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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Manoj Jagannath Sabnis Jun 30, 2024

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

Placeholder Jun 19, 2024

Content is excellent.Amazon packaging was good. Pages are glossy and you also recieve a free ebook from packt

Namig Nurullayev Mar 03, 2024

If you want to read this book, keep in mind that you need a lot of debuggings since technology moves fast and syntax of python packages are also changing. Would definetly not recommend this book for people who are eager about data science. Might be lots of frustration!

. Sep 12, 2023

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.

Vicente Bramadasbull Aug 08, 2023

Kaggle is a great platform, but not knowing how to take advantage of it from the first minute can be the worst mistake you can make. This book is excellent since it addresses critical issues that are often overlooked by beginners.

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

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

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