Build simple, maintainable, and easy to deploy machine learning applications. About This Book * Build simple, but powerful, machine learning applications that leverage Gos standard library along with popular Go packages. * Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go * Understand when and how to integrate certain types of machine learning model in Go applications. Who This Book Is For This book is for Go developers who are familiar with the Go syntax and can develop, build, and run basic Go programs. If you want to explore the field of machine learning and you love Go, then this book is for you! Machine Learning with Go will give readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics is necessary. What You Will Learn * Learn about data gathering, organization, parsing, and cleaning. * Explore matrices, linear algebra, statistics, and probability. * See how to evaluate and validate models. * Look at regression, classification, clustering. * Learn about neural networks and deep learning * Utilize times series models and anomaly detection. * Get to grip with techniques for deploying and distributing analyses and models. * Optimize machine learning workflow techniques In Detail The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations. Style and approach This book connects the fundamental, theoretical concepts behind Machine Learning to practical implementations using the Go programming language. About the AuthorRahul started working on Go as a hobbyist when the language was released by Google. His main experience with the language is to implement large-scale algorithms in Go for prototyping and quick solutions. Specifically, he has used Go extensively for massive scale classification problems, implementing the stochastic gradient descent algorithm, his own deep learning library, and for graph mining. He hasnt extensively written blogs or talked about Go because he usually conducted talks on more theoretical stuf, and his usual language of implementation is Python or Java. You can find him at https://in.linkedin.com/in/arahul , https://research.yahoo.com/ & http://www.arahul.in/ Vipul has been working with Go for the past few years. He started learning it out of curiosity, got hooked to the language, and since then, he has been using it as part of his professional work. As Go provides C-like runtime efficiency and produces well-designed code such as Python, he has used Go to build large-scale machine learning algorithms. He brings his vast experience both in his academic and professional work in the field of machine learning. He has authored several papers and some of his work is patent pending. Throughout his career, he has worked in many different fields such as market mix optimization, efficient budget allocation, computational advertising, large-scale RTB systems, campaign optimization, optimal bidding strategies, app classification, user inference generation, lookalike modeling, NLP, topic modeling, text classification, demographics prediction, and more. You can find him on LinkedIn at https://in.linkedin.com/in/vipulagrawal.