This is the code repository for Machine Learning With Go - Second Edition, published by Packt.
Leverage Go's powerful packages to build smart machine learning and predictive applications
This updated edition of the popular Machine Learning With Go shows you how to overcome the common challenges of integrating analysis and machine learning code within an existing engineering organization.
This book covers the following exciting features:
- Become well versed with data processing, parsing, and cleaning using Go packages
- Learn to gather data from various sources and in various real-world formats
- Perform regression, classification, and image processing with neural networks
- Evaluate and detect anomalies in a time series model
- Understand common deep learning architectures to learn how each model is built
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:
import pandas as pd
# Define column names.
cols = [
'integercolumn',
'stringcolumn'
]
Following is what you need for this book: This book is primarily for Go programmers who want to become a machine learning engineer and to build a solid machine learning mindset along with a good hold on Go packages. This is also useful for data analysts, data engineers, machine learning users who want to run their machine learning experiments using the Go ecosystem. Prior understanding of linear algebra is required to benefit from this book
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | go.1.12.4, Kubernetes, Minikube VM, Pachyderm | Windows 64-bit, Mac OS X, and Linux |
| 2 - 8 | go.1.12.4 | Windows 64-bit, Mac OS X, and Linux |
| 9 | go.1.12.4, TensorFlow | Windows, Mac OS X, and Linux (Any) |
| 10 | go.1.12.4 | Linux, 64-bit (x86), macOS X, Version 10.12.6 (Sierra) or higher |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Daniel Whitenack is a trained PhD data scientist with over 10 years' experience working on data-intensive applications in industry and academia. Recently, Daniel has focused his development efforts on open source projects related to running machine learning (ML) and artificial intelligence (AI) in cloud-native infrastructure (Kubernetes, for instance), maintaining reproducibility and provenance for complex data pipelines, and implementing ML/AI methods in new languages such as Go. Daniel co-hosts the Practical AI podcast, teaches data science/engineering at Ardan Labs and Purdue University, and has spoken at conferences around the world (including ODSC, PyCon, DataEngConf, QCon, GopherCon, Spark Summit, and Applied ML Days, among others).
Janani Selvaraj works as a senior research and analytics consultant for a start-up in Trichy, TamilNadu. She is a mathematics graduate with PhD in environmental management. Her current interests include data wrangling and visualization, machine learning, and geospatial modeling. She currently trains students in data science and works as a consultant on several data-driven projects in a variety of domains. She is an R programming expert and founder of the R-Ladies Trichy group, a group that promotes gender diversity. She has served as a reviewer for Go-Machine learning Projects book.
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