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The Kaggle Book: Data analysis and machine learning for competitive data science
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.
Purchase of the print or Kindle book includes a free eBook in the PDF format.
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.
Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!
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 P
Purchase of the print or Kindle book includes a free eBook in the PDF format.
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.
Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!
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 P
530 pages, Kindle Edition
Published April 22, 2022
76 people are currently reading
293 people want to read
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Displaying 1 - 6 of 6 reviews
April 26, 2022
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 hyper-parameter 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 scientist in their journeys to become masters of their crafts. Highly Recommended!
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 hyper-parameter 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 scientist in their journeys to become masters of their crafts. Highly Recommended!
July 29, 2023
This book came highly recommended by a friend who wanted me to understand the practical utilization of Kaggle platform and gain further insights into machine learning.
Although quite technical, the interviews within the book are extremely enlightening. However, the content could be more personalized and less repetitive.
Although quite technical, the interviews within the book are extremely enlightening. However, the content could be more personalized and less repetitive.
February 11, 2023
This is not a technical book.
Includes interviewers with Kagglers only.
Info here can be found in various YouTube interviews with Kaggle GM's.
It looks like a good attempt to exploit Kaggle wannabes. Worked on me.
Includes interviewers with Kagglers only.
Info here can be found in various YouTube interviews with Kaggle GM's.
It looks like a good attempt to exploit Kaggle wannabes. Worked on me.
September 30, 2023
I am speaking as a novice with no desire to land a role in or adjacent to data science. My career is in marketing and communications. I was initially attracted to Kaggle as a way to shorten the learning curve on various Coursera courses, which I am completing strictly for the challenge of learning.
The Kaggle Book proved to be exactly what I needed, exactly when I needed it. The authors clearly explained concepts previously difficult for me to grasp. Their use of repetition is a strength, not a hindrance, at least from my perspective, as it reinforces learning and over the course of the book explains how functionality can and should be used across various domains.
I have put what I learned into three of the beginner knowledge competitions on Kaggle, while reading The Kaggle Book. So far I'm in the top quartile on two of these, with plans to implement straties with the aim of continuing to climbing up the leaderboards.
The Kaggle Book proved to be exactly what I needed, exactly when I needed it. The authors clearly explained concepts previously difficult for me to grasp. Their use of repetition is a strength, not a hindrance, at least from my perspective, as it reinforces learning and over the course of the book explains how functionality can and should be used across various domains.
I have put what I learned into three of the beginner knowledge competitions on Kaggle, while reading The Kaggle Book. So far I'm in the top quartile on two of these, with plans to implement straties with the aim of continuing to climbing up the leaderboards.
August 17, 2024
I would use this book for ideas on upcoming competition no doubt.
February 22, 2024
Best intro to Kaggle platform, by two well seasoned Kagglers. Introduction of the platforms as well as if Data Science terminology, tools and best practices. Interviews with 30 Kaggle Grandmasters (includ8ng with myself 😀). Highly recommended for learning what Kaggle is about.
Displaying 1 - 6 of 6 reviews