We also see how dimensionality reduction can be used to visualize data. For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon
Views: 11663 The Semicolon
Analysis of Medical Data Using Dimensionality Reduction Techniques
 
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This is a ~3-minute video highlight produced by undergraduate students Robert Colgan and David Gutierrez regarding their research topic during the 2013 AMALTHEA REU Program at Florida Institute of Technology in Melbourne, FL. They were mentored by MS student Jugesh Sundram and professor Dr. G. Bhaskar Tenali (Mathematical Sciences Department). More details about their project can be found at http://www.amalthea-reu.org.
Numerical on PCA | MACHINE LEARNING TUTORIALS
 
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visit our website for full course www.lastmomenttuitions.com ML notes form : https://goo.gl/forms/7rk8716Tfto6MXIh1 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: 46276 Last moment tuitions
Dimensionality Reduction: PCA and Gauss. Proc. Factor Analysis, by Frederic Simard
 
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In this talk, you will learn the basics of dimensionality reduction. The first algorithm that is presented is the principal component analysis (PCA) which is based on explaining the variance in the data set. You will learn how to select a subset of dimensions while maintaining the most information about your data, as to, for example, make a classifier. A quick presentation of the Gaussian Process Factor Analysis follows. This algorithm extract trajectories of a system state in lower dimension space. Speaker: Frederic Simard Contact: [email protected] Website: www.atomsproducts.com
Views: 3032 CAMBAM Students
Principal Components Analysis - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-649069103/m-661438544 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: 291232 Udacity
Vishal Patel | A Practical Guide to Dimensionality Reduction Techniques
 
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PyData DC 2016 This talk provides a step-by-step overview and demonstration of several dimensionality (feature) reduction techniques. Attendees should have some basic level of understanding of data wrangling and supervised learning. The presentation will also include snippets of Python code, so familiarity with Python code will be useful.
Views: 5866 PyData
Reducing High Dimensional Data with PCA and prcomp: ML with R
 
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In this R video, we'll see how PCA can reduce a 1000+ variable data set into 10 variables and barely lose accuracy! Walkthrough & code: http://amunategui.github.io/high-demensions-pca/ Note: data source url in the video no longer works, see the walkthrough for new source: http://amunategui.github.io/high-demensions-pca/ Note: for those that can't use xgboost - I added an alternative script using GBM in the walkthrough: http://amunategui.github.io/high-demensions-pca/ Top of the page under resources look for link: "Alternative GBM Source Code - for those that can't use xgboost" MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 40728 Manuel Amunategui
Feature Selection for reducing the dimensionality :Data mining
 
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Paper ID 208 of ICSCC2017, NIT Kurukshetra Conference
Views: 48 Sai Prasad
PCA 1: curse of dimensionality
 
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Full lecture: http://bit.ly/PCA-alg The number of attributes in our data is often a lot higher than the true dimensionality of the dataset. This means we have to estimate a large number of parameters, which are often not directly related to what we're trying to learn. This creates a problem, because our training data is limited.
Views: 74598 Victor Lavrenko
L22:Data Reduction in Mining| Data Cube Aggregation| Dimensionality reduction |hierarchy generation
 
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Data Reduction in Mining| Data Cube Aggregation| Dimensionality reduction |hierarchy generation Namaskar, In Today's lecture, i will cover Data Reduction in Mining of data warehouse and data mining I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 608 University Academy
Feature Selection in Machine learning| Variable selection| Dimension Reduction
 
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Feature selection is an important step in machine learning model building process. The performance of models depends in the following : Choice of algorithm Feature Selection Feature Creation Model Selection So feature selection is one important reason for good performance. They are primarily of three types: Filter Methods Wrapper Methods Embedded Methods You will learn a number of techniques such as variable selection through Correlation matrix, subset selection, stepwise forward, stepwise backward, hybrid method etc. You will also learn regularization (shrinkage) methods such as lasso and Ridge regression that can well be used for variable selection. Finally you will learn difference between variable selection and dimension reduction ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 28886 Analytics University
Dimensionality Reduction: High Dimensional Data, Part 2
 
