Book Image

The Kaggle Book

By : Konrad Banachewicz, Luca Massaron
5 (2)
Book Image

The Kaggle Book

5 (2)
By: Konrad Banachewicz, Luca Massaron

Overview of this book

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. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!
Table of Contents (20 chapters)
Part I: Introduction to Competitions
Part II: Sharpening Your Skills for Competitions
Part III: Leveraging Competitions for Your Career
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The importance of validation in competitions

If you think about a competition carefully, you can imagine it as a huge system of experiments. Whoever can create the most systematic and efficient way to run these experiments wins.

In fact, in spite of all your theoretical knowledge, you will be in competition with the hundreds or thousands of data professionals who have more or less the same competencies as you.

In addition, they will be using exactly the same data as you and roughly the same tools for learning from the data (TensorFlow, PyTorch, Scikit-learn, and so on). Some will surely have better access to computational resources, although the availability of Kaggle Notebooks and generally decreasing cloud computing prices mean the gap is no longer so wide. Consequently, if you look at differences in knowledge, data, models, and available computers, you won’t find many discriminating factors between you and the other competitors that could explain huge performance...