The subtitle of this book is "Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language". Author Daniel Whitenack 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.

<ASIN:1785882104>

The book shows how to gather, organize, and parse real-work data from a variety of sources. You're then guided through developing a solid statistical toolkit for gaining information about the content of a dataset. The book also covers implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages.

Author: Daniel Whitenack
Publisher: Packt Publishing
Date: Sept 2017
Pages: 304
ISBN: 978-1785882104
Print: 1785882104
Kindle: B01LPRN11G
Audience: Developers wanting to learn ML in Go
Level: Intermediate

• 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

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