Theoretical Foundations of Machine Learning Johns Hopkins University

Overview

Key facts

From his experience on mission-critical projects at the Johns Hopkins Applied Physics Laboratory and teaching hundreds of online master’s students, Dr. Guven designed a program that gets you up and running quickly with applied, real-world applications.

Through Jupyter notebooks with working code examples, video walkthroughs, quizzes, readings, and hands-on projects, you’ll get the conceptual grounding to reason through model design and performance.

Using tools like scikit-learn, pandas and PyTorch, you’ll build models that don’t just “run,” but make meaningful predictions, reveal patterns, and generalize to new data.

With Dr. Guven’s guidance, in the Theoretical Foundations of Machine Learning from Johns Hopkins University you’ll design powerful systems that recognize images and understand audio. You’ll get the skills to solve real world problems in business intelligence, research, healthcare, and software development using the same cutting-edge deep learning techniques elite researchers and industry giants are using to push the boundaries of science and technology.