No-Code Machine Learning Using Amazon AWS SageMaker Canvas - Adding Training Data

This video tutorial covers a machine learning project using Amazon SageMaker Canvas. It begins with an introduction to the project and the data sourced from the UCI Machine Learning repository, focusing on banknote authentication. The data attributes include variance, skewness, kurtosis, and entropy, with class labels indicating genuine or forged notes. The tutorial explains how to split the data into training and test sets, upload it to SageMaker Canvas, and manage datasets, including deletion and status checks. It also covers options for joining datasets within the Canvas interface.

What is the source of the dataset used in the project?

• Amazon Web Services

• UCI Machine Learning Repository

• Kaggle

• Google Dataset Search

Which feature is NOT extracted from the banknote images?

• Brightness

• Entropy

• Skewness

• Variance

What percentage of the dataset is used for training?

• 70%

• 60%

• 80%

• 50%

Where is the dataset uploaded before being used in SageMaker Canvas?

• Local Storage

• Dropbox

• Google Drive

• S3 Bucket

What operation can be performed on datasets within SageMaker Canvas?

• Data Encryption

• Data Joining

• Data Compression

• Data Translation