Book Image

Machine Learning with Go Quick Start Guide

By : Michael Bironneau, Toby Coleman
Book Image

Machine Learning with Go Quick Start Guide

By: Michael Bironneau, Toby Coleman

Overview of this book

Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go. The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced. The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum. The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring. At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.

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Table of Contents (9 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introducing Machine Learning with Go
Introducing Machine Learning with Go
What is ML?
Types of ML algorithms
Why write ML applications in Go?
ML development life cycle
Summary
Further readings
2
Setting Up the Development Environment
Setting Up the Development Environment
Installing Go
Running Go interactively with gophernotes
Example – the most common phrases in positive and negative reviews
Example – exploring body mass index data with gonum/plot
Example – preprocessing data with Gota
Summary
Further readings
3
Supervised Learning
Supervised Learning
Classification
Regression
Summary
Further readings
4
Unsupervised Learning
Unsupervised Learning
Clustering
Principal component analysis
Summary
Further readings
5
Using Pretrained Models
Using Pretrained Models
How to restore a saved GoML model
Deciding when to adopt a polyglot approach
Example – invoking a Python model using os/exec
Example – invoking a Python model using HTTP
Example – deep learning using the TensorFlow API for Go
Summary
Further readings
6
Deploying Machine Learning Applications
Deploying Machine Learning Applications
The continuous delivery feedback loop
Deployment models for ML applications
Summary
Further readings
7
Conclusion - Successful ML Projects
Conclusion - Successful ML Projects
When to use ML
Typical stages in a ML project
When to combine ML with traditional code
Summary
Further readings
8
Other Books You May Enjoy
Other Books You May Enjoy
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Example – preprocessing data with Gota

The quality and speed of the ML algorithm training process depends on the quality of the input data. While many algorithms are robust to irrelevant columns and data that is not normalized, some are not. For example, many models requires data inputs to be normalized to lie between 0 and 1. In this section, we will look at some quick and easy ways to preprocess data with Gota. For these examples, we will be using a dataset containing 1,035 records of the height (inch) and weight (lbs) of major league baseball players[17]. The dataset, as described on the UCLA website, consists of the following features:

  • Name: Player name
  • Team: The baseball team that the player was a member of
  • Position: The player's position
  • Height (inches): Player height
  • Weight (pounds): Player weight in pounds
  • Age: Player age at the time of recording

For the...

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