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Go Machine Learning Projects

Go Machine Learning Projects

By : Xuanyi Chew
5 (1)
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Go Machine Learning Projects

Go Machine Learning Projects

5 (1)
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)
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Classification - Spam Email Detection

What makes you you? I have dark hair, pale skin, and Asiatic features. I wear glasses. My facial structure is vaguely round, with extra subcutaneous fat in my cheeks compared to my peers. What I have done is describe the features of my face. Each of these features described can be thought of as a point within a probability continuum. What is the probability of having dark hair? Among my friends, dark hair is a very common feature, and so are glasses (a remarkable statistic is out of the 300 people or so I polled on my Facebook page, 281 of them require prescription glasses). The epicanthic folds of my eyes are probably less common, as is the extra subcutaneous fat in my cheeks.

Why am I bringing up my facial features in a chapter about spam classification? It's because the principles are the same. If I show you a photo of a human face...

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