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.
