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Go Machine Learning Projects

By : Xuanyi Chew

5 (1)

Buy this Book

Go Machine Learning Projects

5 (1)

By: Xuanyi Chew

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

How to Solve All Machine Learning Problems

What is a problem?

What is an algorithm?

What is machine learning?

Do you need machine learning?

The general problem solving process

What is a model?

On writing and chapter organization

Why Go?

Quick start

Functions

Variables

Let's Go!

Linear Regression - House Price Prediction

The project

Exploratory data analysis

Linear regression

Discussion and further work

Summary

Classification - Spam Email Detection

The project

Exploratory data analysis

The classifier

Naive Bayes

Implementating the classifier

Putting it all together

Summary

Decomposing CO2 Trends Using Time Series Analysis

Exploratory data analysis

Decomposition

Forecasting

Summary

References

Clean Up Your Personal Twitter Timeline by Clustering Tweets

The project

K-means

DBSCAN

Data acquisition

Exploratory data analysis

Data massage

Clustering

Real data

The program

Tweaking the program

Summary

Neural Networks - MNIST Handwriting Recognition

A neural network

Linear algebra 101

Learning

The project

Training the neural network

Cross-validation

Summary

Convolutional Neural Networks - MNIST Handwriting Recognition

Everything you know about neurons is wrong

Neural networks – a redux

The project

CNNs

Describing a CNN

Running the neural network

Testing

Summary

Basic Facial Detection

What is a face?

PICO

GoCV

Pigo

Face detection program

Evaluating algorithms

Summary

Hot Dog or Not Hot Dog - Using External Services

MachineBox

What is MachineBox?

The project

The results

What does this all mean?

Why MachineBox?

Summary

What's Next?

What should the reader focus on?

The researcher, the practitioner, and their stakeholder

What did this book not cover?

Where can I learn more?

Thank you

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Discussion and further work

This model is now ready to be used to predict things. Is this the best model? No, it's not. Finding the best model is a never ending quest. To be sure, there are indefinite ways of improving this model. One can use LASSO methods to determine the importance of variables before using them.

The model is not only the linear regression, but also the data cleaning functions and ingestion functions that come with it. This leads to a very high number of tweakable parameters. Maybe if you didn't like the way I imputed data, you can always write your own method!

Furthermore the code in this chapter can be cleaned up further. Instead of returning so many values in the clean function, a new tuple type can be created to hold the Xs and Ys—a data frame of sorts. In fact, that's what we're going to build in the upcoming chapters. Several...