Time

5 hours

Difficulty

Intermediate

In this guided lab by Next.Tech, you will utilize foundational knowledge of R in order to approach machine learning model driven regression analysis solutions to validate and measure the performance of said models. More specifically, we will cover linear regression, neural networks, regression trees, variable selection, and more.

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30m

Give Me a Number - Regression

Reading Exercise

30m

Computing the Root-Mean-Square Error

Virtual Lab

30m

Building KNN Models for Regression

Virtual Lab

30m

Performing Linear Regression

Virtual Lab

30m

Performing Variable Selection in Linear Regression

Virtual Lab

30m

Building Regression Trees

Virtual Lab

30m

Building Random Forest Models for Regression

Virtual Lab

30m

Using Neural Networks for Regression

Virtual Lab

30m

Performing K-Fold Cross-Validation

Virtual Lab

30m

Performing Leave-One-Out Cross-Validation to Limit Overfitting

Virtual Lab

In this course, you'll learn to perform regression analysis using the R programming language.

By the end of this course, you will have the skills you need in R to perform regression analysis on data to estimate values in sets . This course will cover: The basics of regression modeling, Computing the root mean squared (RSM) value, Building KNN models, Linear regression, Variable selection in linear regression, Neural networks, Regression trees, Random forest models, K-fold cross validation, and Leave-one-out-cross-validation (LOOCV).

Prerequisites

Prior programming experience is not required! However, you may get more out of this course if you've already learned the basics of R, have some understanding of data science techniques, and have taken Classifiaction Models in R.