Machine Learning and Deep Learning for AI-Driven Data Analytics: A Review

Abstract

You will find that Machine Learning (ML) and Deep Learning (DL) are now indispensable to AI-powered data analytics, what with their ability to pull advanced insights from even the most voluminous and intricate datasets. In this review we look at the underpinnings of ML and DL, covering the principal methodologies and how they are put to use in fields as diverse as healthcare, finance, cybersecurity, IoT and business intelligence. We delve into the core learning paradigms – supervised, unsupervised and reinforcement – as well as deep learning architectures you would expect to see, from CNNs and RNNs to Transformers. The scope of our study extends to the tools and frameworks of the data processing pipeline that make for scalable analytics. We also take up some of the more pressing issues, namely computational complexity, interpretability and the matter of data quality, while touching on where things are headed with trends in AutoML, XAI, Edge AI and Federated Learning. Put simply, modern data-driven systems owe much of their automation, decision-making and predictive power to these technologies.