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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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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

Simulation and Optimization Competitions

Reinforcement learning (RL) is an interesting case among the different branches of machine learning. On the one hand, it is quite demanding from a technical standpoint; various intuitions from supervised learning do not hold, and the associated mathematical apparatus is significantly more advanced. On the other hand, it is the easiest to explain to an outsider or layperson. A simple analogy is teaching your pet (I am very intentionally trying to steer clear of the dogs versus cats debate) to perform tricks: you provide a treat for a trick well done, and refuse it otherwise.

RL was a latecomer to the competition party on Kaggle, but the situation has changed in the last few years with the introduction of simulation competitions. In this chapter, we will describe this new and exciting part of the Kaggle universe. As of the time of writing, there have already been ten Featured competitions and five Playground ones. We expect this number to...

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The Kaggle Book
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