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
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Supervised Learning

As we learned in the first chapter, supervised learning is one of two major branches of machine learning. In a way, it is similar to how humans learn a new skill: someone else shows us what to do, and we are then able to learn by following their example. In the case of supervised learning algorithms, we usually need lots of examples, that is, lots of data providing the input to our algorithm and what the expected output should be. The algorithm will learn from this data, and then be able to predict the output based on new inputs that it has not seen before.

A surprising number of problems can be addressed using supervised learning. Many email systems use it to classify emails as either important or unimportant automatically whenever a new message arrives in the inbox. More complex examples include image recognition systems, which can identify what an image...

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