Build simple, maintainable, and easy to deploy machine learning applications.
About This Book
* Build simple, but powerful, machine learning applications that leverage Go's 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 models in Go applications.
Who This Book Is For
This book is for Go developers who are familiar with syntax and programming with Go and now want to enter the field of data science with Go. Those who want practical skills to be able to perform the most common data analysis techniques with Go will find this bookmvery helpful. Familiarity with statistics and math is necessary.
What You Will Learn
* Design philosophy for data analysis and machine learning.
*Explore data gathering, organization, parsing, and cleaning.
*Matrices and linear algebra , Statistics and probability.
*Learn evaluation and validation of models.
*Explore Regression, Classification, Clustering.
*Build model of times series and detect anomaly.
*Learn about deploying and distributing analyses and models.
*Understand relevant machine learning algorithms
In Detail
The mission of this book is to develop readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book will clearly introduce the technical, programming aspects of data analysis in Go, but it will also guide the reader to understand sound machine learning workflows and philosophies for real work analysis scenarios.
Data scientists and analysts are unfortunately known for producing bad, inefficient, and unmaintainable code. This book will address this issue, and will clearly show readers how to be productive with machine learning while also producing application maintaining a high level of integrity. It will also allow readers to overcome the common challenges of integrating analysis and machine learning code within an existing engineering organization.
The reader will take a logical journey to overcome these issues/challenges and create interesting, valuable Go applications. They will begin by exploring the essential philosophies and workflows that must be employed when writing machine learning applications. They will also build on those philosophies with a solid understanding of how to gather, organize, and parse real work data from a variety of sources. The readers will develop a solid statistical toolkit to be able to quick gain intuition about the content of a dataset both numerically and visually. The readers will gain hands on experience implementing essential machine learning techniques (regression, classification, clustering, etc.) with relevant Go packages from the community.
At the end of this journey, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and examples implementations.
