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.
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Produktdetaljer
ISBN
9781838551650
Publisert
2019
Utgave
1. utgave
Utgiver
Vendor
Packt Publishing
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter