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)

Typical stages in a ML project

As we have seen throughout this book, ML is highly dependent on the data that is used for training and testing. For this reason, we find it helpful to view a typical project through the stages in the following diagram, which comes from the Cross Industry Standard Process for Data Mining (CRISP-DM), a popular method for managing data science projects[3]:

In contrast to some other engineering systems, ML normally never produces perfect output, so, for this reason, projects are often iterative. Refinements to the datasets and models allow you to produce progressively better results, provided they are justified by your business needs.

Business and data understanding

Having decided to use ML, a crucial...