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Matrices, Probability, and Statistics

Although we will take a mostly practical/applied approach to machine learning throughout this book, certain fundamental topics are essential to understand and properly apply machine learning. In particular, a fundamental understanding of probability and statistics will allow us to match certain algorithms with relevant problems, understand our data and results, and apply necessary transformations to our data. Matrices and a little linear algebra will then allow us to properly represent our data and implement optimizations, minimizations, and matrix-based transformations.

Do not worry too much if you are a little rusty in math or statistics. We will cover a few of the basics here and show you how to programmatically work with the relevant statistical measures and matrix techniques that will be utilized later in the book. That being said, this...

Key benefits

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 model in Go applications.

Description

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.

Who is this book 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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Ali Zaid Anwar Oct 23, 2017

I'm really happy to read a book on the subject of machine learning written for Go programmers, the book is enjoyable to read, and touches different areas. It's not a book to learn ML, and it's not a book to learn Go, but it's a book for Go programmers to see how to implement ML in Go. The book provide a introduction (or refresher) on the concepts of each chapter, so I don't find myself lost while reading the book, it provides just enough theory to explain the code that follows. Idiotic Go is easy and clear to read, the author add to this the way he structured the sample code, and provided proper comment to explain each part of it.Another cool feature about the book is that the readers can start from any chapter depending on their interest and experience on the subject, wherever I start I find an introduction, explanation of the jargons, the theory, how to do it in Go, which libraries to use, what would be the best practice and links and reference for further studies and readings.I'm really enjoying the book and it's clarity, and I hope the author will continue producing books on the subject of data science and Go with this quality.

Amazon Verified review

Amazon Customer Sep 24, 2018

Bought as gift .Good buy

David Brown Jan 19, 2018

Very easy to follow the information in this book.

kniren Oct 24, 2017

This books introduces many of the most common tools in the life of a data scientist and how to make use of them from the Go programming language. The writing is very clear and there is a large variety of examples on each of the discussed topics.It is amazing to see the Go data science scene flourishing, and books like this might teach a new generation of programmers the rudiments of machine learning, statistical modelling and exploratory data analysis with a language that they already feel confortable.

R. S. Doiel Dec 12, 2017

Well written and to the point. A practical approach to apply ML techniques in Go.

Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

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Machine Learning With Go

Matrices, Probability, and Statistics

Matrices and vectors

Vectors

Statistics

Probability

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Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

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