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)

How to Solve All Machine Learning Problems

Welcome to the book Go Machine Learning Projects.

This is a rather odd book. It's not a book about how machine learning (ML) works. In fact, originally it was decided that we will assume that the readers are familiar with the machine learning (ML) algorithms I am to introduce in these chapters. Doing so would yield a rather empty book, I feared. If the reader knows the ML algorithm, what happens next is to simply apply the ML algorithm in the correct context of the problem! The ten or so chapters in this book would be completed in under 30 pages—anyone who's written a grant report for government agencies would have experience writing such things.

So what is this book going to be about?

It's going to be about applying ML algorithms within a specific, given context of the problem. These problems are concrete, and are specified by me on a whim. But in order to explore the avenues of the application of ML algorithms to problems, the reader must first be familiar with algorithms and the problems! So, this book has to strike a very delicate balance between understanding the problem, and understanding the specific algorithm used to solve the problem.

But before we go too far, what is a problem? And what do I mean when I say algorithm? And what's with this machine learning business?