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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

Summary (and some parting words)

In this chapter, we have discussed how competing on Kaggle can help improve your career prospects. We have touched on building connections, both by teaming up on competitions and participating in events related to past competitions, and also on using your Kaggle experience in order to find a new job. We have discussed how, based on our experience and the experience of other Kagglers, results on Kaggle alone cannot ensure that you get a position. However, they can help you get attention from recruiters and human resource departments and then reinforce how you present competencies in data science (if they are supported by a carefully-built portfolio, as we described in the previous chapter).

This chapter also marks the conclusion of the book. Through fourteen chapters, we have discussed Kaggle competitions, Datasets, Notebooks, and discussions. We covered technical topics in machine learning and deep learning (from evaluation metrics to simulation...

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