Machine Learning/AI Engineer

Machine Learning affects nearly everything we do, from how we shop, how our healthcare works, and how we navigate a new (or familiar) city. With so much of the world shaped by machine learning, understanding how it works and how to do it will both help you make sense of the world around us and gain valuable skills for the modern workplace. So why wait? Let’s dive in to the exciting tools reshaping our world. 

Basic software engineering skills are essential to working on engineering teams, and are at the heart of what differentiates the ""engineer"" title from the ""scientist"" title. As you will see in this Skill Path, engineering tools are a little different from machine learning tools (i.e., bash scripting, unit testing, and git), but the goals are the same: solving problems with data. By the end of this Skill Path, you will be ready to bridge the gap between machine learning and software engineering and work with engineering teams.

You'll learn how to make your code more efficient, resilient, and organized. By diving deep into Git and Github, you'll become a master communicator, able to collaborate across engineering teams and keep your code up to date. 

So many of the applications we use today are driven by algorithms powered by machine learning. Building upon foundational machine learning algorithms, this track will introduce the learner to more advanced algorithms and how to combine, tune and regularize models to make better predictions on data.

Learn how to build machine pipelines!

Show off your knowledge of machine learning engineering by developing your final portfolio project on a topic of your choice.