Why Unsupervised Learning is Indispensable Part of Artificial Intelligence? Learn Types, Clustering, Applications and Opportunities - What and How?
Abstract : Unsupervised learning is a machine learning problem type in which training data consists of a set of input vectors but no corresponding target values. The idea behind this type of learning is to group information based on similarities, patterns, and differences. Unlike in supervised learning problems, unsupervised learning algorithms do not require input-to-output mappings to learn a mapping function—this is what is meant when we say, “no teacher is provided to the learning algorithm.” Consequently, an unsupervised learning algorithm cannot perform classification or regression. Keywords :Unsupervised Learning, Clustering, Association rule, Dimensionality Reduction, Contents 1. Introduction 2. AI and Unsupervised Learning 3. Why Unsupervised Learning so Important? 4. Types of Unsupervised Learning 5. Clustering 6. Association Rule 7. Dimensionality Reduction 8. Differences between Supervised Learning and Unsupervised Learning 9. Practical Examples 10. Conclusions Let'