Machine Learning with Go Quick Start Guide

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.

Type
ebook
Category
Data
publication date
2019-05-31
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

no of pages
168
duration
336
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
approach
This is a fast-paced guide that will help the readers get up and running with machine learning in Go without going into too much depth
audience
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.
meta 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
short description
Machine learning has become an essential part of the modern data-driven world and has been extensively adopted in various fields across financial forecasting, effective searches, robotics, digital imaging in healthcare, and more. This book will teach you to perform various machine learning tasks using Go in different environments.
subtitle
Hands-on techniques for building supervised and unsupervised machine learning workflows
keywords
Machine Learning, Go ML, supervised learning, unsupervised learning, Gonum
Product ISBN
9781838550356