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Supervised Learning in R: Regression

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Certification

Supervised Learning in R: Regression

This course will show you how to predict future events using linear regression, generalized addition models, random forests, and xgboost.

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Description

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Features

This course includes

Duration

4 hours
Video Content
4 hours
Level
Intermediate
Instruction Type
Self Paced
Delivery Method
Online
Available on
Mobile, Desktop, Laptop
Accessibility
Limited Access
Language
English
Subtitles
English

Skills

Machine Learning AlgorithmsData ModelingRegressionData Analysis

Learning Goals

You'll learn about different regression models, how to train these models in R, how to evaluate the models you train and use them to make predictions
In this chapter we introduce the concept of regression from a machine learning point of view
While more sophisticated regression techniques manage some of these issues automatically, it's important to be aware of them, in order to understand which methods best handle various issues -- and which issues you must still manage yourself
Now that we have mastered linear models, we will begin to look at techniques for modeling situations that don't meet the assumptions of linearity

Course Content

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Prerequisites/Requirements

Introduction to Regression in R

Instructors

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John Mount

Co-founder, Principal Consultant at Win-Vector, LLC

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Nina Zumel

Co-founder, Principal Consultant at Win-Vector, LLC

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Course Overview

Hands-On Training, Instructor-Moderated Discussions

Post course interactions

Virtual labs

International faculty

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