"Mathematics for Machine Learning: A Deep Dive into Algorithms" is a comprehensive guide that bridges the gap between mathematical theory and practical applications in the dynamic world of Machine Learning. Whether you're a data science enthusiast, a budding Machine Learning engineer, or a seasoned practitioner, this book equips you with the essential mathematical foundations that power cutting-edge algorithms and data-driven insights. Starting with the fundamentals of linear algebra, multivariable calculus, probability, and statistics, Nibedita expertly guides you through the intricate maze of mathematical concepts. From there, you'll explore the depths of linear regression, classification, support vector machines, neural networks, and more, all while unraveling the underlying mathematical principles that make these algorithms tick. This book isn't just about equations and formulas--it's about unlocking the potential of Machine Learning through a strong mathematical intuition.