Machine Learning With Go

Overview of this book

The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.

Preface

What this book covers

What you need for this book

Free Chapter

Gathering and Organizing Data

Handling data - Gopher style

Best practices for gathering and organizing data with Go

JSON

Caching

Summary

Matrices, Probability, and Statistics

Summary

Evaluation and Validation

Evaluation

Validation

References

Summary

Regression

Understanding regression model jargon

Linear regression

Multiple linear regression

Nonlinear and other types of regression

References

Summary

Classification

Understanding classification model jargon

Logistic regression

k-nearest neighbors

Decision trees and random forests

Naive bayes

References

Summary

Clustering

Understanding clustering model jargon

Measuring Distance or Similarity

Evaluating clustering techniques

k-means clustering

Other clustering techniques

References

Summary

Time Series and Anomaly Detection

Representing time series data in Go

Understanding time series jargon

Statistics related to time series

Auto-regressive models for forecasting

Auto-regressive moving averages and other time series models

Anomaly detection

References

Summary

Neural Networks and Deep Learning

Understanding neural net jargon

Building a simple neural network

Utilizing the simple neural network

Introducing deep learning

References

Summary

Deploying and Distributing Analyses and Models

Running models reliably on remote machines

Building a scalable and reproducible machine learning pipeline

References

Summary

Algorithms/Techniques Related to Machine Learning

Gradient descent

Entropy, information gain, and related methods

Backpropagation

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Utilizing the simple neural network

Now that we have some neural network training functionality that appears to be working, let's try to utilize this functionality in a more realistic modeling scenario. In particular, let's bring back our favorite classification dataset, the iris flower dataset (utilized in Chapter 5, Classification).

If you remember, when trying to classify iris flowers using this dataset, we are trying to classify them into one of three species (setosa, virginica, or versicolor). As our neural net is expecting matrices of float values, we need to encode the three species into numerical columns. One way to do this is to create a column in our dataset for each species. We will then set that column's values to either 1.0 or 0.0 depending on whether the corresponding row's measurements correspond to that species (1.0) or to another species (0...

Machine Learning With Go

Machine Learning With Go

Overview of this book

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