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The Kaggle Book
The Kaggle Book

The Kaggle Book: Data analysis and machine learning for competitive data science

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Profile Icon Konrad Banachewicz Profile Icon Luca Massaron
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Arrow left icon
Profile Icon Konrad Banachewicz Profile Icon Luca Massaron
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Paperback Apr 2022 534 pages 1st Edition
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The Kaggle Book

Introducing Kaggle and Other Data Science Competitions

Data science competitions have long been around and they have experienced growing success over time, starting from a niche community of passionate competitors, drawing more and more attention, and reaching a much larger audience of millions of data scientists. As longtime competitors on the most popular data science competition platform, Kaggle, we have witnessed and directly experienced all these changes through the years.

At the moment, if you look for information about Kaggle and other competition platforms, you can easily find a large number of meetups, discussion panels, podcasts, interviews, and even online courses explaining how to win in such competitions (usually telling you to use a variable mixture of grit, computational resources, and time invested). However, apart from the book that you are reading now, you won’t find any structured guides about how to navigate so many data science competitions and how to get the most out of them – not just in terms of score or ranking, but also professional experience.

In this book, instead of just packaging up a few hints about how to win or score highly on Kaggle and other data science competitions, our intention is to present you with a guide on how to compete better on Kaggle and get back the maximum possible from your competition experiences, particularly from the perspective of your professional life. Also accompanying the contents of the book are interviews with Kaggle Masters and Grandmasters. We hope they will offer you some different perspectives and insights on specific aspects of competing on Kaggle, and inspire the way you will test yourself and learn doing competitive data science.

By the end of this book, you’ll have absorbed the knowledge we drew directly from our own experiences, resources, and learnings from competitions, and everything you need to pave a way for yourself to learn and grow, competition after competition.

As a starting point, in this chapter, we will explore how competitive programming evolved into data science competitions, why the Kaggle platform is the most popular site for such competitions, and how it works.

We will cover the following topics:

  • The rise of data science competition platforms
  • The Common Task Framework paradigm
  • The Kaggle platform and some other alternatives
  • How a Kaggle competition works: stages, competition types, submission and leaderboard dynamics, computational resources, networking, and more

The rise of data science competition platforms

Competitive programming has a long history, starting in the 1970s with the first iterations of the ICPC, the International Collegiate Programming Contest. In the original ICPC, small teams from universities and companies participated in a competition that required solving a series of problems using a computer program (at the beginning, participants coded in FORTRAN). In order to achieve a good final rank, teams had to display good skills in team working, problem solving, and programming.

The experience of participating in the heat of such a competition and the opportunity to stand in a spotlight for recruiting companies provided the students with ample motivation and it made the competition popular for many years. Among ICPC finalists, a few have become renowned: there is Adam D’Angelo, the former CTO of Facebook and founder of Quora, Nikolai Durov, the co-founder of Telegram Messenger, and Matei Zaharia, the creator of Apache Spark. Together with many other professionals, they all share the same experience: having taken part in an ICPC.

After the ICPC, programming competitions flourished, especially after 2000 when remote participation became more feasible, allowing international competitions to run more easily and at a lower cost. The format is similar for most of these competitions: there is a series of problems and you have to code a solution to solve them. The winners are given a prize, but also make themselves known to recruiting companies or simply become famous.

Typically, problems in competitive programming range from combinatorics and number theory to graph theory, algorithmic game theory, computational geometry, string analysis, and data structures. Recently, problems relating to artificial intelligence have successfully emerged, in particular after the launch of the KDD Cup, a contest in knowledge discovery and data mining, held by the Association for Computing Machinery’s (ACM’s) Special Interest Group (SIG) during its annual conference (https://kdd.org/conferences).

The first KDD Cup, held in 1997, involved a problem about direct marketing for lift curve optimization and it started a long series of competitions that continues today. You can find the archives containing datasets, instructions, and winners at https://www.kdd.org/kdd-cup. Here is the latest available at the time of writing: https://ogb.stanford.edu/kddcup2021/. KDD Cups proved quite effective in establishing best practices, with many published papers describing solutions, techniques, and competition dataset sharing, which have been useful for many practitioners for experimentation, education, and benchmarking.

