In this project-based course you will learn to perform feature engineering and create custom R models on Azure ML Studio, all without writing a single line of code! You will build a Random Forests model in Azure ML Studio using the R programming language. The data to be used in this course is the Bike Sharing Dataset. The dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information. Using the information from the dataset, you can build a model to predict the number of bikes rented during certain weather conditions. You will leverage the Execute R Script and Create R Model modules to run R scripts from the Azure ML Studio experiment perform feature engineering.
Build Random Forests in R with Azure ML Studio
Taught in English
Instructor: Snehan Kekre
6,894 already enrolled
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Recommended experience
(66 reviews)
What you'll learn
Train and evaluate a regression model on Azure ML Studio
Perform feature Engineering and data pre-processing using custom R scripts
Write custom machine learning models in R
Skills you'll practice
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Guided Project
Recommended experience
(66 reviews)
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Learn, practice, and apply job-ready skills in less than 2 hours
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction and Overview
Feature Engineering and Preprocessing
Removing Outliers
Model Building and Training
Scoring and Evaluating the Models
Model Evaluation
Recommended experience
A basic understanding of machine learning workflows. Some prior experience with programming in R is preferred.
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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