As an example, an algorithm might be fed a more compact amount of labeled speech data after which you can skilled on the much bigger list of unlabeled speech data so that you can produce a machine learning model able to speech recognition.Semi-supervised learning can fix the trouble of not possessing plenty of labeled data to get a supervised learn