Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. This course provides an introduction to machine learning. Topics include: (i) Supervised learning (regression and classification). (ii)Unsupervised learning (clustering and dimensionality reduction). (iii) Case studies and applications in machine learning to learn how to apply learning algorithms.