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Advanced Dimensionality Reduction in R

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$12

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

To reduce dimensionality, unsupervised machine learning algorithms can be used. These algorithms have many benefits. You will learn how to reduce dimensionality to get the most out of these benefits. This course uses interesting datasets like the MNIST database with handwritten numbers, Zalando's fashion version MNIST and a credit card fraud detection dataset. We will first be examining t–SNE, which is an algorithm that performs linear dimensionality loss. Next, we'll show you how to reduce dimensionality in predictive models. Finally, you will learn how GLRM can be used for compressing large data (with numerical and categorical values) and to impute missing value. Are you ready to start compressing high dimensional data?

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Dimensionality Reduction in R

What You Will Learn

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Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM

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You will see the application of GLRM to compress big data (with numerical and categorical values) and impute missing values

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You will learn what a distance metric is and which ones are the most common, along with the problems that arise with the curse of dimensionality

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You will also apply t-SNE to understand the patterns learned by a neural network

Course Instructors

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Federico Castanedo

Data Scientist at DataRobot

Federico Castanedo is the Lead Telco Data Scientist at DataRobot. He is also an O'Reilly author on data science. Previously, he was the Lead Data Scientist at Vodafone Group and before that Chief Da...

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Course Cover

$12

$6

49% OFF

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