Getting started with machine learning in R /

Getting started with machine learning in R /

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Bibliographic Details
Author / Creator:	Rennert, Phil, speaker.
Imprint:	[Place of publication not identified] : Packt, [2018]
Description:	1 online resource (1 streaming video file (1 hr., 48 min., 4 sec.))
Language:	English
Subject:	R (Computer program language) Machine learning. Electronic data processing. Computer algorithms. Algorithms. Algorithms Computer algorithms Electronic data processing Machine learning R (Computer program language) Instructional films Instructional films.
Format:	E-Resource Video Streaming Video
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/13659780

Hidden Bibliographic Details
Notes:	Title from title screen (viewed August 1, 2018). Date of publication from resource description page. Presenter, Phil Rennert.
Summary:	"You will learn to apply machine learning techniques in the popular statistical language R. This course will get you started with Machine Learning and R by understanding Machine Learning and installing R. The course will then take you through some different types of ML. You will work with a classic dataset using Machine Learning. You will learn Linear and Logistic Regression algorithms and analyze the dataset. The course will take you through algorithms like Random Forest and Naive Bayes for working on your data in R. You will then see some of the excellent graphical tools in R, and some discussion of the goals and techniques for presenting graphical data. Analysis of the data set is demonstrated from end to end, with example R code you can use. Then you'll have a chance to do it yourself on another data set. By the end of the course you will learn how to gain insights from complex data and how to choose the correct algorithm for your specific needs."--Resource description page

Description
Item Description:	Title from title screen (viewed August 1, 2018). Date of publication from resource description page.
Physical Description:	1 online resource (1 streaming video file (1 hr., 48 min., 4 sec.))