Machine-Learning-with-Go-

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Machine-Learning-with-Go-

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Machine Learning with Go by Packt Publishing

Machine-Learning-with-Go- 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Machine Learning with Go [Video]
This is the code repository for [Machine Learning with Go [Video]](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-go-video?utm_source=github&utm_medium=repository&utm_campaign=9781789134735), published by [Packt](https://www.packtpub.com/?utm_source=github). It contains all the supporting project files necessary to work through the video course from start to finish.
## About the Video Course
The mission of this course is to turn you into a productive, innovative data analyst who can leverage Go to build robust and valuable applications. To this end, the course clearly introduces the technical aspects of building predictive models in Go, but also helps you understand how machine learning workflows are applied in real-world scenarios.

This course shows you how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives you patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization.

You’ll begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Then you’ll develop a solid statistical toolkit that will allow you to quickly understand gain intuition about the content of a dataset. Finally, you’ll gain hands-on experience of implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages.

By the end, you’ll have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations

<H2>What You Will Learn</H2>
<DIV class=book-info-will-learn-text>
<UL>
<LI>Find out about data gathering, organization, parsing, and cleaning 
<LI>Explore matrices, linear algebra, statistics, and probability 
<LI>See how to evaluate and validate models 
<LI>Look at regression, classification, clustering 
<LI>Find out about neural networks and deep learning 
<LI>Utilize times series models and anomaly detection 
<LI>Get to grip with techniques for deploying and distributing analyses and models 
<LI>Optimize machine learning workflow techniques </LI></UL></DIV>

## Instructions and Navigation
### Assumed Knowledge
To fully benefit from the coverage included in this course, you will need:<br/>
Familiarity with some statistics and math topics is necessary.
### Technical Requirements
This course has the following software requirements:<br/>
Go 1.x

## Related Products
* [Data Storytelling with Power BI [Video]](https://www.packtpub.com/big-data-and-business-intelligence/data-storytelling-power-bi-video?utm_source=github&utm_medium=repository&utm_campaign=9781789959475)

* [Data Storytelling with Qlik Sense [Video]](https://www.packtpub.com/big-data-and-business-intelligence/data-storytelling-qlik-sense-video?utm_source=github&utm_medium=repository&utm_campaign=9781789959123)

* [Implementing Deep Learning Algorithms with TensorFlow 2.0 [Video]](https://www.packtpub.com/big-data-and-business-intelligence/implementing-deep-learning-algorithms-tensorflow-20-video?utm_source=github&utm_medium=repository&utm_campaign=9781789950496)

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