Machine Learning with Go [Video]

More Information
  • Find out about data gathering, organization, parsing, and cleaning
  • Explore matrices, linear algebra, statistics, and probability
  • See how to evaluate and validate models
  • Look at regression, classification, clustering
  • Find out about neural networks and deep learning
  • Utilize times series models and anomaly detection
  • Get to grip with techniques for deploying and distributing analyses and models
  • Optimize machine learning workflow techniques

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

Style and Approach

This course connects the fundamental, theoretical concepts behind Machine Learning to practical implementations using the Go programming language. It will give you the practical skills to perform the most common machine learning tasks with Go.

  • Build simple, but powerful, machine learning applications that leverage Go’s standard library along with popular Go packages
  • Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go
  • Understand when and how to integrate certain types of machine learning model in Go applications
Course Length 2 hours 48 minutes
ISBN 9781789134735
Date Of Publication 28 Feb 2018


Daniel Whitenack

Daniel Whitenack is a trained PhD data scientist with over 10 years' experience working on data-intensive applications in industry and academia. Recently, Daniel has focused his development efforts on open source projects related to running machine learning (ML) and artificial intelligence (AI) in cloud-native infrastructure (Kubernetes, for instance), maintaining reproducibility and provenance for complex data pipelines, and implementing ML/AI methods in new languages such as Go. Daniel co-hosts the Practical AI podcast, teaches data science/engineering at Ardan Labs and Purdue University, and has spoken at conferences around the world (including ODSC, PyCon, DataEngConf, QCon, GopherCon, Spark Summit, and Applied ML Days, among others).