This article will cover the following.
- Brief concepts and Prerequisites
- Implementation – Login into the Azure Machine Learning portal.
- Implementation – Extract data from the titanic data-store and create a dataset in Azure
Brief Concept
What is Microsoft Azure Machine Learning?
- It’s a data modeling environment from which we can get an end-to-end approach to a problem to an answer.
- Use Azure Machine Learning to deploy your model into production as a web service in minutes—a web service that can be called from any device, anywhere and that can use any data source.
Prerequisites
Read the following article to create an Azure account and read here for some basic information about Azure to get started.
Implementation – Login into the Azure Machine Learning portal
Steps to be followed.
- Open Azure Machine Learning portal and sign-in into the application using your Azure credentials.
- See the dashboard once you have logged-in into the portal.
Implementation – Extract data from the titanic data-store and create a dataset in Azure
Steps to be followed.
- Go to the following URL and locate the Titanic data store there.
- Download the train.csv file on the file system, which contains the mock-data for the Titanic.
- Go to the portal and click on the Add >> Dataset option from there.
- Choose the local file i.e. train.csv and upload the data into the dataset
- See the dataset section and you will be able to see the newly created dataset there.
Please find the attached train.csv file for reference. Happy learning!
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