The successful examples of both competitive programming events and the KDD Cup inspired companies (such as Netflix) and entrepreneurs (such as Anthony Goldbloom, the founder of Kaggle) to create the first data science competition platforms, where companies can host data science challenges that are hard to solve and might benefit from crowdsourcing. In fact, given that there is no golden approach that works for all the problems in data science, many problems require a time-consuming approach that can be summed up as try all that you can try.

In fact, in the long run, no algorithm can beat all the others on all problems, as stated by the No Free Lunch theorem by David Wolpert and William Macready. The theorem tells you that each machine learning algorithm performs if and only if its hypothesis space comprises the solution. Consequently, as you cannot know beforehand if a machine learning algorithm can best tackle your problem, you have to try it, testing it directly on your problem before being assured that you are doing the right thing. There are no theoretical shortcuts or other holy grails of machine learning – only empirical experimentation can tell you what works.

For more details, you can look up the No Free Lunch theorem for a theoretical explanation of this practical truth. Here is a complete article from Analytics India Magazine on the topic: https://analyticsindiamag.com/what-are-the-no-free-lunch-theorems-in-data-science/.

Crowdsourcing proves ideal in such conditions where you need to test algorithms and data transformations extensively to find the best possible combinations, but you lack the manpower and computer power for it. That’s why, for instance, governments and companies resort to competitions in order to advance in certain fields:

  • On the government side, we can quote DARPA and its many competitions surrounding self-driving cars, robotic operations, machine translation, speaker identification, fingerprint recognition, information retrieval, OCR, automatic target recognition, and many others.
  • On the business side, we can quote a company such as Netflix, which entrusted the outcome of a competition to improve its algorithm for predicting user movie selection.

The Netflix competition was based on the idea of improving existing collaborative filtering. The purpose of this was simply to predict the potential rating a user would give a film, solely based on the ratings that they gave other films, without knowing specifically who the user was or what the films were. Since no user description or movie title or description were available (all being replaced with identity codes), the competition required entrants to develop smart ways to use the past ratings available. The grand prize of US $1,000,000 was to be awarded only if the solution could improve the existing Netflix algorithm, Cinematch, above a certain threshold.

The competition ran from 2006 to 2009 and saw victory for a team made up of the fusion of many previous competition teams: a team from Commendo Research & Consulting GmbH, Andreas Töscher and Michael Jahrer, quite renowned also in Kaggle competitions; two researchers from AT&T Labs; and two others from Yahoo!. In the end, winning the competition required so much computational power and the ensembling of different solutions that teams were forced to merge in order to keep pace. This situation was also reflected in the actual usage of the solution by Netflix, who preferred not to implement it, but simply took the most interesting insight from it in order to improve its existing Cinematch algorithm. You can read more about it in this Wired article: https://www.wired.com/2012/04/netflix-prize-costs/.

At the end of the Netflix competition, what mattered was not the solution per se, which was quickly superseded by the change in business focus of Netflix from DVDs to online movies. The real benefit for both the participants, who gained a huge reputation in collaborative filtering, and the company, who could transfer its improved recommendation knowledge to its new business, were the insights that were gained from the competition.

The Kaggle competition platform

Companies other than Netflix have also benefitted from data science competitions. The list is long, but we can quote a few examples where the company running the competition reported a clear benefit from it. For instance:

  • The insurance company Allstate was able to improve its actuarial models built by their own experts, thanks to a competition involving hundreds of data scientists (https://www.kaggle.com/c/ClaimPredictionChallenge)
  • As another well-documented example, General Electric was able to improve by 40% on the industry-standard performance (measured by the root mean squared error metric) for predicting arrival times of airline flights, thanks to a similar competition (https://www.kaggle.com/c/flight)

The Kaggle competition platform has to this day held hundreds of competitions, and these two are just a couple of examples of companies that used them successfully. Let’s take a step back from specific competitions for a moment and talk about the Kaggle company, which is the common thread through this book.

