Machine Learning with Go Quick Start Guide: Hands-on techniques for building supervised and unsupervised machine learning workflows

Book Description

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.

What you will learn

• Understand the types of problem that machine learning solves, and the various approaches
• Import, pre-process, and explore data with Go to make it ready for machine learning algorithms
• Visualize data with gonum/plot and Gophernotes
• Diagnose common machine learning problems, such as overfitting and underfitting
• Implement supervised and unsupervised learning algorithms using Go libraries
• Build a simple web service around a model and use it to make predictions

Who this book is for

This book is for developers and data scientists with at least beginner-level knowledge of Go, and a vague idea of what types of problem Machine Learning aims to tackle. No advanced knowledge of Go (and no theoretical understanding of the math that underpins Machine Learning) is required.

Table of Contents

1. Introducing Machine Leaning with Go
2. Setting Up the Development Environment
3. Supervised Learning
4. Unsupervised Learning
5. Using Pretrained Models
6. Deploying Machine Learning Applications
7. Conclusion – Successful ML Projects