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)

Decomposition

There are two things to note about the previous screenshot:

  • CO2 levels in the air are steadily rising over time.
  • There are dips and then bumps in the levels of CO2, but the result still ends up rising overall. These dips and bumps happen on a regular pattern.

The first point is what is known to statisticians as a trend. You may already be familiar with the notion of a Trend Line from Microsoft Excel. A trend is a kind of pattern that describes gradual change over time. In our case, it is quite clear that the trend is upward.

The second point is called seasonality—for very apt reasons, as it may turn out. Seasonality describes the pattern of variance that happens regularly. If you carefully look at the chart, typically at around August to October of each year, the CO2 levels drop to the lowest point of the year. After which, they rise steadily again until...