This is a dedicated watch page for a single video.
A financial services company has tasked its ML team to analyze customer transaction data for enhancing its fraud detection system and identify potential customer segments for targeted marketing campaigns. The company has a dataset with multiple features, including transaction amounts, frequency, and location data. The tasks are listed below, and the ML team must use Amazon SageMaker built-in algorithms to complete these tasks: Reduce the dimensionality of the dataset to improve model performance and visualization. Perform cluster analysis to identify distinct customer groups. Detect anomalous transactions that may indicate fraud. Which combination of SageMaker built-in algorithms should the ML team use to meet the requirements?