Data Analysis and Machine Learning with Kaggle: How to win competitions on Kaggle and build a successful career in data science

Get a step ahead of your competitors with a concise collection of smart data handling and modeling techniques

Key Features

• Learn how Kaggle works and how to make the most of competitions from two expert Kagglers
• Sharpen your modeling skills with ensembling, feature engineering, adversarial validation, AutoML, transfer learning, and techniques for parameter tuning
• Discover tips, tricks, and best practices for winning on Kaggle and becoming a better data scientist

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, Data Analysis and Machine Learning with Kaggle assembles the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two masters of Kaggle walk you through modeling strategies you won’t easily find elsewhere, and the tacit 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 data, tabular data, 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 and other competition platforms
• Make the most of Kaggle Notebooks, Datasets, and Discussion forums
• Understand different modeling tasks including binary and multi-class classification, object detection, NLP (Natural Language Processing), and time series
• Design good validation schemes, learning about k-fold, probabilistic, and adversarial validation
• Get to grips with evaluation metrics including MSE and its variants, precision and recall, IoU, mean average precision at k, as well as never-before-seen metrics
• Handle simulation and optimization competitions on Kaggle
• Create a portfolio of projects and ideas to get further in your career

Who This Book Is For

This book is suitable for Kaggle users and data analysts/scientists of all experience levels who are trying to do better in Kaggle competitions and secure jobs with tech giants.

Table of Contents

1. Introducing Data Science competitions
2. Organizing Data with Datasets
3. Working and learning with kaggle notebooks
4. Leveraging Discussion forums
5. Detailing competition tasks and metrics
6. Designing good validation schemes
7. Ensembling and stacking solutions
8. Modelling for tabular competitions
9. Modeling for image classification and segmentation
10. Modeling for Natural Language Processing
11. Handling simulation and optimization competitions
12. Creating your portfolio of projects and ideas
13. Finding new professional opportunities