Book Image

Machine Learning With Go

Book Image

Machine Learning With Go

Overview of this book

The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.
Table of Contents (11 chapters)

Understanding classification model jargon

As with regression, classification problems come with their own set of jargon. There is some overlap with terms used in regression, but there are also some new terms specific to classification:

  • Categories, labels, or classes: These terms are used interchangeably to represent the various distinct choices for our prediction. For example, we could have a fraud class and a not fraud class, or we could have sitting, standing, running, and walking categories.
  • Binary classification: This type of classification is one with only two categories or classes, such as yes/no or fraud/not fraud.
  • Multi-class classification: This type of classification is one with more than two classes, such as a classification trying to assign one of hot dog, airplane, cat, and so on, to an image.
  • Labeled data or annotated data: Real-world observations or records that...