The field of Machine Learning can be overwhelming, and you might feel like you're losing control. Instead of giving up, let me share a secret with you: it's easier than you think. All you need is a "Rosetta Stone" to fully accelerate your work with machine learning. This book takes you on an alternative route, starting with the fundamental concepts from calculus, linear algebra, numerical methods, and optimization, leading up to the state-of-the-art algorithms that have emerged over the last couple of decades. This is an ambitious promise, but my background in industry, research, and teaching has given me deep insights into the common challenges of developing efficient algorithms for prediction. This book will focus on the coding perspective—a "from-scratch" approach where the configuration includes setting up the environment properly, defining the dataset, and configuring training and validation. Moreover, this book will dedicate significant effort to eliminating version-related errors that could cause problems when maintaining the code for future development.