Applied Machine Learning

Course description

In this comprehensive machine learning course, students learn practical skills to tackle real-world problems using machine learning algorithms. They gain hands-on experience that covers the most popular machine learning algorithms, including supervised and unsupervised learning, feature selection and engineering, model evaluation and comparison, ensemble methods, natural language processing, deep learning, and cloud services. Students gain a strong foundation in the theory and practical application of machine learning in various decision-making problems, ensuring a balance between theory and practical application. Throughout the course, students explore the fundamental issues and challenges in machine learning, evaluate various supervised and unsupervised learning algorithms using appropriate datasets, implement the most commonly used machine learning algorithms and perform comparative analysis of multiple algorithms. They discuss feature selection and experimental setup techniques for real-world tasks, and apply machine learning to formulate and solve practical problems in various industries.

Students registering for credit courses for the first time must declare a program at the point of registration. Declaring a program does not necessarily mean students must complete a program, individual courses may be taken for skill improvement and upgrading.

For more information, please contact Continuing Education