A Comprehensive Overview of Machine Learning and Deep Learning Algorithms

Abstract

Machine learning and deep learning are two key areas of artificial intelligence (AI) that have attracted a lot of attention in recent years. Despite having the same goal of creating models that learn from data, both techniques have different architectures, algorithms, and applications. The proposed study aims to present a comprehensive overview of machine learning and deep learning algorithms focusing on their applications, data requirements and preprocessing, feature engineering,  complexity and stability, time required to train them, computational resources requirements, interpretability and explainability, processing different data types, performance to handle noise and outliers in huge datasets, strengths, and weaknesses select the most suitable algorithm for a given problem by studying the characteristics of each technique. This study will be useful for researchers and practitioners in selecting the most suitable machine learning or deep learning algorithm for a given problem.