This course covers the major Machine Learning Techniques used in data analysis. It covers supervised and unsupervised algorithms and will be assessed via coursework, a project and a final exam. With the rise of data science and big data fields, machine learning has gained further recogniton as the key driver behind the successful advance of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary approaches so as to be able to modify them for specific uses. Our focus is less on theory and more on practice. Students engage in hands-on implementation of some of the fundamental algorithms such as predictive modeling and clustering applied to real, open-ended problems. While most of the course focuses on machine learning, we also have a few lectures on text/data mining algorithms. On completion of this course students will be able to process datasets and use the extracted information to help develop key business decisions or improve various business processes.