The Kaggle Book: Master data science competitions with machine learning, GenAI, and LLMs, Edition 2
About this ebook
- 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
- 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
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.
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About the author
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.
