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The Kaggle Book

You're reading from   The Kaggle Book Data analysis and machine learning for competitive data science

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801817479
Length 534 pages
Edition 1st Edition
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Authors (2):
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Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Konrad Banachewicz Konrad Banachewicz
Author Profile Icon Konrad Banachewicz
Konrad Banachewicz
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Toc

Table of Contents (20) Chapters Close

Preface
1. Part I: Introduction to Competitions
2. Introducing Kaggle and Other Data Science Competitions FREE CHAPTER 3. Organizing Data with Datasets 4. Working and Learning with Kaggle Notebooks 5. Leveraging Discussion Forums 6. Part II: Sharpening Your Skills for Competitions
7. Competition Tasks and Metrics 8. Designing Good Validation 9. Modeling for Tabular Competitions 10. Hyperparameter Optimization 11. Ensembling with Blending and Stacking Solutions 12. Modeling for Computer Vision 13. Modeling for NLP 14. Simulation and Optimization Competitions 15. Part III: Leveraging Competitions for Your Career
16. Creating Your Portfolio of Projects and Ideas 17. Finding New Professional Opportunities 18. Other Books You May Enjoy
19. Index

Finding New Professional Opportunities

After introducing how to better highlight your work and achievements in competitions in the previous chapter, we will now conclude our overview of how Kaggle can positively affect your career. This last chapter discusses the best ways to leverage all your efforts to find new professional opportunities. We expect you now have all the previously described instruments (your Kaggle Discussions, Notebooks, and Datasets, and a GitHub account presenting quite a few projects derived from Kaggle), so this chapter will move to softer aspects: how to network and how to present your Kaggle experience to recruiters and companies.

It is common knowledge that networking opens up many possibilities, from being contacted about new job opportunities that do not appear on public boards to having someone to rely on for data science problems you are not an expert in. Networking on Kaggle is principally related to team collaboration during competitions and connections...

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