This graduate course concentrates on the application of state-of-the-art AI and machine learning algorithms for solving real-world problems. This course starts with reviewing the Python programming language and its important related packages. The covered topics include data preparation, training, evaluation, metrics, supervised learning (regression, classification, neural networks, deep learning, convolutional neural networks, and recurrent neural networks), basics of unsupervised and reinforcement learning, and recommender systems. The course uses active learning techniques to guarantee better engagement from the students. Also, experts from the industry are invited to talk about the practical applications in this domain. This course includes assignments and a practical term project.