```json
{
	"no": ["unknown", "primary", "secondary"],  
	"yes": ["tertiary"]  
}
```

Based on the given feature description and task, it can be observed that the education level of the client is a categorical variable with four categories: 'tertiary', 'secondary', 'unknown', and 'primary'. To analyze the relationship between the feature and the target variable, we need to consider the education level for clients who did not subscribe to a term deposit (target class no) and those who did subscribe (target class yes).

From the analysis, it can be seen that clients with an education level of 'unknown', 'primary', or 'secondary' tend to not subscribe to a term deposit. On the other hand, clients with an education level of 'tertiary' tend to subscribe to a term deposit.

Therefore, the dictionary shows that for the target class "no" (did not subscribe to a term deposit), the possible values of the education feature are 'unknown', 'primary', and 'secondary'. For the target class "yes" (subscribed to a term deposit), the possible value of the education feature is 'tertiary'.