Foundations of Machine Learning for Artificial Intelligence

This comprehensive textbook provides a clear and structured introduction to the principles of machine learning that power modern artificial intelligence. It covers essential algorithms, including supervised and unsupervised learning, neural networks, and reinforcement learning. Through examples and exercises, readers will understand model training, evaluation, and optimization. Designed for students and AI practitioners, the book emphasizes both mathematical foundations and real-world applications. By linking theory with coding practice, it equips learners with the skills to build intelligent systems and apply machine learning to solve practical challenges in various domains.