Machine learning algorithms review

Abstract

Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machine learning and its algorithms. It will examine different types of machine learning models and introduce their most popular algorithms. The methodology of this paper is a literature review, which examines the most commonly used machine learning algorithms in the current field. Such algorithms include Nave Bayes, Decision Tree, KNN, and K-Mean Cluster. Nowadays, machine learning is everywhere and almost everyone using a technology product is enjoying its convenience. Applications like spam mail classification, image recognition, personalized product recommendations, and natural language processing all use machine learning algorithms. The conclusion is that there is no single algorithm that can solve all the problems. The choice of the use of algorithms and models must depend on the specific problem.

Evaluation of Water Quality Assessment Through Machine Learning: A Water Quality Index-Based Approach

“…These algorithms learn from data and extract patterns and features to improve their performance. They have become widely used in various applications, such as spam mail classi cation, image recognition, personalized product recommendations, and natural language processing (Ling, 2023). Machine learning is a sub eld of arti cial intelligence (AI) and has a subset called deep learning, which uses deep neural networks to learn from complex data.…”