Here's a little bit of gossip for you: The original project for this title had to do with detecting foreign influence on US elections in social media. At about the same time, I was also applying for a visa to the United States, to give a series of talks. It later transpired that I hadn't needed the visa after all; ESTA covered all the things I had wanted to do in the United States. But as I was preparing for the visa, an attorney gave me a very stern talking-to about writing a book on the politics of the United States. The general advice is this—if I don't want trouble with US Customs and Border Patrol, I should not write or say anything on social media about American politics, and especially not write a chapter of a book on it. So, I had to hastily rewrite this chapter. The majority of methods...
Go Machine Learning Projects
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Go Machine Learning Projects
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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)
Preface
Free Chapter
How to Solve All Machine Learning Problems
Linear Regression - House Price Prediction
Classification - Spam Email Detection
Decomposing CO2 Trends Using Time Series Analysis
Clean Up Your Personal Twitter Timeline by Clustering Tweets
Neural Networks - MNIST Handwriting Recognition
Convolutional Neural Networks - MNIST Handwriting Recognition
Basic Facial Detection
Hot Dog or Not Hot Dog - Using External Services
What's Next?
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