Machine Learning Using Python (MLPY): A Comprehensive Study of Models and Techniques

Abstract

Machine Learning with Python (MLPY) has emerged as a cornerstone of modern data-driven research and intelligent system development. Python’s simplicity, extensive libraries, and strong community support make it one of the most widely adopted programming languages for implementing machine learning models. This article presents a comprehensive overview of machine learning concepts using Python, focusing on both theoretical foundations and practical applications. It explores core learning paradigms, including supervised, unsupervised, and reinforcement learning, while emphasizing commonly used algorithms such as linear regression, decision trees, support vector machines, k-means clustering, and neural networks. The study highlights the role of popular Python libraries, including NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch, in facilitating efficient data preprocessing, model training, evaluation, and visualization. Additionally, the article discusses real-world applications of MLPY across various domains, such as healthcare, finance, natural language processing, and computer vision. Challenges related to data quality, model interpretability, and computational complexity are also addressed. By integrating conceptual explanations with practical insights, this work aims to provide learners, researchers, and practitioners with a clear understanding of how machine learning techniques can be effectively developed and deployed using Python to solve complex real-world problems.