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

We have covered a lot of ground in this chapter, and introduced many important machine learning concepts. The first step in tackling a supervised learning problem is to collect and preprocess the data, making sure that it is normalized, and split into training and validation sets. We covered a range of different algorithms for both classification and regression. In each example, there were two phases: training the algorithm, followed by inference; that is, using the trained model to make predictions from new input data. Whenever you try a new machine learning technique on your data, it is important to keep track of its performance against the training and validation datasets. This serves two main purposes: it helps you diagnose underfitting/overfitting and also provides an indication of how well your model is working.

It is usually best to choose the simplest model that...