Introduction Machine Learning is a large domain that drives today's smart systems. It is generally divided into supervised and unsupervised learning. If you are starting out, you may want to take a look at our comprehensive Introduction to Machine Learning blog, where we discuss the types, applications, and real-world applications in detail. In this era of huge datasets and real-time data creation, interpreting raw, unlabeled data is a serious challenge. Unsupervised learning comes into action here. In contrast to its opposite, supervised learning, which demands labeled data for training, unsupervised learning enables the machine to identify hidden patterns, clustering, or structures independently. It replicates how naturally humans collate information about training, Whether it’s identifying customer segments, detecting anomalies in financial transactions, or finding relationships in social networks, unsupervised learning has proven to be an essential tool in the machine lear...