A step-by-step tutorial on machine learning for engineers unfamiliar with programming

Machine learning (ML) has garnered significant attention within the engineering domain. However, engineers without formal ML education or programming expertise may encounter difficulties when attempting to integrate ML into their work processes. This study aims to address this challenge by offering a tutorial that guides readers through the construction of ML models using Python. We introduce three simple datasets and illustrate how to preprocess the data for regression, classification, and clustering tasks. Subsequently, we navigate readers through the model development process utilizing well-established libraries such as NumPy, pandas, scikit-learn, and matplotlib. Each step, including data preparation, model training, validation, and result visualization, is covered with detailed explanations. Furthermore, we explore explainability techniques to help engineers understand the underlying behavior of their models. By the end of this tutorial, readers will have hands-on experience with three fundamental ML tasks and understand how to evaluate and explain the developed models to make engineering projects efficient and transparent.