Go Machine Learning Projects, published by Packt
This is the code repository for Go-Machine-Learning-Projects, published by Packt.
Perform end-to-end data analysis to gain efficient data insight
Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This book will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured.
This book covers the following exciting features:
- Set up a machine learning environment with Go libraries
- Use Gonum to perform regression and classification
- Explore time series models and decompose trends with Go libraries
- Clean up your Twitter timeline by clustering tweets
- Learn to use external services for your machine learning needs
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
Word: she - true
Word: shan't - false
Word: be - false
Word: learning - true
Word: excessively. - true
Following is what you need for this book: Some coding experience in Golang and knowledge of basic machine learning concepts will aid you in understanding the concepts covered in this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-12).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | Go language | Windows |
| 2 | Go Land | Windows |
Xuanyi Chew Xuanyi Chew is the Chief Data Scientist of a Sydney-based logistics startup. He is the primary author of Gorgonia, an open source deep learning package for Go. He's been practicing machine learning for the past 12 years, applying them typically to help startups. His goal in life is to make an artificial general intelligence a reality. He enjoys learning new things.
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