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The Kaggle Workbook

The Kaggle Workbook

By : Konrad Banachewicz, Luca Massaron
4.8 (25)
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The Kaggle Workbook

The Kaggle Workbook

4.8 (25)
By: Konrad Banachewicz, Luca Massaron

Overview of this book

More than 80,000 Kaggle novices currently participate in Kaggle competitions. To help them navigate the often-overwhelming world of Kaggle, two Grandmasters put their heads together to write The Kaggle Book, which made plenty of waves in the community. Now, they’ve come back with an even more practical approach based on hands-on exercises that can help you start thinking like an experienced data scientist. In this book, you’ll get up close and personal with four extensive case studies based on past Kaggle competitions. You’ll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. Get into computer vision by discovering different solutions for identifying the type of disease present on cassava leaves. And see how the Kaggle community created predictive algorithms to solve the natural language processing problem of subjective question-answering. You can use this workbook as a supplement alongside The Kaggle Book or on its own alongside resources available on the Kaggle website and other online communities. Whatever path you choose, this workbook will help make you a formidable Kaggle competitor.
Table of Contents (7 chapters)
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Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

The Kaggle Book

Konrad Banachewicz

Luca Massaron

ISBN: 978-1-80181-747-9

  • 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

Machine Learning with PyTorch and Scikit-Learn

Sebastian Raschka

Yuxi (Hayden) Liu

ISBN: 978-1-80181-931-2

  • Explore frameworks, models, and techniques for machines to ‘learn’ from data
  • Use scikit...
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