When we train a supervised learning model, we give it examples that include both:

• Input data (what we know)
• Labels (what we want the computer to predict)

The machine learns to match inputs to the right labels so it can later make accurate predictions on new data.

Here are some examples:

An unsupervised learning model also takes data as input, but it does not have any correct answer labels. It looks for patterns in the data and forms clusters.

Here is a table comparing supervised and unsupervised learning:

Reinforcement Learning vs Transfer Learning

Make a copy of the worksheet. Move it into your Theme A4 folder.

Follow the instructions and complete the tasks.