Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

The paper presents a detailed exploration of machine learning (ML) and deep learning (DL), focusing on the theoretical foundations, core algorithms, and practical applications across diverse industries. It delves into the fundamental categories of machine learning, namely supervised, unsupervised, and reinforcement learning, highlighting key algorithms, working principles, and use cases. In parallel, the paper investigates the architecture and application of deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), showcasing their transformative role in image recognition, natural language processing, time series analysis, and autonomous systems. Through comparative analysis and visualization, the paper bridges theoretical constructs with domain-specific implementations, offering valuable insights into how intelligent systems can be developed using modern data-driven approaches.

Keywords: Machine Learning, Deep Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, CNN, RNN, Neural Networks, Data-Driven Models, Artificial Intelligence