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Unsupervised Learning in R

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5

(3)

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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

Machine learning is often used to find patterns in data. It is impossible to predict the future. This is unsupervised learning. This can be used to identify the unsupervised learning that is being done to target marketing campaigns by grouping consumers based on their buying history and demographics. Another example is to determine the unmeasured factors that affect differences in crime rates between cities. This course will give you a general introduction to clustering and dimension in R from a machine learning perspective. This course will allow you to quickly get data into insight.

Course Overview

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Virtual Labs

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Introduction to R

What You Will Learn

You'll have applied k-means clustering to a fun "real-world" dataset!

Learn exactly what PCA does, visualize the results of PCA with biplots and scree plots, and deal with practical issues such as centering and scaling the data before performing PCA

You'll extend what you've learned by combining PCA as a preprocessing step to clustering using data that consist of measurements of cell nuclei of human breast masses

Course Instructors

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Hank Roark

Senior Data Scientist, Boeing

Hank is a Senior Data Scientist at Boeing and a long time user of the R language. Prior to his current role, he led the Customer Data Science team at H2O.ai, a leading provider of machine learning and predictive analytics services.

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