This book aims to bridge the gap between mathematics and machine learning, showcasing the critical role of mathematics in solving complex data-driven tasks. Each chapter presents key mathematical concepts, accompanied by clear explanations and python code samples, ensuring that readers can grasp the underlying principles. From matrix operations and optimization techniques to probability distributions and statistical inference, the book covers a wide range of mathematical topics that are essential for understanding machine learning algorithms. Additionally, the book explores various machine learning techniques, including linear regression, logistic regression, decision trees, neural networks, and more. By incorporating mathematical rigour into the discussion of machine learning, this book equips readers with the tools they need to effectively analyze and implement machine learning algorithms in practice.