Book Image

Machine Learning with Go Quick Start Guide

By : Michael Bironneau, Toby Coleman
Book Image

Machine Learning with Go Quick Start Guide

By: Michael Bironneau, Toby Coleman

Overview of this book

Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go. The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced. The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum. The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring. At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.
Table of Contents (9 chapters)

Summary

In this book, you have learned about many important techniques that are required to develop ML applications in Go, and deploy them as production systems. The best way to develop your knowledge is with hands-on experience, so dive in and start adding ML software into your own Go applications. The skills you have learned here will allow you to start adding cutting-edge ML capabilities to the projects that you are working on.

ML is a rapidly evolving field with new algorithms and datasets being published every week, both by academics and technology companies. We recommend that you read the technical blogs, papers, and code repositories that cover this research, many of which we have referenced throughout this book. You might find a new state-of-the-art model that solves a problem you have been working on, waiting for you to implement it in Go.

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