English | 2023 | ISBN: 978-1804611210 | 172 Pages | PDF, EPUB | 38 MB
Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises.
- Challenge yourself to start thinking like a Kaggle Grandmaster
- Fill your portfolio with impressive case studies that will come in handy during interviews
- Packed with exercises and notes pages for you to enhance your skills and record key findings
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.
What you will learn
- Take your modeling to the next level by analyzing different case studies
- Boost your data science skillset with a curated selection of exercises
- Combine different methods to create better solutions
- Get a deeper insight into NLP and how it can help you solve unlikely challenges
- Sharpen your knowledge of time-series forecasting
- Challenge yourself to become a better data scientist
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