Book Image

Go Machine Learning Projects

By : Xuanyi Chew
Book Image

Go Machine Learning Projects

By: Xuanyi Chew

Overview of this book

Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This book will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured. The book begins by guiding you through setting up your machine learning environment with Go libraries and capabilities. You will then plunge into regression analysis of a real-life house pricing dataset and build a classification model in Go to classify emails as spam or ham. Using Gonum, Gorgonia, and STL, you will explore time series analysis along with decomposition and clean up your personal Twitter timeline by clustering tweets. In addition to this, you will learn how to recognize handwriting using neural networks and convolutional neural networks. Lastly, you'll learn how to choose the most appropriate machine learning algorithms to use for your projects with the help of a facial detection project. By the end of this book, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound understanding of the practical implementations of machine learning algorithms in real-world projects.
Table of Contents (12 chapters)

Tweaking the program

If you have been following up to this point, you may get very poor results from all the clustering algorithms. I'd like to remind you that the stated objective of this book in general is to impart an understanding of what it's like to do data science in Go. For the most part, I have advocated a method that can be described as think hard about the problem, then write the answers down. But the reality is that often trial and error are required.

The solution that works for me on my Twitter home timeline may not work for you. For example, this code works well on a friend's Twitter feed. Why is this? He follows a lot of similar people who talk about similar things at the same time. It's a little harder to cluster tweets in my Twitter home feed. I follow a diverse array of people. The people I follow don't have set schedules of tweeting...