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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Understanding clustering model jargon

Clustering is quite unique and comes with it's own set of terms, which are shown below. Keep in mind that the following list is only a partial list as there are many different types of clustering with corresponding jargon:

Clusters or groups: Each of these clusters or groups is a collection of data points into which our clustering technique organizes our data points.
Intra-group or intra-cluster: Clusters resulting from clustering can be evaluated using a measure of similarity between data points and other data points in the same resulting cluster. This is called intra-group or intra-cluster evaluation and similarity.
Inter-groupor inter-cluster: Clusters resulting from clustering can be evaluated using a measure of dissimilarity between data points and other data points in other resulting clusters. This is called inter-group or inter...

Machine Learning With Go

Machine Learning With Go

Overview of this book

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