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by Konrad Banachewicz (Author), Luca Massaron (Author), Anthony Goldbloom (Foreword by)
Get a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters. Discover tips, tricks, and best practices for competing effectively on Kaggle and becoming a better data scientist.
Key Features:
- Learn how Kaggle works and how to make the most of competitions from over 30 expert Kagglers
- Sharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoML
- A concise collection of smart data handling techniques for modeling and parameter tuning
Book Description:
Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with the rest of the community, and gain valuable experience to help grow your career.
The first book of its kind, The Kaggle Book assembles the techniques and skills you'll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won't easily find elsewhere, and the knowledge they've accumulated along the way. As well as Kaggle-specific tips, you'll learn more general techniques for approaching tasks based on image, tabular, and textual data, and reinforcement learning. You'll design better validation schemes and work more comfortably with different evaluation metrics.
Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you.
What You Will Learn:
- Get acquainted with Kaggle as a competition platform
- Make the most of Kaggle Notebooks, Datasets, and Discussion forums
- Create a portfolio of projects and ideas to get further in your career
- Understand binary and multi-class classification and object detection
- Approach NLP and time series tasks more effectively
- Design k-fold and probabilistic validation schemes
- Get to grips with common and never-before-seen evaluation metrics
- Handle simulation and optimization competitions on Kaggle
Who this book is for:
This book is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this book useful.
A basic understanding of machine learning concepts will help you make the most of this book.
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