The mission of this course is to turn you into a productive, innovative data analyst who can leverage Go to build robust and valuable applications. To this end, the course clearly introduces the technical aspects of building predictive models in Go, but also helps you understand how machine learning workflows are applied in real-world scenarios.

This course shows you how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives you patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization.

You’ll begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Then you’ll develop a solid statistical toolkit that will allow you to quickly understand gain intuition about the content of a dataset. Finally, you’ll gain hands-on experience of implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages.

By the end, you’ll have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations