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  • Book Overview & Buying The Kaggle Book
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The Kaggle Book

The Kaggle Book - Second Edition

By : Luca Massaron, Bojan Tunguz, Konrad Banachewicz
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The Kaggle Book

The Kaggle Book

By: Luca Massaron, Bojan Tunguz, Konrad Banachewicz

Overview of this book

Kaggle has become the proving ground for millions of data enthusiasts worldwide, offering what no classroom tutorial can match: battle-tested skills built through real-world challenges and the hands-on experience that employers seek. Every competition sharpens your data analysis skills, expands your network within the data scientist community, and gives compelling proof of expertise to unlock career opportunities. The first book of its kind, The Kaggle Book brings together everything you need to excel in competitions, data science projects, and beyond. This new edition includes fresh content and new chapters on Kaggle Models, time series, and Generative AI competitions, with three Kaggle Grandmasters guiding you through modeling strategies and sharing hard-earned insights accumulated over years of competition. The book extends far past competition tactics, revealing techniques for tackling image, tabular, and textual data as well as reinforcement learning tasks. You’ll also discover tips for designing better validation schemes and working confidently with both standard and unconventional evaluation metrics. Whether you want to climb the Kaggle leaderboard, accelerate your data science career, or improve the accuracy of your models, this book is for you. Join our Discord community of over 1,000 members to learn, share, and grow together!
Table of Contents (23 chapters)
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1
Part 1: Your Kaggle Launchpad: Mastering the Essentials
7
Part 2: Elevating Your Game: Advanced Techniques for Competitive Success
17
Part 3: Kaggle for Your Career: Building Your Profile and Finding Opportunities
21
Other Books You May Enjoy
22
Index

Generative AI in Kaggle Competitions

Generative AI—particularly Large Language Models (LLMs)—has quickly become a transformative force in data science workflows. Unlike traditional models that predict values or labels, LLMs can generate entirely new data: from synthesized text and code to feature suggestions and documentation. This capability has opened up new possibilities across the entire ML pipeline, from data preparation to model development.

In this chapter, you will learn how the transformative power of generative AI, particularly LLMs, is being practically applied in the competitive data science landscape of Kaggle. We will move beyond theoretical concepts to explore the specific strategies and techniques that are giving competitors a substantial edge. You will discover how to leverage LLMs across the entire machine learning pipeline, from cleaning and augmenting data to generating code and summarizing complex information. We will dissect crucial skills such...

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The Kaggle Book
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