The Kaggle Book: Data analysis and machine learning for competitive data science. Questo articolo non è disponibile.The Kaggle Book: Data analysis and machine learning for competitive data science

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 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

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.

Table of Contents

1. Introducing Kaggle and Other Data Science Competitions
2. Organizing Data with Datasets
3. Working and Learning with Kaggle Notebooks
4. Leveraging Discussion Forums
5. Competition Tasks and Metrics
6. Designing Good Validation
7. Modeling for Tabular Competitions
8. Hyperparameter Optimization
9. Ensembling with Blending and Stacking Solutions
10. Modeling for Computer Vision
11. Modeling for NLP
12. Simulation and Optimization Competitions
13. Creating Your Portfolio of Projects and Ideas
14. Finding New Professional Opportunities

Konrad Banachewicz holds a PhD in statistics from Vrije Universiteit Amsterdam. He is a lead data scientist at eBay and a Kaggle Grandmaster. He worked in a variety of financial institutions on a wide array of quantitative data analysis problems. In the process, he became an expert on the entire lifetime of a data product cycle.

Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms.