Introduction to machine learning / Jacob Pearson.

Includes bibliographical references and index.

Introduction to Machine Learning by Jacob Pearson offers a foundational overview of the core principles, techniques, and applications of machine learning within the broader field of Artificial Intelligence. The book introduces key concepts such as supervised and unsupervised learning, model evaluation, feature selection, and common algorithms including regression, classification, clustering, and neural networks. It balances theoretical understanding with practical implementation, often illustrating how data-driven models are built, trained, and optimized for real-world problems. Emphasis is placed on interpreting results, avoiding overfitting, and understanding the ethical implications of automated decision-making. Designed for beginners and early learners, it serves as a bridge between basic statistical reasoning and more advanced machine learning methodologies.