Physics-informed machine learning (PIML) is a methodology that combines principles from physics with machine learning (ML) techniques to enhance the accuracy and interpretability of predictive models. By incorporating physical laws and constraints into the learning process, physics-informed machine learning enables more robust predictions and reduces the need for large amounts of training data. PIML has a wide range of applications in science and engineering, such as modeling physical systems, solving partial differential equations, and performing inverse analysis and optimization. In part I of this two-part series, the authors will provide attendees with an overview of the main concepts, methods, applications, and challenges of PIML. According to the way that a first-principle model is integrated with a data-driven ML model, it is possible to classify physics-informed strategies. In this overview, seven strategies will be covered: physics-constrained ML; physics-guided ML; physics-encoded ML; data-augmentation via physics principles; transfer learning from physics-based synthetic data to experimental data; delta-learning physics correction to improve physics generalization and delta-learning unknown physics to represent unmodeled physical phenomena. The benefits of these approaches including better generalization, explainability, and efficiency of the ML models will be addressed. This work will present related challenges and limitations of each approach. Finally, the authors will discuss some open research questions and future directions for PIML. By the end of this tutorial, the participants will have a comprehensive understanding of the principles and potential of PIML, as well as the ability to critically evaluate PIML models.