The Kaggle Book: Data analysis and machine learning for competitive data science PDF AZW3 EPUB MOBI TXT Download

Get a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters. Discover tips, tricks, and best practices for competing effectively on Kaggle and becoming a better data scientist.Key FeaturesLearn how Kaggle works and how to make the most of competitions from over 30 expert KagglersSharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoMLA concise collection of smart data handling techniques for modeling and parameter tuningBook DescriptionMillions 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.What you will learnGet acquainted with Kaggle as a competition platformMake the most of Kaggle Notebooks, Datasets, and Discussion forumsCreate a portfolio of projects and ideas to get further in your careerDesign k-fold and probabilistic validation schemesGet to grips with common and never-before-seen evaluation metricsUnderstand binary and multi-class classification and object detectionApproach NLP and time series tasks more effectivelyHandle simulation and optimization competitions on KaggleWho this book is forThis 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.Table of ContentsIntroducing Kaggle and Other Data Science CompetitionsOrganizing Data with DatasetsWorking and Learning with Kaggle NotebooksLeveraging Discussion ForumsCompetition Tasks and MetricsDesigning Good ValidationModeling for Tabular CompetitionsHyperparameter OptimizationEnsembling with Blending and Stacking SolutionsModeling for Computer VisionModeling for NLPSimulation and Optimization CompetitionsCreating Your Portfolio of Projects and IdeasFinding New Professional Opportunities

Konrad Banachewicz
Packt Publishing (April 22, 2022)
530 pages

File Size: 4 MB
Available File Formats: PDF AZW3 DOCX EPUB MOBI TXT or Kindle audiobook Audio CD(Several files can be converted to each other)
Language: English, Francais, Italiano, Espanol, Deutsch, chinese

Konrad Banachewicz holds a PhD in statistics from Vrije Universiteit Amsterdam. He is a lead data scientist at eBay and a Kaggle Grandmaster. He worked in a variety of financial institutions on a wide array of quantitative data analysis problems. In the process, he became an expert on the entire lifetime of a data product cycle.Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms. <div id="

  • I was wary of reading this book, let alone reviewing it. I’ve seen a LOT of people who are so into Kaggle that they don’t understand that real-world data is FAR messier, complicated, and requires a lot of work before you get to the fun of modeling. I didn’t want my review to help fuel more of that thinking.Thankfully, I was wrong.This book is so much more than a cheat sheet guide to winning Kaggle competitions. It helps the reader use Kaggle as a stepping stone toward an AI/ML-related profession, such as data science or ML engineering.It starts with the history of Kaggle before it spends most of the remaining pages talking about all respects of a competition. There are chapters on organizing data; using the Kaggle notebook environment; using their discussion forums; the various tasks and metrics seen in a competition; validating, modeling, and optimizing your models; bringing together different solutions for the best results; and specific advice for both computer vision, natural language processing, and optimization tasks. The book ends with chapters on building a portfolio both within and without Kaggle as well as finding professional work.If you’re interested in Kaggle or machine learning, this is a great book to get you started.
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  • I wish this book existed years ago when I started my journey from newbie to Grandmaster. There is a lot to absorb in this book and my recommendation would be to jump into the areas that match your current need. For example, if you are joining a competition, learn about how to evaluate a specific competition, but also read about approaching discussions, for example. That doesn’t mean you shouldn’t read it from beginning to end, but it seems like a reference tool that comes in handy during times of need after chapter 5.There are gems throughout the book. I think the #1 question will be: “will this help me win a competition?” Winning is hard. Winning on Kaggle against some of the best data scientists in the world takes rigor and determination. This book gives you the foundation upon which to build. The Kaggle Book can help you get up to speed much faster and become a useful resource to go back to when you are trying to understand something. So it’s a tool in your toolbox, not a solution.For me, the single best feature was reading the interviews with the amazingly talented Kaggle Grandmasters and Masters. I actually have a document with interviews with Grandmasters and competition winners. I go back to that document from time to time for inspiration, reminders, and tips. This book has a dozen or more interviews all in one resource. Really priceless advice.Finally, the two authors are Grandmasters. They aren’t authors who just researched a subject and wrote a book on it. The authors are brilliant, highly-respected data scientists. This is a true Kaggle masterclass.
  • 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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