In the previous unit, we covered the three main paradigms of machine learning: Supervised, Unsupervised, and Reinforcement Learning. As a quick recap, supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning uses rewards and penalties.

Engagement Message

Ready to apply these concepts to some new scenarios?

Type

Sort Into Boxes

Practice Question

Sort these machine learning tasks into the correct paradigm based on the data they would use.

Labels

• First Box Label: Supervised Learning
• Second Box Label: Unsupervised Learning

First Box Items

• Predicting house prices
• Identifying fraudulent transactions
• Diagnosing diseases from scans

Second Box Items

• Grouping shoppers by behavior
• Recommending movies based on similar users
• Segmenting a customer base

Type

Multiple Choice

Practice Question

A self-driving car's algorithm is adjusted every time it makes a mistake during a simulation, like bumping into a virtual curb. It receives a "penalty" for the error and a "reward" for driving correctly. This is an example of:

A. Supervised Learning B. Unsupervised Learning C. Reinforcement Learning D. Data Labeling

Suggested Answers

• A
• B
• C - Correct
• D

Type

Fill In The Blanks

Markdown With Blanks

Let's practice identifying features and labels. For a model that predicts whether a student will pass an exam, fill in the blanks.