This book focuses on practical applications of machine learning models in biomedical engineering with "hands-on" tutorials using Python and existing libraries such as Scikit-Learn and PyTorch. It provides an overview of a broad range of machine learning models such as decision trees, random forests, regression, neural networks, principal component analysis and clustering with examples drawn from machine learning applications on biomedical datasets (e.g., clinical data, physiological signals and medical images) and diseases (e.g., cancer and cardiovascular disease). The book also covers the basic approaches for model selection and performance assessment (e.g., use of cross-validation and receiver operating characteristic curves) as well as strengths and limitations of each machine learning algorithm. This book serves as a textbook for an introductory machine learning class, where educators can easily incorporate machine learning into the biomedical engineering curriculum. Supplemental materials such as lecture slides, codes and exercises are provided.