Program Courses
Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.
CIS 435-DL : Practical Data Science Using Machine Learning
Description
This course provides an overview of machine learning concepts, techniques, and tools that will help you deepen your understanding of large, complex datasets and your knowledge of intelligent systems. You will learn machine learning techniques that can optimize business processes, identify new revenue models, and support evidence-based decision making in industries such as finance, retail, and health care.
You will develop skills that will help you deconstruct a business problem into actionable tasks that include exploratory data analysis and visualization, data preprocessing and dimensionality reduction, algorithm selection, and model evaluation, optimization, and deployment. Open-source development frameworks, including Python and the Scikit-Learn and TensorFlow libraries, are used to implement supervised and unsupervised learning methods. You will work with a variety of machine learning algorithms. Regression, classification, regularization, decision trees, clustering, Bayesian, ensemble, dimensionality reduction, and different neural networks will all be explored.
Prerequisites: CIS 414 or MSDS 430
Recommended: CIS 417
Winter 2025 | ||||
Start/End Dates | Day(s) | Time | Building | Section |
01/06/25 - 03/22/25 | Sync Session WW | 7 – 9:30 p.m. | 55 | |
Instructor | Course Location | Status | CAESAR Course ID | |
Kakade, Sunil | Online | Open |