Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Machine Learning With Go
Machine Learning With Go

Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

Arrow left icon
Profile Icon Joseph Langstaff Whitenack
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (6 Ratings)
Paperback Sep 2017 304 pages 1st Edition
eBook
€8.99 €32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Joseph Langstaff Whitenack
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (6 Ratings)
Paperback Sep 2017 304 pages 1st Edition
eBook
€8.99 €32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.99 €32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m

What do you get with a Packt Subscription?

Free for first 7 days. €18.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing
Table of content icon View table of contents Preview book icon Preview Book

Machine Learning With Go

Matrices, Probability, and Statistics

Although we will take a mostly practical/applied approach to machine learning throughout this book, certain fundamental topics are essential to understand and properly apply machine learning. In particular, a fundamental understanding of probability and statistics will allow us to match certain algorithms with relevant problems, understand our data and results, and apply necessary transformations to our data. Matrices and a little linear algebra will then allow us to properly represent our data and implement optimizations, minimizations, and matrix-based transformations.

Do not worry too much if you are a little rusty in math or statistics. We will cover a few of the basics here and show you how to programmatically work with the relevant statistical measures and matrix techniques that will be utilized later in the book. That being said, this...

Matrices and vectors

If you spend much time learning and applying machine learning, you will see a bunch of references to matrices and vectors. In fact, many machine learning algorithms boil down to a series of iterative operations on matrices. What are matrices and vectors, and how do we represent them in our Go programs?

For the most part, we will utilize packages from github.com/gonum to form and work with matrices and vectors. This is a great series of Go packages focused on numerical computing, and they just keep getting better and better.

Vectors

A vector is an ordered collection of numbers arranged in either a row (left to right) or column (up and down). Each of the numbers in a vector is called a component. This might...

Statistics

At the end of the day, the success of your machine learning application is going to come down to the quality of your data, your understanding of the data, and your evaluation/validation of the results. All three of these things require us to have an understanding of statistics.

The field of statistics helps us to gain an understanding of our data, and to quantify what our data and results look like. It also provides us with mechanisms to measure how well our application is performing and prevent certain machine learning pitfalls (such as overfitting).

As with linear algebra, we aren't able to give a complete introduction to statistics here, but there are many resources online and in print to learn introductory statistics. Here we will focus on a fundamental understanding of the basics, along with the practicalities of implementation in Go. We will introduce the...

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...

References

Vectors and matrices:

Statistics:

Visualization:

Probability:

Summary

This introduction to matrices, linear algebra, statistics, and probability in Go has given us a set of tools to understand, structure, and operate on data. This set of tools will be used throughout the book as we work on a diverse set of problems, and these tools could be used in a variety of contexts outside of machine learning. However, in the next chapter, we will discuss some ideas and techniques that will be extremely important in the machine learning context, specifically, evaluation and validation.

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • 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.

Description

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.

Who is this book 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

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 26, 2017
Length: 304 pages
Edition : 1st
Language : English
ISBN-13 : 9781785882104
Vendor :
Google
Category :
Languages :

What do you get with a Packt Subscription?

Free for first 7 days. €18.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Product Details

Publication date : Sep 26, 2017
Length: 304 pages
Edition : 1st
Language : English
ISBN-13 : 9781785882104
Vendor :
Google
Category :
Languages :

Packt Subscriptions

See our plans and pricing
Modal Close icon
€18.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
€189.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
€264.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total 158.97
Go: Design Patterns for Real-World Projects
€74.99
Machine Learning With Go
€41.99
Go Systems Programming
€41.99
Total 158.97 Stars icon
Banner background image

Table of Contents

10 Chapters
Gathering and Organizing Data Chevron down icon Chevron up icon
Matrices, Probability, and Statistics Chevron down icon Chevron up icon
Evaluation and Validation Chevron down icon Chevron up icon
Regression Chevron down icon Chevron up icon
Classification Chevron down icon Chevron up icon
Clustering Chevron down icon Chevron up icon
Time Series and Anomaly Detection Chevron down icon Chevron up icon
Neural Networks and Deep Learning Chevron down icon Chevron up icon
Deploying and Distributing Analyses and Models Chevron down icon Chevron up icon
Algorithms/Techniques Related to Machine Learning Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(6 Ratings)
5 star 66.7%
4 star 16.7%
3 star 0%
2 star 0%
1 star 16.7%
Filter icon Filter
Top Reviews

Filter reviews by




Ali Zaid Anwar Oct 23, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I'm really happy to read a book on the subject of machine learning written for Go programmers, the book is enjoyable to read, and touches different areas. It's not a book to learn ML, and it's not a book to learn Go, but it's a book for Go programmers to see how to implement ML in Go. The book provide a introduction (or refresher) on the concepts of each chapter, so I don't find myself lost while reading the book, it provides just enough theory to explain the code that follows. Idiotic Go is easy and clear to read, the author add to this the way he structured the sample code, and provided proper comment to explain each part of it.Another cool feature about the book is that the readers can start from any chapter depending on their interest and experience on the subject, wherever I start I find an introduction, explanation of the jargons, the theory, how to do it in Go, which libraries to use, what would be the best practice and links and reference for further studies and readings.I'm really enjoying the book and it's clarity, and I hope the author will continue producing books on the subject of data science and Go with this quality.
Amazon Verified review Amazon
Amazon Customer Sep 24, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Bought as gift .Good buy
Amazon Verified review Amazon
David Brown Jan 19, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very easy to follow the information in this book.
Amazon Verified review Amazon
kniren Oct 24, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This books introduces many of the most common tools in the life of a data scientist and how to make use of them from the Go programming language. The writing is very clear and there is a large variety of examples on each of the discussed topics.It is amazing to see the Go data science scene flourishing, and books like this might teach a new generation of programmers the rudiments of machine learning, statistical modelling and exploratory data analysis with a language that they already feel confortable.
Amazon Verified review Amazon
R. S. Doiel Dec 12, 2017
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Well written and to the point. A practical approach to apply ML techniques in Go.
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is included in a Packt subscription? Chevron down icon Chevron up icon

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription? Chevron down icon Chevron up icon

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits? Chevron down icon Chevron up icon

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled? Chevron down icon Chevron up icon

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title? Chevron down icon Chevron up icon

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team. 

Can I download the code files for Early Access titles? Chevron down icon Chevron up icon

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date? Chevron down icon Chevron up icon

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready? Chevron down icon Chevron up icon

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access? Chevron down icon Chevron up icon

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered? Chevron down icon Chevron up icon

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content? Chevron down icon Chevron up icon

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access? Chevron down icon Chevron up icon

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.