This introduction to matrices, linear algebra, statistics, and probability in Go has given us a set of tools to understand, structure, and operate on data. This set of tools will be used throughout the book as we work on a diverse set of problems, and these tools could be used in a variety of contexts outside of machine learning. However, in the next chapter, we will discuss some ideas and techniques that will be extremely important in the machine learning context, specifically, evaluation and validation.
Machine Learning With Go
Machine Learning With Go
Overview of this book
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.
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Current Title:
Machine Learning With Go
Table of Contents (11 chapters)
Preface
Free Chapter
Gathering and Organizing Data
Matrices, Probability, and Statistics
Evaluation and Validation
Regression
Classification
Clustering
Time Series and Anomaly Detection
Neural Networks and Deep Learning
Deploying and Distributing Analyses and Models
Algorithms/Techniques Related to Machine Learning
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