No-Code Machine Learning Using Amazon AWS SageMaker Canvas - Adding Train and Test Data

The video tutorial introduces a project using Amazon AWS SageMaker Canvas, focusing on an SMS spam collection dataset sourced from Kaggle. It explains how to import the dataset into SageMaker, detailing the steps to access and import data from an S3 bucket. The tutorial provides an overview of the train and test datasets, highlighting their sizes. Finally, it sets the stage for the next steps in the modeling process.

What is the source of the SMS spam collection dataset used in the project?

• Amazon AWS

• Microsoft Azure

• Kaggle

• Google Cloud

What is the primary purpose of the SMS spam collection dataset?

• To analyze email spam

• To classify SMS messages as spam or legitimate

• To predict stock prices

• To detect fraudulent transactions

Which section of the AWS SageMaker Canvas interface allows you to add a new dataset?

• Dashboard

• Data Set Section

• New Model

• Settings

Where is the SMS spam dataset stored before being imported into the project?

• AWS S3 Bucket

• Dropbox

• Google Drive

• Local Drive

How many rows are approximately in the test dataset?

• 400

• 300

• 200

• 100