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

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Probability

At this point, we now understand a couple of ways to represent/manipulate our data (matrices and vectors), and we know how to gain and understanding about our data, and how to quantify how our data looks (statistics). However, sometimes when we are developing machine learning applications, we also want to know how likely it is that a prediction is correct or how significant certain results are, given a history of results. Probability can help us answer these how likely and how significant questions.

Generally, probability has to do with the likelihood of events or observations. For example, if we are going to flip a coin to make a decision, how likely is it that we would see heads (50%), how likely is it that we would see tails (50%), or even how likely is it that the coin is a fair coin? This might seem like a trivial example, but many similar questions come up when...

Machine Learning With Go

Machine Learning With Go

Overview of this book

Related Content you might be interested in

Current Title:

Machine Learning With Go

Hands-On Deep Learning with Go

Machine Learning with Go Quick Start Guide

Practical Time Series Analysis