Machine Learning Fundamentals: Supervised Learning with Python

Résumé

Machine Learning Fundamentals: Supervised Learning with Python is an introductory academic book designed for students, beginners in data science, and readers who want to understand how intelligent systems learn from data. The book begins by clarifying the meaning of artificial intelligence, distinguishing AI as a tool from human intelligence, and presenting its history, major branches, practical applications, advantages, and risks such as bias, privacy, job displacement, and environmental cost. It then introduces machine learning as a data-driven approach in which systems improve through examples rather than explicit rules, covering its historical development, main learning paradigms, tools, and the complete workflow of a machine learning project. The central part focuses on supervised learning, explaining labeled data, classification, regression, generalization, empirical and real risk, learning bias, overfitting, and the main stages of model development. It presents key algorithms including perceptron, multilayer perceptron, gradient descent, linear regression, least squares, k-nearest neighbors, support vector machines, and deep learning, with mathematical explanations and visual support. The final chapter introduces Python for machine learning, major libraries, basic programming concepts, and simple implementations. The tone is pedagogical, progressive, and practical.