A history of Kaggle

Kaggle took its first steps in February 2010, thanks to Anthony Goldbloom, an Australian trained economist with a degree in Economics and Econometrics. After working at Australia’s Department of the Treasury and the Research department at the Reserve Bank of Australia, Goldbloom interned in London at The Economist, the international weekly newspaper on current affairs, international business, politics, and technology. At The Economist, he had occasion to write an article about big data, which inspired his idea to build a competition platform that could crowdsource the best analytical experts to solve interesting machine learning problems (https://www.smh.com.au/technology/from-bondi-to-the-big-bucks-the-28yearold-whos-making-data-science-a-sport-20111104-1myq1.html). Since the crowdsourcing dynamics played a relevant part in the business idea for this platform, he derived the name Kaggle, which recalls by rhyme the term gaggle, a flock of geese, the goose also being the symbol of the platform.

After moving to Silicon Valley in the USA, his Kaggle start-up received $11.25 million in Series A funding from a round led by Khosla Ventures and Index Ventures, two renowned venture capital firms. The first competitions were rolled out, the community grew, and some of the initial competitors came to be quite prominent, such as Jeremy Howard, the Australian data scientist and entrepreneur, who, after winning a couple of competitions on Kaggle, became the President and Chief Scientist of the company.

Jeremy Howard left his position as President in December 2013 and established a new start-up, fast.ai (www.fast.ai), offering machine learning courses and a deep learning library for coders.

At the time, there were some other prominent Kagglers (the name indicating frequent participants of competitions held by Kaggle) such as Jeremy Achin and Thomas de Godoy. After reaching the top 20 global rankings on the platform, they promptly decided to retire and to found their own company, DataRobot. Soon after, they started hiring their employees from among the best participants in the Kaggle competitions in order to instill the best machine learning knowledge and practices into the software they were developing. Today, DataRobot is one of the leading companies in developing AutoML solutions (software for automatic machine learning).

The Kaggle competitions claimed more and more attention from a growing audience. Even Geoffrey Hinton, the “godfather” of deep learning, participated in (and won) a Kaggle competition hosted by Merck in 2012 (https://www.kaggle.com/c/MerckActivity/overview/winners). Kaggle was also the platform where François Chollet launched his deep learning package Keras during the Otto Group Product Classification Challenge (https://www.kaggle.com/c/otto-group-product-classification-challenge/discussion/13632) and Tianqi Chen launched XGBoost, a speedier and more accurate version of gradient boosting machines, in the Higgs Boson Machine Learning Challenge (https://www.kaggle.com/c/higgs-boson/discussion/10335).

Besides Keras, François Chollet has also provided the most useful and insightful perspective on how to win a Kaggle competition in an answer of his on the Quora website: https://www.quora.com/Why-has-Keras-been-so-successful-lately-at-Kaggle-competitions.

Fast iterations of multiple attempts, guided by empirical (more than theoretical) evidence, are actually all that you need. We don’t think that there are many more secrets to winning a Kaggle competition than the ones he pointed out in his answer.

Notably, François Chollet also hosted his own competition on Kaggle (https://www.kaggle.com/c/abstraction-and-reasoning-challenge/), which is widely recognized as being the first general AI competition in the world.

Competition after competition, the community revolving around Kaggle grew to touch one million in 2017, the same year as, during her keynote at Google Next, Fei-Fei Li, Chief Scientist at Google, announced that Google Alphabet was going to acquire Kaggle. Since then, Kaggle has been part of Google.

Today, the Kaggle community is still active and growing. In a tweet of his (https://twitter.com/antgoldbloom/status/1400119591246852096), Anthony Goldbloom reported that most of its users, other than participating in a competition, have downloaded public data (Kaggle has become an important data hub), created a public Notebook in Python or R, or learned something new in one of the courses offered:

Figure 1.1: A bar chart showing how users used Kaggle in 2020, 2019, and 2018

Through the years, Kaggle has offered many of its participants even more opportunities, such as:

And, most importantly, learning more about the skills and technicalities involved in data science.

Other competition platforms

Though this book focuses on competitions on Kaggle, we cannot forget that many data competitions are held on private platforms or on other competition platforms. In truth, most of the information you will find in this book will also hold for other competitions, since they essentially all operate under similar principles and the benefits for the participants are more or less the same.

