Upon completing this Specialization, you’ll be able to build supervised machine learning models, design and train deep learning architectures in PyTorch, and optimize models for stronger performance. You’ll practice the kinds of tasks used in machine learning engineering roles, including framing ML problems, selecting model approaches, debugging training issues, and explaining technical decisions.

These skills can help you prepare for roles such as Machine Learning Engineer, Deep Learning Engineer, AI Engineer, Computer Vision Engineer, NLP Engineer, Applied Scientist, or modeling-focused Data Scientist.

You’ll learn from curated lessons taught by expert instructors across the platform. Each lesson is selected for the specific skill it teaches best, so you can benefit from a range of perspectives while focusing on the machine learning and deep learning skills most relevant to your goals.

No. While the courses are designed to build from supervised machine learning to advanced deep learning model engineering, you can personalize your path based on your experience and goals. Each course is organized around job tasks and skills, so you can start where it makes the most sense, focus on the skills you need, and skip ahead when you already know a topic.