This chapter presents an overview of machine learning concepts and their applications in biomedicine, with a focus on methods and basic understanding. It outlines main categories of machine learning and describes supervised learning techniques such as linear regression, logistic regression, decision trees, ensemble methods, support vector machines, and deep learning. The chapter also discuss unsupervised learning methods like clustering, dimensionality reduction, and anomaly detection, as well as semi-supervised and reinforcement learning. Techniques for data preprocessing and model optimization are discussed, along with methods for model evaluation. The chapter concludes with example implementations on drug sensitivity prediction using logistic regression, Random Forest, and the AlphaML platform.