Stay one step ahead of your competitors with proven tips, strategies, and insights from over 30 Kaggle Masters and Grandmasters and become a better data scientist.
This new edition features updated content and new chapters on Kaggle Models, time series, and Generative AI competitions.
Key Features
- Learn how Kaggle works to make the most of every competition with winning strategies from 30+ expert Kagglers
- Sharpen your modeling skills with feature engineering, adversarial validation, gradient boosting, tabular deep learning, ensembling, and AutoML
- Master data handling techniques for smarter modeling and parameter tuning
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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.
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What you will learn
- Get acquainted with Kaggle as a competition platform
- Make the most of Kaggle Notebooks, Datasets, Models and Discussion forums
- Build a compelling portfolio of projects and ideas to advance your career
- Understand binary and multi-class classification, as well as object detection
- Approach NLP and time series problems with greater efficiency
- Design k-fold and probabilistic validation schemes and experiment with multiple approaches
- Get to grips with common and never-before-seen evaluation metrics
- Handle simulation, optimization, and the new Generative AI competitions on Kaggle
Who this book is for
This book is for anyone interested in Kaggle, whether you’re just starting out, a veteran user, or somewhere in between. Data analysts and data scientists looking to improve their performance in Kaggle competitions and improve their job prospects with tech giants will find this book useful.
A basic understanding of machine learning concepts will help you get the most out of this book.
Table of Contents
- Introducing Kaggle and Other Data Science Competitions
- Organizing Data with Datasets
- Working and Learning with Kaggle Notebooks
- Kaggle Models
- Leveraging Discussion Forums
- Competition Tasks and Metrics
- Designing Good Validation
- Modeling for Tabular Competitions
- Hyperparameter Optimization
- Ensembling with Blending and Stacking Solutions
- Modeling for Computer Vision
- Modeling for NLP
(N.B. Please use the Read Sample option to see further chapters)
Luca Massaron is a data scientist with over a decade of experience in transforming data into high-impact, innovative artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is the author of numerous bestselling books on AI, machine learning, and algorithms. Luca is also a 3x Kaggle Grandmaster who reached number 7 in the worldwide user rankings for his performance in data science competitions. Additionally, he is recognized as a Google Developer Expert (GDE) in AI, Kaggle, and the cloud.
Bojan Tunguz is the founder and CEO of TabulAI, a start-up focused on applying machine learning and AI to structured-data problems. Before founding TabulAI, he worked at three other machine learning start-ups and most recently at NVIDIA. He holds a PhD in theoretical physics from the University of Illinois and has taught as a professor at three liberal arts colleges.
Konrad Banachewicz holds a PhD in statistics from Vrije Universiteit Amsterdam. His academic work focused on extreme dependency modeling in credit risk. In addition to his research activities, he was a tutor and supervised master's students. He transitioned from classical statistics to data mining and machine learning before “data science” became a buzzword. Over the next decade, he tackled quantitative analysis problems in various financial institutions, becoming an expert in the full life cycle of a data product. His work spanned high-frequency trading to credit risk, predicting potato prices, and analyzing anomalies in the performance of large-scale industrial equipment. He is a believer in knowledge sharing and also competes on Kaggle.
