Machine Learning (ML) is a subset within AI associated with providing machines the ability to learn from experience without the need to be programmed explicitly. Or in simple words, ML or machine learning is a part of AI. So while all ML models are, by default, AI models, the opposite may not always be true.
In ML, it’s important to distinguish between supervised vs. unsupervised learning, and a hybrid version named semi-supervised learning. In short, supervised learning is where the algorithm is given a set of training data. Supervised models learn from ground truth data that was labeled manually by data scientists. In computer vision, this process is called image annotation. The model uses this data to learn (training) how to make predictions on new data (inferencing).
On the other hand, unsupervised learning is where the algorithm is given raw data that is not annotated. Here, the algorithm is not explicitly told what to do with it and must learn how to make predictions by itself. This type of ML model is suitable to perform specific tasks on distinct data types, for example, fraud detection or financial analysis, that require identifying a hidden structure in unlabeled data.