The discussed article provides a comprehensive overview of the universal workflow for developing machine learning models, offering a step-by-step guide that can serve as a checklist for both beginners and experienced practitioners in artificial intelligence. It begins with the importance of contextualizing the machine learning project by clearly defining its objectives and success criteria. The article then delves into data exploration and the selection of appropriate algorithms, highlighting supervised, unsupervised, and reinforcement learning as key approaches. Emphasizing the significance of high-quality data, it details the processes of data collection, preprocessing, and cleaning, including handling non-numeric columns, missing values, and outliers. The article further explains model evaluation methods such as hold-out validation, K-fold validation, and iterated K-fold validation with shuffling. It also covers the building of a benchmark model and the importance of model optimization to enhance precision and effectiveness, discussing both learned parameters and hyperparameters. The conclusion reinforces the article's utility as both a refresher for seasoned data scientists and a foundational guide for newcomers embarking on their first machine learning projects.