Machine Learning and Deep Learning for Big Data Analytics: A Review of Methods and Applications

Abstract

The rapid increase in data creation poses significant challenges and also opens up possibilities for innovation that hinges on data analysis. This review explores how machine learning (ML) and deep learning (DL) techniques are used in in-depth data analysis, focusing on modern advancements, methodologies, and practical implementations. A comprehensive examination is conducted on ML methods designed for large datasets, covering approaches of supervised, unsupervised, and reinforcement learning. Also, a study is performed on various DL structures, like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, known for their ability to identify complex patterns in datasets with high dimensionality. Preparation of data and creation of features are crucial for enhancing the quality and usefulness of data; in this discussion, methods for handling noise, addressing missing data, and selecting relevant features are explained. Furthermore, the significant impacts of ML and DL in different sectors such as healthcare, finance, and retail are highlighted, emphasizing their transformative effects. The conversation also addresses the difficulties related to scaling up and improving performance, crucial for effectively using machine learning and deep learning models with large amounts of data. Examining new developments like automated machine learning, edge computing, and the potential integration of quantum computing with ML provides insight into the future direction of advanced data analysis. Ethical concerns, including data privacy, bias, and interpretability of models, are carefully analyzed to ensure the responsible use of these technologies. This document aims to be a thorough source of information for researchers and practitioners looking to utilize ML and DL for advanced data analysis.