Build simple, maintainable, and easy to deploy machine learning
applications. About This Book * Build simple, but powerful, machine
learning applications that leverage Go's standard library along
with popular Go packages. * Learn the statistics, algorithms, and
techniques needed to successfully implement machine learning in Go
* Understand when and how to integrate certain types of machine
learning model in Go applications. Who This Book Is For This book
is for Go developers who are familiar with the Go syntax and can
develop, build, and run basic Go programs. If you want to explore
the field of machine learning and you love Go, then this book is
for you! Machine Learning with Go will give readers the practical
skills to perform the most common machine learning tasks with Go.
Familiarity with some statistics and math topics is necessary. What
You Will Learn * Learn about data gathering, organization, parsing,
and cleaning. * Explore matrices, linear algebra, statistics, and
probability. * See how to evaluate and validate models. * Look at
regression, classification, clustering. * Learn about neural
networks and deep learning * Utilize times series models and
anomaly detection. * Get to grip with techniques for deploying and
distributing analyses and models. * Optimize machine learning
workflow techniques In Detail The mission of this book is to turn
readers into productive, innovative data analysts who leverage Go
to build robust and valuable applications. To this end, the book
clearly introduces the technical aspects of building predictive
models in Go, but it also helps the reader understand how machine
learning workflows are being applied in real-world scenarios.
Machine Learning with Go shows readers how to be productive in
machine learning while also producing applications that maintain a
high level of integrity. It also gives readers patterns to overcome
challenges that are often encountered when trying to integrate
machine learning in an engineering organization. The readers will
begin by gaining a solid understanding of how to gather, organize,
and parse real-work data from a variety of sources. Readers will
then develop a solid statistical toolkit that will allow them to
quickly understand gain intuition about the content of a dataset.
Finally, the readers will gain hands-on experience implementing
essential machine learning techniques (regression, classification,
clustering, and so on) with the relevant Go packages. Finally, the
reader will have a solid machine learning mindset and a powerful Go
toolkit of techniques, packages, and example implementations. Style
and approach This book connects the fundamental, theoretical
concepts behind Machine Learning to practical implementations using
the Go programming language.
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