Machine Learning Fundamentals

Abstract

Machine learning (ML) is a topic of study focused on comprehending and developing “learning” methods, or methods that use data to enhance performance on a certain set of tasks. It is considered to be a component of artificial intelligence. The Types of learning in Machine Learning are Supervised Learning: uses labeled data for model training, Unsupervised Learning: uses unlabeled data for model training. When labeled data is not available (there is no result to predict), the learning purpose is to find hidden similarities, groups or clusters among examples, or to determine characteristics in the data structure. Reinforcement Learning: consists of a trained agent that learns on the basis of rewards or penalties. The Model techniques used in machine learning based models are: 1) Classification: prediction task of categorical values in supervised learning. 2) Regression: prediction task of continuous values in supervised learning. 3) Clustering: find groups or similarities in data in unsupervised learning. 4) Dimensionality reduction (DR): reduce the number of variables/features in data in unsupervised learning. Among the types of learning, each machine learning consists of variety of algorithms and performance measures, which is aligned with various model techniques. This chapter focuses on all the types of machine learning algorithms such as Support vector machine, Discriminant Analysis, Naïve Bayes, K nearest neighbor, K Means, Decision tree, principal component analysis, etc. © 2024 Scrivener Publishing LLC.