Machine Learning with Go Quick Start Guide

Machine Learning with Go Quick Start Guide

Hands-on techniques for building supervised and unsupervised machine learning workflows


This eBook includes

Formats : PDF, EPUB, MOBI (Downlodable)

Pages : 168

ISBN : 9781838551650

Language : English

About the Book

Book description

This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering

Key Features

  • Your handy guide to building machine learning workflows in Go for real-world scenarios
  • Build predictive models using the popular supervised and unsupervised machine learning techniques
  • Learn all about deployment strategies and take your ML application from prototype to production ready

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.

Machine Learning with Go Quick Start Guide

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Author Details

Packt Publishing

Packt Publishing

Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.

Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.

As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.

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