Supervised, unsupervised and semi-supervised learning

Machine learning methods are categorized into three main types: supervised, unsupervised, and semi-supervised learning.

• Supervised learning uses labeled data, where both input and desired output are known. The model learns to map inputs to outputs based on these labeled examples.
• Unsupervised learning works only with input data, discovering patterns and structures without predefined output labels. It is commonly used for clustering tasks.
• Semi-supervised learning combines both approaches, using a small set of labeled data along with a larger set of unlabeled data. This method helps reduce the effort required for data preparation, especially in deep learning applications that demand large datasets.

The main challenge across all types is the effort needed to create and label high-quality datasets, particularly for deeper networks.