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)

The project

What we're going to do is to cluster tweets on Twitter. We will be using two different clustering techniques, K-means and DBSCAN. For this chapter, we're going to rely on some skills we built up in Chapter 2, Linear Regression – House Price Prediction. We will also be using the same libraries used in Chapter 2, Linear Regression – House Price Prediction. On top of that, we will also be using the clusters library by mpraski.

By the end of the project, we will be able to clean up any collection of tweets from Twitter, and cluster them into groups. The main body of code that fulfills the objective is very simple, it's only about 150 lines of code in total. The rest of the code is for fetching and preprocessing data.