Michael Bironneau - Machine Learning with Go Quick Start Guide

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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 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.

NiclasJern has been using computers for fun and profit since he got his first computer (a C64) at the age of four. After a prolonged period of combining the founding and running of a start-up, Walkbase, with his university studies, he graduated from bo Akademi University with an M.Sc. in computer engineering in 2015. His hobbies include long walks, lifting heavy metal objects at the gym, and spending quality time with his wife and daughter. He currently works at Stratacache, which acquired Walkbase in 2017, where he continues to lead the Walkbase engineering teams and design and build the future of retail technology.

Philipp Mieden is a German security researcher and software engineer, currently focusing on network security monitoring with applied machine learning. He presented his research on classifying malicious behavior in network traffic at several international contests and conferences and won multiple prizes. He holds a B.Sc. degree from the Ludwig Maximilian University of Munich, and shares many of his projects on GitHub. Besides network anomaly detection, Philipp is also interested in hardware security, industrial control systems, and reverse engineering malware.

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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.

This book is intended for developers and data scientists with at least a beginner-level knowledge of Go, and a vague idea of what types of problems ML aims to tackle. No advanced knowledge of Go, or a theoretical understanding of the mathematics that underpins ML, is required.

, 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.

, 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.