The Kaggle Book: Data analysis and machine learning for competitive data science
Author: Konrad Banachewicz ,Luca Massaron ,Anthony Goldbloom (Foreword)
Publisher finelybook 出版社: Packt Publishing (April 22, 2022)
Language 语言: English
Print Length 页数: 530 pages
ISBN-10: 1801817472
ISBN-13: 9781801817479
Book Description
By finelybook
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
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 an amazing community of data scientists, and gain valuable experience to help grow your career.
The first book of its kind, The Kaggle Book assembles in one place 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, 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
Design k-fold and probabilistic validation schemes
Get to grips with common and never-before-seen evaluation metrics
Understand binary and multi-class classification and object detection
Approach NLP and time series tasks more effectively
Handle simulation and optimization competitions on Kaggle
