The previous two chapters discussed topics related to network programming, TCP/IP, HTTPS, RPC, and the net package. This chapter will talk about machine learning in Go, including many interesting topics such as calculating statistical properties, classification, regression, clustering, anomaly detection, neural networks, outlier analysis, and working with Apache Kafka. However, as all these are huge topics that deserve a book on their own, this chapter will only scratch the surface and give you a quick introduction to them, as well as introduce you to some handy Go packages that can help you to do the job.
Notice that each machine learning technique has some theory behind it – knowing the theory, the parameters, and the limitations of the techniques you are trying to use is essential for the success of your work. Additionally, visualizing your data...
