Machine Learning With Go
Daniel Whitenack
Sold by PBShop.store UK, Fairford, GLOS, United Kingdom
AbeBooks Seller since 11 June 1999
New - Soft cover
Condition: New
Quantity: Over 20 available
Add to basketSold by PBShop.store UK, Fairford, GLOS, United Kingdom
AbeBooks Seller since 11 June 1999
Condition: New
Quantity: Over 20 available
Add to basketNew Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Seller Inventory # L0-9781785882104
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.
Daniel Whitenack (@dwhitena), PhD, is a trained data scientist working with Pachyderm (@pachydermIO). Daniel develops innovative, distributed data pipelines that include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (GopherCon, JuliaCon, PyCon, ODSC, Spark Summit, and more), teaches data science/engineering at Purdue University (@LifeAtPurdue), and, with Ardan Labs (@ardanlabs), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.
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