This course provides an understanding of data preprocessing and feature engineering as well as machine learning concepts and algorithms using Python. Students will receive hands-on training on supervised learning algorithms such as KNN, Naïve Bayes, Linear and Logistic Regression, Support Vector Machines, Decision Trees and Ensembles, and Artificial Neural Networks (ANNs). Unsupervised learning algorithms such as Association Rules, K-Means, Hierarchical Clustering, t-SNE, and DBSCAN will also be covered. Prerequisite: BSTAT 5325 and INSY 5336.