Question

once completed, answer the questions below to show what you learned.

1. a large corporation is struggling with email spam reaching employees inboxes. they have a database of 100,000 previously labeled emails (spam/not spam) and want to protect against future spam.

supervised learning - classification based on labeled examples
unsupervised learning - clustering emails based on content
no machine learning needed - simple keyword filtering

1. a major online retailer wants to understand their customer base better to improve their marketing strategy. they have customer purchase history, but no predetermined categories or labels.

supervised learning with customer labels

Explanation:

1. For the corporation with a labeled - email database, supervised learning - classification is appropriate as it has pre - labeled data (spam/ not spam) and aims to predict future spam. In supervised learning, the model is trained on labeled examples to make predictions.
2. For the online retailer with customer purchase history but no predefined categories or labels, unsupervised learning - clustering is suitable. Unsupervised learning is used to find patterns in data without pre - existing labels, and clustering can group similar customers based on their purchase behavior.

Answer:

1. Supervised learning - classification based on labeled examples
2. Unsupervised learning - clustering emails based on content