Machine Learning and Go: A Practical Guide for Developers

The book "Machine Learning and Go (Golang)" is a practical guide for developers who want to apply machine learning methods without leaving the Go ecosystem. Unlike most ML books focused on Python, this edition focuses on using native Go code, minimal dependencies, and high performance.

What this book is about

You will journey from the basics of machine learning to building full-fledged models in Go. The book covers:

• Fundamentals of statistics and linear algebra necessary for understanding ML
• Implementation of classical algorithms: linear and logistic regression, decision trees, k-nearest neighbors
• Working with numerical data through packages gonum, gorgonia, and go-svm
• Building neural networks in pure Go without TensorFlow
• Data processing and preparation: cleaning, normalization, feature engineering
• Model evaluation: metrics, cross-validation, hyperparameter tuning
• Deploying ML models to production: REST API with net/http, gRPC, working with Docker
• Integration with databases and real-world Go projects

Who this book is for

This book will be useful for:

• Go developers who want to add ML to their stack
• Data Scientists looking to learn Go for production solutions
• Students studying machine learning and seeking an alternative to Python
• Engineers who need to deploy a lightweight ML model without heavy frameworks

What you will learn

• Why Go is an excellent choice for ML in production (speed, ease of deployment, static typing)
• How to implement machine learning algorithms from scratch in Go
• How to use libraries gonum (linear algebra), gorgonia (neural networks), and go-sklearn
• How to process large volumes of data using goroutines and channels
• How to build data pipelines for ML models
• How to test and debug ML code in Go
• How to integrate ready-made models into web services

Book features

• All code examples are available on GitHub and ready to run
• Practical exercises after each chapter
• Comparison with Python implementations for better understanding
• Tips for optimizing Go model performance

This book is a bridge between the world of machine learning and the Go ecosystem. You will learn to build models that are fast, easy to deploy, and do not require a Python interpreter. Perfect for those who want to use Go not only for backend but also for Data Science.

Reader requirements: basic knowledge of Go (syntax, goroutines, interfaces) and a foundational understanding of mathematics (linear algebra and statistics at a school level). Prior ML knowledge is not required — the book starts with the basics.