You only need labeled examples for supervised machine learning tasks, where you must specify the ground truth for your AI model during training. Examples of supervised learning tasks include image classification, facial recognition, sales forecasting, customer churn prediction, and spam detection.

Unsupervised learning, on the other hand, deals with situations where you don’t know the ground truth and want to use machine learning models to find relevant patterns. Examples of unsupervised learning include customer segmentation, anomaly detection in network traffic, and content recommendation.

Semi-supervised learning stands somewhere between the two. It solves classification problems, which means you’ll ultimately need a supervised learning algorithm for the task. But at the same time, you want to train your model without labeling every single training example, for which you’ll get help from unsupervised machine learning techniques.

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