The Kaggle Book
Packt Publishing Limited (Verlag)
978-1-83508-320-8 (ISBN)
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 DescriptionKaggle 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!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 forThis 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.
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.
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
Generative AI in Kaggle Competitions
Simulation and Optimization Competitions
Creating Your Portfolio of Projects and Ideas
Finding New Professional Opportunities
| Erscheinungsdatum | 21.06.2025 |
|---|---|
| Vorwort | Anthony Goldbloom |
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Studium ► Querschnittsbereiche ► Epidemiologie / Med. Biometrie |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 1-83508-320-X / 183508320X |
| ISBN-13 | 978-1-83508-320-8 / 9781835083208 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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