Machine learning in R : automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks

Resource Information

The work Machine learning in R : automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks represents a distinct intellectual or artistic creation found in Merrimack Valley Library Consortium. This resource is a combination of several types including: Work, Still Image, Visual Materials.

Label

Machine learning in R : automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks

Title remainder

automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks

Statement of responsibility

with Michael Grogan

Creator

• Grogan, Michael

Speaker

• Grogan, Michael

Subject

• Neural networks (Computer science)
• Big data
• Statistics -- Data processing
• R (Computer program language)
• Machine learning

Language

eng

Summary

"In the world of big data, analysis by traditional statistical methods is no longer sufficient. The amount of data and the number of potential relationships that could be analyzed is simply too complex to conduct manually. In this video, you'll learn a better way: how to automate the analysis of big data by using machine learning techniques in R. You'll explore the cornerstone methods of machine learning (i.e., k-means clustering, decision trees, random forests, and neural networks); you'll incorporate these methods inside R to construct a set of machine learning algorithms; and then you'll deploy these algorithms against a real-world dataset to perform a high-value business analysis of the data. Course prerequisites include basic knowledge of linear algebra, probability, statistics, and familiarity with R."--Resource description page

Cataloging source

UMI

Characteristic

videorecording

LC call number

QA276.45.R3

PerformerNote

Presenter, Michael Grogan

Runtime

39

Technique

live action