This course builds from Machine Learning I and will add the time series Auto-Regressive Integrated Moving Average (ARIMA) and Naïve Bayes model to the student’s portfolio. Students will also learn neural networks (NN)—the bedrock of deep learning. In this course, students are tasked to construct their own single-layer artificial neural network in Python from “scratch.” This will allow students to gain a deeper understanding of how neural networks work in preparation for deep learning methods in Machine Learning 3.0. As in ML-I, this is a hands-on and application-heavy module that looks at real-world data and cases in different fields. Students will also strengthen their ability to interpret machine learning models and closely link their results with the (business) value expected by stakeholders. The most recent developments in machine learning is also discussed in class through journal scans and presentations. The culminating activity is a public presentation of students’ projects that will be assessed based on the impact, novelty, and mastery of the machine learning methodologies.