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Data Science for Biologists Dimensionality Reduction: High Dimensional Data Part 2 Course Website: data4bio.com Instructors: Nathan Kutz: faculty.washington.edu/kutz Bing Brunton: faculty.washington.edu/bbrunton Steve Brunton: faculty.washington.edu/sbrunton
Views: 5358 Data4Bio
Dimensionality Reduction
 
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Access the Dimensionality Reduction Workshop materials here: https://rapidminer-my.sharepoint.com/:f:/p/hmatusow/EoaCUAnKlxVEgw2sDQPIHtwBdzI-eFqHy8OJDQIt2o-Olw?e=cZEahH
Views: 321 RapidMiner, Inc.
PCA, SVD
 
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Linear dimensionality reduction: principal components analysis (PCA) and the singular value decomposition (SVD)
Views: 67160 Alexander Ihler
Statistical & Data Science Modelling in High Dimension
 
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Statistical modelling in high dimension : When there are too many predictors variables, selecting the best set is a challenge. There are ways to reduce the dimension.Following ways can be tried out. 1- feature Selection 2- Shrinkage (Regularization) 3- Dimension reduction (PCA & PLS) A number of points have to be kept in mind while dealing with high dimension data ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Applied Machine Learning 2019 - Lecture 14 - Dimensionality Reduction
 
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Principal Component Analysis, Linear Discriminant Analysis, Manifold Learning, T-SNE Slides and more materials are on the class website: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
Views: 584 Andreas Mueller
Advanced Data Mining projects with R : Why Dimensionality Reduction? | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2n53Vi6]. When there are a lot of variables, it becomes difficult to extract data. We need to devise something that will let us gather data in less number of variables. Dimensionality reduction provides you with that solution. • Get a numerical dataset • Calculate the covariance matrix • Create a feature vector. Create low dimension data. For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 151 Packt Video
Curse of Dimensionality - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-666010252/m-672718832 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 18321 Udacity
Principal Component Analysis (PCA) clearly explained (2015)
 
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NOTE: On April 2, 2018 I updated this video with a new video that goes, step-by-step, through PCA and how it is performed. Check it out! https://youtu.be/FgakZw6K1QQ RNA-seq results often contain a PCA or MDS plot. This StatQuest explains how these graphs are generated, how to interpret them, and how to determine if the plot is informative or not. I've got example code (in R) for how to do PCA and extract the most important information from it on the StatQuest website: https://statquest.org/2015/08/13/pca-clearly-explained/ For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest
BADM 3.3: Dimension Reduction Approaches
 
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Other dimension reduction approaches This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. For more information: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 575 Galit Shmueli
Introduction to Data Mining: Feature Subset Selection
 
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In this Data Mining Fundamentals tutorial, we discuss another way of dimensionality reduction, feature subset selection. We discuss the many techniques for feature subset selection, including the brute-force approach, embedded approach, and filter approach. Feature subset selection will reduce redundant and irrelevant features in your data. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCrXC0 Watch the latest video tutorials here: https://hubs.ly/H0hCsk70 See what our past attendees are saying here: https://hubs.ly/H0hCsk80 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 7414 Data Science Dojo
Weka Tutorial 10: Feature Selection with Filter (Data Dimensionality)
 
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This tutorial shows how to select features from a set of features that performs best with a classification algorithm using filter method.
Views: 69306 Rushdi Shams
Data Prep: Feature Selection & Dimensionality Reduction
 
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Automatically select variables for analysis with the feature selection node. Discusses in-database feature selection as well.well.
Views: 421 TIBCO Products
DATA MINING   1 Data Visualization   3 2 2  Multidimensional Scaling
 
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https://www.coursera.org/learn/datavisualization
Views: 11015 Ryo Eng
Data Reduction (As breif as Possible)
 
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This video is about brief knowledge about data reduction. how data reduction is used to compressed data.
Views: 1112 Tech Insight