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

adnan baloch Mar 28, 2016

You don't have to be married to a physicist to appreciate the role of the team at CERN that confirmed the existence of the Higgs Boson. Who better to be a reviewer of this book than a member of that team? That fact itself should inspire confidence in the utility of this book. The author uses interesting analogies to explain the different aspects of predictive analytics and even goes so far as to present comparison tables, serving to drive home his points. The ease and power of the Python programming language is put to good use in explaining the process of data cleaning and wrangling. The better part of the first half of the book is dedicated to exploring the various aspects of these two critical processes with easy to follow examples and code. A whole chapter is devoted to laying out the statistical concepts that are integral to getting the most out of the remainder of the book. The latter part of the book details supervised and unsupervised predictive modelling algorithms, shows how to implement them in Python and furthermore, delves deep into the mathematics of these widely used algorithms so that readers become well equipped to tackle real world challenges of predictive analytics in ANY programming language of their choice. In my opinion, the author really succeeded in making the serious subject matter of this book sound cool and exciting.

Amazon Verified review

A. Zubarev Apr 18, 2016

In my view Learning Predictive Analytics with Python is one of the most successful publications on such a difficult to initially grasp subject as Machine Learning. Yes, despite the name of the book does not imply so, it is in fact a gentle submersion into the Machine Learning, a so highly praised Data Science topic. Luckily, learning it would be much easier with Learning Predictive Analytics with Python from such a talented author. It is the most exciting yet easy to follow, logical and at the same time entertaining material I ever read so far. Tasteful, relevant examples, based on free software and datasets anyone can obtain. And the book also has several gems, these are the coverage of the ID3 algorithm (based on my observation looks like totally omitted in the most modern literature, but undeservedly), building various regressions and testing your model. One small advice to the reader: get familiarized yourself with iPython, and perhaps read some theory on statistics, not really necessary, but if you are going to apply the newly acquired knowledge at work or study then it could be a great deal of steering you into the right direction.

Julian Cook Mar 13, 2016

If you are familiar with Packt (the publisher), you will know that they tend to carpet bomb particular areas, with multiple overlapping titles. This makes it difficult to recommend just one title if anyone asks you, since different books have different strengths.The strength of this book is that the author really does explain how to use PANDAS (python data analysis library) and statistical analysis from the ground up. Most pandas users will be familiar with pd.read_csv, but he covered a lot of options that I had never really understood properly, because I chiefly learnt from examples that don't really give you the 'why' of things.You might say, why not read the original book by Wes McKinney? I would have to say that this is a much more interesting read and has better flow. The Wes McKinney book sometimes reads like documentation and you are not sure what to really focus on.The coverage of statistical learning is also good, for instance he does a nice explanation of logistic regression and the underlying methodology with just enough math to properly explain the distinction between linear regression and logistic regression.I think the book is thorough enough that you could actually use it as a coursebook for statistical learning w/python, which a high praise for a book with a fairly generic title.

a reader Sep 26, 2020

This is a good book. I do not understand why there are bad reviews for it. I would like to thank the author for the good job! Well done! Unfortunately, the author deleted the datasets the book uses from the Google drive.

Jeremie Oct 04, 2017

Book deserves three to four stars max. It is ok and interesting. It is introduces a lot of concepts but shame it doesn't go a little bit more into details especially in the end of the book when talking about clustering and regression. It is one thing to talk about clustering but there is nothing about what to do with it once it is done.there isnt much discussion about regression tree and random forest algorithms which deserve more such as for example what can one do to improve the algos if thstbdont work well or what other algos are available.perhaps simply the book needs to advise on further reading

Learning Predictive Analytics with Python: Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python

What do you get with a Packt Subscription?