This chapter introduces the foundational principles of machine learning, providing a comprehensive overview of the key components involved in building effective learning systems. It begins with data handling and preparation, emphasizing the importance of data quality, preprocessing techniques, and feature engineering. The chapter then explores core learning algorithms, including supervised, unsupervised, and semi-supervised methods, highlighting their underlying principles and practical applications. Model evaluation and interpretation are discussed in depth as well as the deployment and operationalization of machine learning models.