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Mixture Models 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

Mixture modeling can be used when there is an interest in the heterogeneity of populations. Mixture models use familiar probability distributions (e.g. The mixture models employ familiar probability distributions such as Poisson, Gaussian and Binomial to provide a formal, but useful statistical framework for classifications and clustering. The probability of belonging to a specific cluster can be calculated and inferences made about sub-populations. This is an exception from traditional clustering methods. Marketing, for instance, might allow you to group different customer segments to determine their likelihood to buy specific products. You can target them with targeted promotions more effectively. Natural language processing can be used for grouping documents into topics. This will enable you to determine the importance of each topic in all documents. This course will explain Mixture Models and how to use them.

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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Intermediate R

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Introduction to the Tidyverse

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Foundations of Probability in R

What You Will Learn

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Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification

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In this course, you will learn what Mixture Models are, how they are estimated, and when it is appropriate to apply them!

Course Instructors

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Victor Medina

Instrutor

Instructor at Data Camp

Course Reviews

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