Although many other platforms are localized in specific countries or are specialized only for certain kinds of competitions, for completeness we will briefly introduce some of them, at least those we have some experience and knowledge of:

Other minor platforms are CrowdAI (https://www.crowdai.org/) from École Polytechnique Fédérale de Lausanne in Switzerland, InnoCentive (https://www.innocentive.com/), Grand-Challenge (https://grand-challenge.org/) for biomedical imaging, DataFountain (https://www.datafountain.cn/business?lang=en-US), OpenML (https://www.openml.org/), and the list could go on. You can always find a large list of ongoing major competitions at the Russian community Open Data Science (https://ods.ai/competitions) and even discover new competition platforms from time to time.

You can see an overview of running competitions on the mlcontests.com website, along with the current costs for renting GPUs. The website is often updated and it is an easy way to get a glance at what’s going on with data science competitions across different platforms.

Kaggle is always the best platform where you can find the most interesting competitions and obtain the widest recognition for your competition efforts. However, picking up a challenge outside of it makes sense, and we recommend it as a strategy, when you find a competition matching your personal and professional interests. As you can see, there are quite a lot of alternatives and opportunities besides Kaggle, which means that if you consider more competition platforms alongside Kaggle, you can more easily find a competition that might interest you because of its specialization or data.

In addition, you can expect less competitive pressure during these challenges (and consequently a better ranking or even winning something), since they are less known and advertised. Just expect less sharing among participants, since no other competition platform has reached the same richness of sharing and networking opportunities as Kaggle.

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Key benefits

  • Learn how Kaggle works and how to make the most of competitions from over 30 expert Kagglers
  • Sharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoML
  • A concise collection of smart data handling techniques for modeling and parameter tuning

Description

Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the knowledge they’ve accumulated along the way. As well as Kaggle-specific tips, you’ll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You’ll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!

Who is this book for?

This book is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this book useful. A basic understanding of machine learning concepts will help you make the most of this book.

What you will learn

  • Get acquainted with Kaggle as a competition platform
  • Make the most of Kaggle Notebooks, Datasets, and Discussion forums
  • Create a portfolio of projects and ideas to get further in your career
  • Design k-fold and probabilistic validation schemes
  • Get to grips with common and never-before-seen evaluation metrics
  • Understand binary and multi-class classification and object detection
  • Approach NLP and time series tasks more effectively
  • Handle simulation and optimization competitions on Kaggle

