ROLE OF ALGORITHMS IN MACHINE LEARNING

Abstract: Machine learning has emerged as a transformative force in various sectors, including healthcare, finance, transportation, and more. At the heart of machine learning are algorithms—structured sequences of instructions that enable computers to learn patterns from data. This thesis explores the critical role algorithms play in the machine learning process, from data preprocessing to model training and evaluation. It examines different types of algorithms, such as supervised, unsupervised, and reinforcement learning algorithms, and their respective applications. Additionally, it evaluates how algorithm selection impacts model performance and discusses current trends such as automated machine learning (AutoML) and algorithmic fairness. A Machine Learning algorithm, which is a part of AI, uses an assortment of accurate, probabilistic, and upgraded techniques that empower computers to pick up from the past point of reference and perceive hard to perceive patterns from massive, noisy, or complex data.