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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: 12088 Last moment tuitions
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. .
Introduction to Data Mining: Dimensionality Reduction
 
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In part five of data preprocessing, we discuss the curse of dimensionality and the purpose of dimensionality reduction. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8LqR0 See what our past attendees are saying here: https://hubs.ly/H0f8LqS0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 5244 Data Science Dojo
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
Views: 70906 Siraj Raval
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: 1686 Ranji Raj
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: 3551 Complex Objects
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: 19646 Cognitive Class
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: 67386 Data4Bio
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: 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: 8743 Data4Bio
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: 14955 Udacity
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: 269677 Udacity
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: 3439 Udacity
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: 68058 Victor Lavrenko
Principal Component Analysis (PCA)
 
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This Lecture Describes Principal Component Analysis (PCA) with the help of an easy example.
Views: 106873 Saurabh Singh
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. 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/
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. .
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: 607 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: 9056 The SemiColon
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. .
Reducing High Dimensional Data with PCA and prcomp: ML with R
 
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Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUOKu 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" This has been re-designed as 'Reducing High Dimensional Data in R' on Udemy.com, $19 COUPON!!!: https://www.udemy.com/practical-data-science-reducing-high-dimensional-data-in-r/?couponCode=1111 Check out my other in-depth classes on Udemy.com (discounts and specials) at http://amunategui.github.io/udemy/ Follow me on Twitter https://twitter.com/amunategui and signup to my newsletter: http://www.viralml.com/signup.html More on http://www.ViralML.com and https://amunategui.github.io Thanks!
Views: 39211 Manuel Amunategui
Introduction to Data Mining: Feature Subset Selection
 
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In part six of data preprocessing, we discuss another way of dimensionality reduction, feature subset selection. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8Lrw0 See what our past attendees are saying here: https://hubs.ly/H0f8M7M0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 5634 Data Science Dojo
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
 
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LDA is surprisingly simple and anyone can understand it. Here I avoid the complex linear algebra and use illustrations to show you what it does so you will know when to use it and how to interpret the results. Sample code for R is at the StatQuest website: https://statquest.org/2016/07/10/statquest-linear-discriminant-analysis-lda-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/
Dimensionality Reduction and visualization introduction Lecture 1@ Applied AI Course
 
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for more information please visit https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-is-dimensionality-reduction-1/
Views: 3235 Applied AI Course
Weka Tutorial 09: Feature Selection with Wrapper (Data Dimensionality)
 
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This tutorial shows you how you can use Weka Explorer to select the features from your feature vector for classification task (Wrapper method)
Views: 65874 Rushdi Shams
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: 29070 nptelhrd
StatQuest: t-SNE, Clearly Explained
 
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t-SNE is a popular method for making an easy to read graph from a complex dataset, but not many people know how it works. Here's the dope! Also, if you'd like to see a code example in R, here's one: http://statquest.org/2017/09/18/statquest-t-sne-clearly-explained/ This StatQuest is by ReQuest! StatQuesters wanted t-SNE to be clearly explained, and now I've gone and done it. 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/
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: 3994 PyData
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: 2114 Ibrahim Almosallam
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: 4472 Data4Bio
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: 66279 Rushdi Shams
PCA, SVD
 
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Linear dimensionality reduction: principal components analysis (PCA) and the singular value decomposition (SVD)
Views: 61919 Alexander Ihler
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: 22531 Analytics University
StatQuest: 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/
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: 2943 CAMBAM Students
Tariq Rashid - Dimension Reduction and Extracting Topics - A Gentle Introduction
 
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Filmed at PyData 2017 Description Text mining has many powerful methods for unlocking insights into the messy, ambiguous, but interesting text created by people. Singular value decomposition (SVD) is a useful method for reducing the many dimensions of text data, and distill out key themes in that text - called topic modelling or latent semantic analysis. This talk for beginners will gently explain SVD and how to use it. Abstract Text mining and natural language processing are hugely powerful fields that can unlock insights into the vast amounts of human knowledge, creativity and drivel (!) for automated computing. Examples include the fun of highlighting trends in internet chatter through to more serious analysis of finding patterns and links in leaked data sets of public interest. One key tool is to reduce the many dimensions of text data, and distill out the key themes in that text. People call this topic modelling, latent semantic analysis, and a few other names too. The powerful method at the heart of this is called singular value decomposition (SVD). This talk will gently introduce singular valued decomposition (SVD), explaining the mathematics in an accessible manner, and demonstrate how it can be used, using the Chilcot Iraq Report as an example dataset. Example code, notebooks and data sets are public on GitHub, and there is a blog for more discussion of this, and other text mining ideas http://makeyourowntextminingtoolkit.blogspot.co.uk www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1084 PyData
Dimensionality reduction method
 
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DATA VISALIZATION WITH PROCESSING
Views: 91 Paul Rosero
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: 403 Galit Shmueli
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: 130 Packt Video
Principal Component Analysis (PCA) using Python (Scikit-learn)
 
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Principal Component Analysis (PCA) using Python (Scikit-learn) Step by Step Tutorial: https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60
Views: 32203 Michael Galarnyk
BPDM 2017 - Dimension Reduction and Clustering
 
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Ruiwen Zhang. PhD Senior Research Statistician SAS Institute, Inc.
StatQuest: MDS and PCoA
 
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MDS (multi-dimensional scaling) and PCoA (principal coordinate analysis) are very, very similar to PCA (principal component analysis). There really only one small difference, but that difference means you need to know what you're doing if you're going to use MDS effectively. This video make sure you learn what you need to know to use MDS and PCoA. 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/
Feature Selection
 
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Subset selection Forward Stepwise Backward Stepwise Variable Selection Shrinkage Method Dimension Reduction Tuning Parameter Principle Component Analysis
Views: 4468 Sunil Bhatia
Principal Component Analysis and Singular value Decomposition in Python - Tutorial 19 in Jupyter
 
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In this python for data science tutorial, you will learn about how to do principal component analysis (PCA) and Singular value decomposition (SVD) in python using seaborn, pandas, numpy and pylab. environment used is Jupyter notebook. This is the 19th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets
Views: 10014 TheEngineeringWorld
Probabilistic Dimensional Reduction with Gaussian Process...
 
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Google Tech Talks February 12, 2007 ABSTRACT Density modelling in high dimensions is a very difficult problem. Traditional approaches, such as mixtures of Gaussians, typically fail to capture the structure of data sets in high dimensional spaces. In this talk we will argue that for many data sets of interest, the data can be represented as a lower dimensional manifold immersed in the higher dimensional space. We will then present the Gaussian Process Latent Variable Model (GP-LVM), a non-linear probabilistic variant of principal component analysis (PCA) which implicitly assumes that the data lies on a lower dimensional space. Having introduced the GP-LVM we will review extensions to the...
Views: 6000 GoogleTechTalks

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