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Table of Contents

19 Chapters
Part I: Introduction to Competitions Chevron down icon Chevron up icon
Introducing Kaggle and Other Data Science Competitions Chevron down icon Chevron up icon
Organizing Data with Datasets Chevron down icon Chevron up icon
Working and Learning with Kaggle Notebooks Chevron down icon Chevron up icon
Leveraging Discussion Forums Chevron down icon Chevron up icon
Part II: Sharpening Your Skills for Competitions Chevron down icon Chevron up icon
Competition Tasks and Metrics Chevron down icon Chevron up icon
Designing Good Validation Chevron down icon Chevron up icon
Modeling for Tabular Competitions Chevron down icon Chevron up icon
Hyperparameter Optimization Chevron down icon Chevron up icon
Ensembling with Blending and Stacking Solutions Chevron down icon Chevron up icon
Modeling for Computer Vision Chevron down icon Chevron up icon
Modeling for NLP Chevron down icon Chevron up icon
Simulation and Optimization Competitions Chevron down icon Chevron up icon
Part III: Leveraging Competitions for Your Career Chevron down icon Chevron up icon
Creating Your Portfolio of Projects and Ideas Chevron down icon Chevron up icon
Finding New Professional Opportunities Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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2 star 11.8%
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Dmitry Efimov Apr 23, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A more accurate title would be "Tricks and tips for Kaggle competitions". In case you are thinking about participating in Kaggle competitions or already have, you should get this book. The book cannot guarantee that you'll win the competition. After all, winning requires a lot of creative thinking. But using common practices will definitely help you climb the leaderboard. The book is the first of its kind and I would definitely buy it for my home library. Often overly detailed, this is a great practical guide for Kaggle competitions, including Kaggle platform overview, many lines of Python code, strategies and best practices. It does not discuss machine learning algorithms or machine learning theory in general; for that, you should look for specialized machine learning theory books. However, the authors provide a list of most popular algorithms used in competitions, as well as their key features and most important parameters. The chapters about Bayesian Optimization and Blending/Stacking are probably the best I have seen so far. The book has a lot of blurbs with interviews from Kagglers, which is the most entertaining part for me. In my opinion, these blurbs can be converted to another great book. In spite of the fact that this book is fantastic now, I expect it to become outdated pretty quickly given the level of details provided by the authors and the pace at which machine learning is progressing. It would be wise for the authors to consider a new edition when it becomes outdated. I would recommend this book to people who are interested in machine learning competitions and are familiar with machine learning theory.
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Manoj Jagannath Sabnis Jun 30, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I received the Book on time and the Book Packaging was good. I'll also recommend Amazon to my Friends and Relatives.
Amazon Verified review Amazon
. Sep 12, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've been in ML industry for years, but still I learned lots of new things, thanks to this book.It is well explained what to do when competition metrics is not one of the standard ones in tensorflow or pytorch, in section "custom metrics, and custom objective funciton" and "post-processing".Adversarial validation to estimate difference between distributions of training and test datasetpseudo-labeling: to label some test datasetHyperparameter search with Halving (HalvingGridSearchCV, HalvingRandomSearchCV)Bayesian optimization using scikit-optimize, kerasTuner, TPE, optunaJoin the book’s Discord workspace for a monthly Ask me Anything session with the authors:Data augmentation for NLP (albumentations)This book also interviewed lots of Kaggle experts for in-depth insights and technical/professional tips.
Amazon Verified review Amazon
anandprakash Nov 08, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
At first, I received the wrong book for which I placed a replacement. Today I returned the wrong book and got the replacement of the right one.Coming on to the condition of the book:The pages are glossy and smooth as you can see in the pictures.The print is of great quality.The pictures inside the book might not be that clear but it is still readable. (notice the 2nd pic)
Amazon Verified review Amazon
WU. Apr 26, 2022
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This is the first book that I've come across that is singularly focused on the rules, format, tips, and best practices for Kaggle ML/Data Science competitions. As such, this book is well-deserving of your dollars and attention.Before even delving into specific aspects of Machine Learning, the authors chose to spend a great deal of time (chapters 1-5) outlining the basics of Kaggle competitions from the history of the platform, to teams, datasets, notebooks, discussion forums, etiquette, and the different types of competitions available on the site. Complete beginners to Kaggle would get the most use of these chapters, it sure beats trying to figure all of this stuff out on your own.The remaining chapters start getting increasingly advanced in terms of subjects and techniques. I definitely appreciate the authors discussing the importance of the design of good model validation before delving deeper into hyperparameter tuning, walk before you run!The later chapters really drill into more advanced techniques such as using hyperparameter studies and Bayesian optimization to extract the best combination of values for your specific model. Ensembling and stacking are presented as clearly as I've seen anywhere, along with the most helpful snippets of code to date on a ML book. This alone might be worth the price for some. Intermediate and advanced users will get the most of these chapters.A nice extra is the Q&A sections in each chapter with "Kaggle Masters", people who have either won competitions in the past or who regularly place very high in many competitions. These are done informally and provide a lot of great tips.Now, who is this book really for? If you are new to Machine Learning, I'd say that perhaps this would not be the best place to start. While the book is great for what it sets out to do (teach you to become a better competitor) it is not perfect.Some information that could be helpful to beginners is grossly glossed over, such as the explanation of specific hyperparams. It is very odd how they chose to handle this. Case-and-point: when going over XGBoost hyperparams such as "n_estimators", they describe it as "usually an integer ranging from 10 to 5,000". Compare this with Corey Wade's explanation("Gradient Boosting with XGBoost and SciKit Learn", also from Packt ), "The number of trees in the ensemble/the number of trees trained on the residuals after each boosting round. Increasing might improve accuracy on larger datasets". Which is more useful you think? You either explain it clearly for the benefit of all or just leave it out. Giving the domain and range is not a proper substitution. Obviously, the author's expect the reader to have had some exposure to algorithms and modeling as the pace of several sections move a little too quickly for the complete beginner. As such, I would say this is a perfect book for semi-intermediate to advanced users looking to extract the most out of their models.All in all, this is an excellent resource that will be sure to help countless current and aspiring data scientists in their journeys to become masters of their crafts. I wish I had access to this text five years ago...Highly Recommended!
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When we publish the book, the code files will also be available to download from the Packt website.

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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.