Your next step
The examples shape the model
Inspect a collection for the faults that matter.
Start learningWhat you’ll learn
A feature is an input clue
Identify the information available for a prediction.
About 4 minutes · Open activity
What does one example represent?
Choose the correct unit of an example.
About 4 minutes · Open activity
Where did these examples come from?
Identify missing information about collection and permitted use.
About 4 minutes · Open activity
Wrong labels teach the wrong target
Recognize a labeling error.
About 4 minutes · Open activity
Repeated examples can distort the picture
Identify duplication in a training collection.
About 4 minutes · Open activity
Who and what are missing?
Identify a coverage gap relative to intended use.
About 4 minutes · Open activity
A shortcut can look like learning
Detect an irrelevant clue that predicts the training label.
About 4 minutes · Open activity
Make useful variations of an example
Recognize data augmentation that preserves the intended target.
About 4 minutes · Open activity
Keep a record of the dataset
Explain why a dataset needs a version and collection notes.
About 4 minutes · Open activity
Repair the parcel dataset
Find one label problem, one coverage gap and one source or documentation issue in a collection.
About 6 minutes · Open activity