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)

References

Time series statistics (ACF and PACF):

  • How to use ACF: https://coolstatsblog.com/2013/08/07/how-to-use-the-autocorreation-function-acf/
  • Identifying the number of AR or MA terms in an ARIMA model: https://people.duke.edu/~rnau/411arim3.htm

Auto-regressive models:

  • A more mathematical introduction to AR models: https://onlinecourses.science.psu.edu/stat501/node/358
  • github.com/sajari/regression docs: https://godoc.org/github.com/sajari/regression

ARMA/ARIMA models:

  • Introduction to ARIMA: https://people.duke.edu/~rnau/411arim.htm

Anomaly detection:

  • InfluxDB: https://www.influxdata.com/
  • Prometheus: https://prometheus.io/
  • github.com/lytics/anomalyzer docs: https://godoc.org/github.com/lytics/anomalyzer
  • github.com/sec51/goanomaly docs: https://godoc.org/github.com/sec51/goanomaly