Machine Learning with Go

Course details

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.

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

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

About the Author

Daniel Whitenack (@dwhitena), PhD, is a trained data scientist working with Pachyderm (@pachydermIO). Daniel develops innovative, distributed data pipelines that include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (GopherCon, JuliaCon, PyCon, ODSC, Spark Summit, and more), teaches data science/engineering at Purdue University (@LifeAtPurdue), and, with Ardan Labs (@ardanlabs), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.