A hands-on capstone project course to gain experience applying machine learning and signal processing concepts. Students will be responsible for defining an appropriate capstone project and delivering a final report and presentation at the end of the course. The course will cover applied topics in machine learning and signal processing to assist students in completion of their projects. Topics include Python data structures, Python libraries such as Pandas, Scikit-learn, Keras, TensorFlow, PyTorch, and SciPy Signal Processing. The course will also cover basic data programming in SQL, scripting in Linux, version control, parallel computing basics and introduce distributed computing environments, including compute resources available on campus and in the cloud. The course will cover scaling up a project using parallel processing, graphics processing units (GPUs), and technologies such as Apache MapReduce and Apache Spark. The course will also feature guest speakers from academia and industry.