What do you get with a Packt Subscription?

Free for first 7 days. $15.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

Subscribe now

View plans & pricing

Key benefits

Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond

Harness the power of R to build flexible, effective, and transparent machine learning models

Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz

Description

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.

What you will learn

Discover the origins of machine learning and how exactly a computer learns by example

Prepare your data for machine learning work with the R programming language

Classify important outcomes using nearest neighbor and Bayesian methods

Predict future events using decision trees, rules, and support vector machines

Forecast numeric data and estimate financial values using regression methods

Model complex processes with artificial neural networks — the basis of deep learning

Avoid bias in machine learning models

Evaluate your models and improve their performance

Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow

What do you get with a Packt Subscription?

Free for first 7 days. $15.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

Subscribe now

View plans & pricing

Frequently bought together

$59.99

$54.99

$43.99

Total $ 158.97

FAQs

What is included in a Packt subscription?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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.

Filter reviews by

All

Amazon verified reviews

floren25 Jun 28, 2020

Puede resultar chocante, como poco, ver que alguien califica de «entretenido» un libro más bien técnico como éste, pero resulta que el calificativo es bastante apropiado. Lo es porque Brett lantz ha escrito una obra muy bien estructurada y pedagógicamente escrita sobre un tema que a muchos puede resultar intimidatorio, pero que él se las ingenia para volver atractivo.El libro es una introducción amable y facilitada a catorce algoritmos de «Machine Learning» (expresión que suele traducirse al castellano como «aprendizaje automático»), una muestra representativa, aunque desde luego no exhaustiva, de los métodos de Machine Learning en circulación. Los catorce algoritmos cubren desde k-nearest neighbors hasta Random Forests, pasando por Decision trees y Association rules. Los distintos algoritmos (de aprendizaje supervisado, no supervisado y de meta-aprendizaje) cubren cuatro tipos de tareas: clasificar, predecir, detectar patrones y agrupar.Aunque ya tenía noticia de ello, me parece digno de ser resaltado que los algoritmos más potentes, como Artificial Neural Networks o Support Vector Machines, son lo que Lantz llama «algoritmos de caja negra»: sabemos que funcionan bien pero no sabemos por qué funcionan bien.Uno de los problemas frecuentes con este tipo de libros es que hay complementos que tienes que bajar de Internet y que no siempre se comportan como deberían. En este caso, ¡loado sea el cielo!, no es así: las bases de datos y el código escrito en el lenguaje R funcionan sin problemas. Sólo en los tres últimos capítulos me he llevado algún tropezón. Especialmente en el último, que es con diferencia el de contenido más avanzado: computación en la nube, big data y demás.Pero, a pesar de lo que acabo de decir, se aprende mucho con este texto, dado el esmero que pone el autor en explicar todos los detalles (como las líneas de código que pudieran resultar más intrigantes) y en no dejar a nadie atrás.En suma, una excelente iniciación a Machine Learning usando el software R, el más empleado por los estadísticos, aunque no el más común en aprendizaje automático.

Amazon Verified review

Ashish Tambadkar May 13, 2019

I am a complete beginner to R as well as Machine learning, this book has clearly stated every concept and made me understand machine learning even better. I highly recommend this book

Arthur P. Jun 09, 2019

Very understandably written and easy to follow!

Alberto May 22, 2022

Buen libro a blanco y negro

Mohamed Ali Hefnawy Aug 06, 2020

I like this book, as I already took many R courses and boot camps, but practicing doing projects in the book is really a very good experience I went through it.

Machine Learning with R: Expert techniques for predictive modeling , Third Edition

What do you get with a Packt Subscription?