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)

Data acquisition

In the earlier exercises, I asked you to look at the dots and figure out the distance. This gives a hint as to how we need to think of our data. We need to think of our data as coordinates in some imaginary coordinate space. Now, our data won't be just two-dimensional, because it's textual. Instead, it'll be multidimensional. This gives us hints as to how our data will look—slices of numbers representing a coordinate in some arbitrarily large N-dimensional space.

But, first, we'll need to get the data.

To acquire the tweets from the feed, we'll be using Aditya Mukherjee's excellent Anaconda library. To install it, simply run go get -u github.com/ChimeraCoder/Anaconda.

Of course, one can't just grab data from Twitter willy-nilly. We will need to acquire data via the Twitter API. The documentation of Twitter's API is...