Machine learning has become a cornerstone of modern technology, driving innovation across fields such as healthcare, finance, transportation, and more. Yet, understanding its core principles and effectively applying its methods remains a challenge for many. *The Essentials of Machine Learning: Theory to Applications* bridges this gap by providing a balanced exploration of the theoretical foundations and practical applications of machine learning. This book is designed to cater to students, researchers, and professionals, offering a clear and structured approach to learning. It covers essential topics such as supervised and unsupervised learning, neural networks, decision trees, and advanced techniques like deep learning and reinforcement learning. Each concept is paired with real-world applications, demonstrating how these methods solve complex problems in various industries. Initiated in early 2023 and completed after two years of meticulous effort, this book reflects the commitment to making machine learning accessible and actionable. Whether you're a beginner exploring...