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)

Time Series and Anomaly Detection

Most of the models that we have discussed up to this point predict a property about something based on other properties related to that something. For example, we predicted the species of a flower based on measurements of the flower. We also tried to predict the progression of the disease diabetes in a patient based on medical attributes about that patient.

The premise of time series modeling is different from these types of property prediction problems. Simply put, time series modeling helps us predict the future based on attributes about the past. For example, we may want to predict future stock prices based on previous values of that stock price, or we may want to predict how many users will be on our website at a certain time based on data about how many users were on our website at previous times. This is sometimes called forecasting.

The...