Machine Learning with Go Quick Start Guide
Hands-on techniques for building supervised and unsupervised machine learning workflows
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- 2019.05.31. 전자책 출간
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<Machine Learning with Go Quick Start Guide> ▶Book Description
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.
▶What You Will Learn
- Understand the types of problem that machine learning solves, and the various approaches
- Import, pre-process, and explore data with Go to make it ready for machine learning algorithms
- Visualize data with gonum/plot and Gophernotes
- Diagnose common machine learning problems, such as overfitting and underfitting
- Implement supervised and unsupervised learning algorithms using Go libraries
- Build a simple web service around a model and use it to make predictions
- Your handy guide to building machine learning workflows in Go for real-world scenarios
- Build predictive models using the popular supervised and unsupervised machine learning techniques
- Learn all about deployment strategies and take your ML application from prototype to production ready
▶Who This Book Is For
This book is for developers and data scientists with at least beginner-level knowledge of Go, and a vague idea of what types of problem Machine Learning aims to tackle. No advanced knowledge of Go (and no theoretical understanding of the math that underpins Machine Learning) is required.
▶What this book covers
- Chapter 1, Introducing Machine Learning with Go, introduces ML and the different types of ML-related problems. We will also look into the ML development life cycle, and the process of creating and taking an ML application to production.
- Chapter 2, Setting Up the Development Environment, explains how to set up an environment for ML applications and Go. We will also gain an understanding of how to install an interactive environment, Jupyter, to accelerate data exploration and visualization using libraries such as Gota and gonum/plot.
- Chapter 3, Supervised Learning, introduces supervised learning algorithms and demonstrates how to choose an ML algorithm, train it, and validate its predictive power on previously unseen data.
- Chapter 4, Unsupervised Learning, reuses many of the techniques related to data loading and preparation that we have implemented in this book, but will focuses instead on unsupervised machine learning.
- Chapter 5, Using Pretrained Models, describes how to load a pretrained Go ML model and use it to generate a prediction. We will also gain an understanding of how to use HTTP to invoke ML models written in other languages, where they may reside on a different machine or even on the internet.
- Chapter 6, Deploying Machine Learning Applications, covers the final stage of the ML development life cycle: taking an ML application written in Go to production.
- Chapter 7, Conclusion – Successful ML Projects, takes a step back and examines ML development from a project management point of view.
Machine learning (ML) plays a vital part in the modern data-driven world, and has been extensively adopted in various fields across financial forecasting, effective searching, robotics, digital imaging in healthcare, and many more besides. It is a rapidly evolving field, with new algorithms and datasets being published every week, both by academics and technology companies. This book will teach you how to perform various machine learning tasks using Go in different environments.
You will learn about many important techniques that are required to develop ML applications in Go, and deploy them as production systems. The best way to develop your knowledge is with hands-on experience, so dive in and start adding ML software to your own Go applications.
▶About the Author
- Michael Bironneau
Michael Bironneau is an award-winning mathematician and experienced software engineer. He holds a PhD in mathematics from Loughborough University and has worked in several data science and software development roles. He is currently technical director of the energy AI technology company, Open Energi.
- Toby Coleman
Toby Coleman is an experienced data science and machine learning practitioner. Following degrees from Cambridge University and Imperial College London, he has worked on the application of data science techniques in the banking and energy sectors. Recently, he held the position of innovation director at cleantech SME Open Energi, and currently provides machine learning consultancy to start-up businesses.
▶TABLE of CONTENTS
1. Introducing Machine Learning with Go
2. Setting Up the Development Environment
3. Supervised Learning
4. Unsupervised Learning
5. Using Pretrained Models
6. Deploying Machine Learning Applications
7. Conclusion - Successful ML Projects
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