FAQs

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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

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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.

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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.

Leonardo Damasceno Dec 11, 2017

Did not like. Too superficial. Treat each topic as 'cake recipe'.

Amazon Verified review

Dimitri Shvorob Dec 07, 2017

Looking at the five-star reviews, I notice that "John DCousta" has only reviewed, and given five-star reviews, to Ganguly's two (Packt) books, and "Alessandro Breschi" - whose profile initially had name "Sunith Shetty" - has similarly only reviewed, and given five-star reviews, to three Packt books, one of them plagiarized. In all likelihood, both reviews are fake. Another thing you should know is that Ganguly's other book, "Learning Generative Adversarial Networks", is plagiarized. Even if this one isn't - which I think is unlikely - you should not support a plagiarist by buying his books.

John DCousta Oct 16, 2017

This book is for data analyst and aspiring data science professionals who are familiar with basics of R and want to expand their skill set in data analysis activities (without diving too much into mathematics/statistical jargon)- data cleaning & munging, eda, machine learning such as- regression, classification, advance clustering, deep learning (image recognition), handling large dataset in R with Spark.

Amazon Customer Oct 02, 2017

The R Data Analysis Cookbook 2nd Edition is primarily focused on real life data analysis and data science activities performed by data analyst/data scientist using R and offers succinct examples on a variety of data analysis topics such as data cleaning & munging, exploratory analysis, vectorized operations, regression, classification, advance clustering, deep learning (image recognition), geospatial analysis, social network analysis, handling large dataset in R with Spark and MongoDB. I enjoyed the section dealing with classification, image recognition and R with distrbuted system. This book does not provide introduction to R language (as it assume the readers to have basic knowledge in R as prerequisite). Although the book provide brief explanation of the machine learning algorithms used in the recipes, with equation, how it works along with its pros/cons, but it doesn't explain in details or great depth about each of the machine learning algorthim. For such information, you will have to look elsewhere such as "Beginning R Programming" and "Machine Learning: An Algorithmic Perspective". Overall it a very good book and hits the road running, if you just have basic knowledge of R programming.

R Data Analysis Cookbook, Second Edition: Customizable R Recipes for data mining, data visualization and time series analysis , Second Edition

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