Introduction
Machine learning is driving innovation across industries, but the terminology can feel overwhelming. At its core, most machine learning tasks fit into one of two paradigms: supervised or unsupervised learning.
Supervised Learning
Supervised learning algorithms are trained on labeled data. The model learns mapping functions between inputs and known targets. Typical tasks include spam detection (classification) and house price prediction (regression).
Unsupervised Learning
Unsupervised learning deals with unlabeled datasets. The model attempts to find underlying patterns, groupings, or structures on its own. Common tasks include customer segmentation (clustering) and recommendation engine associations.
Conclusion
Choosing between these methods depends on the nature of your data and the specific problem you want your model to solve.