semi-supervised learning uses a combination of supervised and unsupervised learning techniques and that's because in a scenario where we'd make use of semi-supervised learning we'd have a combination of both labeled and unlabeled data let's expand on this idea with an example say we have access to a large unlabeled data set that we'd like to train a model on and that manually labeling all of the state ourselves is just not practical well we could go through and manually label some portion of this large data set ourselves