Project: Build Random Forests in R with Azure ML Studio

提供方
Rhyme
在此指導項目中,您將:

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
Beginner初級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English) + subtitles
Laptop不適用於移動設備

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.

您要培養的技能

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

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的講師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的虛擬桌面,無需下載。

在分屏視頻中,您的講師會為您提供分步指導

常見問題

常見問題

  • 購買項目後,您將獲得完成項目所需的一切內容,包括通過 Web 瀏覽器訪問云桌面工作空間,其中包含您需要了解的文件和軟件,以及特定領域的專家提供的分步視頻說明。

  • 因為您的工作空間包含適合筆記本電腦或台式計算機使用的雲桌面,所以項目不在移動設備上使用。

  • 項目講師是特定領域的專家,他們在項目的技能、工具或領域上都很有經驗,並且熱衷於分享自己的知識以影響全球數百萬的學生。

  • 您可以從項目中下載並保留您創建的任何文件。為此,您可以在訪問云桌面時使用‘文件瀏覽器’功能。

  • 項目沒有助學金。

  • 您不需要任何前期經驗即可開始項目。講師將逐步指導您完成項目。

  • 是,您可以在瀏覽器的雲桌面中獲得完成項目所需的一切。

  • 您可以通過直接在瀏覽器中的分屏環境中完成項目來進行學習。在屏幕的左側,您將在工作空間中完成任務。在屏幕的右側,您將看到有講師逐步指導您完成項目。