Build simple, maintainable, and easy to deploy machine learning applications.
About This Book
- Build simple, but powerful, machine learning applications that leverage Go’s standard library along with popular Go packages.
- Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go
- Understand when and how to integrate certain types of machine learning model in Go applications.
Who This Book Is For
This book is for Go developers who are familiar with the Go syntax and can develop, build, and run basic Go programs. If you want to explore the field of machine learning and you love Go, then this book is for you! Machine Learning with Go will give readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics is necessary.
What You Will Learn
- Learn about data gathering, organization, parsing, and cleaning.
- Explore matrices, linear algebra, statistics, and probability.
- See how to evaluate and validate models.
- Look at regression, classification, clustering.
- Learn about neural networks and deep learning
- Utilize times series models and anomaly detection.
- Get to grip with techniques for deploying and distributing analyses and models.
- Optimize machine learning workflow techniques
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.
Style and approach
This book connects the fundamental, theoretical concepts behind Machine Learning to practical implementations using the Go programming language.
Table of contents
Gathering and Organizing Data
- Handling data - Gopher style
- Best practices for gathering and organizing data with Go
- CSV files
- SQL-like databases
- Data versioning
Matrices, Probability, and Statistics
- Matrices and vectors
- Evaluation and Validation
- Understanding regression model jargon
- Linear regression
- Multiple linear regression
- Nonlinear and other types of regression
- Understanding classification model jargon
- Logistic regression
- k-nearest neighbors
- Decision trees and random forests
- Naive bayes
- Understanding clustering model jargon
- Measuring Distance or Similarity
- Evaluating clustering techniques
- k-means clustering
- Other clustering techniques
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
Neural Networks and Deep Learning
- Understanding neural net jargon
- Building a simple neural network
- Utilizing the simple neural network
- Introducing deep learning
Deploying and Distributing Analyses and Models
- Running models reliably on remote machines
- Building a scalable and reproducible machine learning pipeline
- Algorithms/Techniques Related to Machine Learning
- Title: Machine Learning With Go
- Release date: September 2017
- Publisher(s): Packt Publishing
- ISBN: 9781785882104
You might also like
Hands-On High Performance with Go
Proven methodologies and concurrency techniques that will help you write faster and better code with Go …
40 Algorithms Every Programmer Should Know
Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental …
Go: Design Patterns for Real-World Projects
An insightful guide to learning the Go programming language About This Book Get insightful coverage of …
Go in Practice
Summary Go in Practice guides you through 70 real-world techniques in key areas like package management, …