Build Random Forests in R with Azure ML Studio

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estrellas
39 calificaciones
ofrecido por
Coursera Project Network
3,244 ya inscrito
En este proyecto guiado, tú:

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

Clock2 hours
BeginnerPrincipiante
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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. This is the fourth course in this series on building machine learning applications using Azure Machine Learning Studio. I highly encourage you to take the first course before proceeding. It has instructions on how to set up your Azure ML account with $200 worth of free credit to get started with running your experiments! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Habilidades que desarrollarás

Data Scienceazure-machine-learningArtificial Intelligence (AI)Machine LearningRandom Forest

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Introduction and Overview

  2. Feature Engineering and Preprocessing

  3. Removing Outliers

  4. Model Building and Training

  5. Scoring and Evaluating the Models

  6. Model Evaluation

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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