Understanding ML vocabulary is essential before going deeper.

Core Terms

• Feature: An input variable used to make predictions (e.g. age, income, pixel value)
• Label: The output you're trying to predict (e.g. spam/not spam, house price)
• Example: One row of data — a set of features with a label
• Model: The mathematical function learned from training data
• Prediction: The output your model produces for new data
• Training set: Data used to train the model
• Test set: Held-out data used to evaluate the model

Reference:

Google ML glossary

https://developers.google.com/machine-learning/glossary