Book Image

Go Machine Learning Projects

By : Xuanyi Chew
Book Image

Go Machine Learning Projects

By: Xuanyi Chew

Overview of this book

Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This book will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured. The book begins by guiding you through setting up your machine learning environment with Go libraries and capabilities. You will then plunge into regression analysis of a real-life house pricing dataset and build a classification model in Go to classify emails as spam or ham. Using Gonum, Gorgonia, and STL, you will explore time series analysis along with decomposition and clean up your personal Twitter timeline by clustering tweets. In addition to this, you will learn how to recognize handwriting using neural networks and convolutional neural networks. Lastly, you'll learn how to choose the most appropriate machine learning algorithms to use for your projects with the help of a facial detection project. By the end of this book, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound understanding of the practical implementations of machine learning algorithms in real-world projects.
Table of Contents (12 chapters)

Forecasting

We're decomposing a time series here with the STL algorithm. There are other methods of decomposing time series—you may be familiar with one: the discrete Fourier transform. If your data is a time-based signal (like electrical pulses or music), a Fourier transform essentially allows you to decompose a time series into various parts. Bear in mind that they are no longer seasonality and trend, but rather decompositions of different time and frequency domains.

This begs the question: what is the point of decomposing a time series?

A primary reason why we do any machine learning at all is to be able to predict values based on an input. When done on time series, this is called forecasting.

Think about this for a bit: if a time series is made up of multiple components, wouldn't it be better to be able to predict per component? If we are able to break a time...