Machine Learning with Go Quick Start Guide : Hands-On Techniques for Building Supervised and Unsupervised Machine Learning Workflows.

Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introducing Machine Learning with Go; What is ML?; Types of ML algorithms; Supervised learning problems; Unsupervised learning problems; Why write ML applications in Go?; The advantages of Go; Go's mature ecosystem; Transfer knowledge and models created in other languages; ML development life cycle; Defining problem and objectives; Acquiring and exploring data; Selecting the algorithm; Preparing data; Training; Validating/testing; Integrating and deploying; Re-validating; Summary

Further readingsChapter 2: Setting Up the Development Environment; Installing Go; Linux, macOS, and FreeBSD; Windows; Running Go interactively with gophernotes; Example -- the most common phrases in positive and negative reviews; Initializing the example directory and downloading the dataset; Loading the dataset files; Parsing contents into a Struct; Loading the data into a Gota dataframe; Finding the most common phrases; Example -- exploring body mass index data with gonum/plot; Installing gonum and gonum/plot; Loading the data; Understanding the distributions of the data series

Example -- preprocessing data with GotaLoading the data into Gota; Removing and renaming columns; Converting a column into a different type; Filtering out unwanted data; Normalizing the Height, Weight, and Age columns; Sampling to obtain training/validation subsets; Encoding data with categorical variables; Summary; Further readings; Chapter 3: Supervised Learning; Classification; A simple model -- the logistic classifier; Measuring performance; Precision and recall; ROC curves; Multi-class models; A non-linear model -- the support vector machine; Overfitting and underfitting; Deep learning

Neural networksA simple deep learning model architecture; Neural network training; Regression; Linear regression; Random forest regression; Other regression models; Summary; Further readings; Chapter 4: Unsupervised Learning; Clustering; Principal component analysis; Summary; Further readings; Chapter 5: 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; Installing TensorFlow

Import the pretrained TensorFlow modelCreating inputs to the TensorFlow model; Summary; Further readings; Chapter 6: Deploying Machine Learning Applications; The continuous delivery feedback loop; Developing; Testing; Deployment; Dependencies; Model persistence; Monitoring; Structured logging; Capturing metrics; Feedback; Deployment models for ML applications; Infrastructure-as-a-service; Amazon Web Services; Microsoft Azure; Google Cloud; Platform-as-a-Service; Amazon Web Services; Amazon Sagemaker; Amazon AI Services; Microsoft Azure; Azure ML Studio; Azure Cognitive Services; Google Cloud; AI Platform.

Machine learning has become an essential part of the modern data-driven world and has been extensively adopted in various fields across financial forecasting, effective searches, robotics, digital imaging in healthcare, and more. This book will teach you to perform various machine learning tasks using Go in different environments.

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