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THE KAGGLE WORKBOOK self-learning exercises and valuable insights for Kaggle data science competitions /
Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Challenge yourself to start thinking like a Kaggle Gran...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing Ltd.,
[2023]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Banachewicz, Konrad, |e author. | |
| 245 | 1 | 4 | |a THE KAGGLE WORKBOOK |h [electronic resource] : |b self-learning exercises and valuable insights for Kaggle data science competitions / |c Konrad Banachewicz, Luca Massaron. |
| 260 | |a Birmingham, UK : |b Packt Publishing Ltd., |c [2023] | ||
| 300 | |a 1 online resource | ||
| 520 | |a Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features 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 Book Description 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 Who this book is for If you're new to Kaggle and want to sink your teeth into practical exercises, start with The Kaggle Book, first. A basic understanding of the Kaggle platform, along with knowledge of machine learning and data science is a prerequisite. This book is suitable for anyone starting their Kaggle journey or veterans trying to get better at it. Data analysts/scientists who want to do better in Kaggle competitions and secure jobs with tech giants will find this book helpful. | ||
| 505 | 0 | |a Cover -- Copyright -- Table of Contents -- Preface -- Chapter 1: The Most Renowned Tabular Competition -- Porto Seguro's Safe Driver Prediction -- Understanding the competition and the data -- Understanding the evaluation metric -- Examining the top solution ideas from Michael Jahrer -- Building a LightGBM submission -- Setting up a denoising autoencoder and a DNN -- Ensembling the results -- Summary -- Chapter 2: The Makridakis Competitions -- M5 on Kaggle for Accuracy and Uncertainty -- Understanding the competition and the data -- Understanding the Evaluation Metric | |
| 505 | 8 | |a Examining the 4th place solution's ideas from Monsaraida -- Computing predictions for specific dates and time horizons -- Assembling public and private predictions -- Summary -- Chapter 3: Vision Competition: Cassava Leaf Disease Competition -- Understanding the data and metrics -- Building a baseline model -- Learning from top solutions -- Pretraining -- Test time augmentation -- Transformers -- Ensembling -- A complete solution -- Summary -- Chapter 4: NLP Competition -- Google Quest Q&A Labeling -- The baseline solution -- Learning from top solutions -- Summary -- Packt page | |
| 505 | 8 | |a Other Books You May Enjoy -- Index | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning |v Problems, exercises, etc. | |
| 650 | 0 | |a Big data |v Problems, exercises, etc. | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 655 | 0 | |a Electronic books. | |
| 655 | 7 | |a Problems and exercises. |2 fast |0 (OCoLC)fst01423783 | |
| 655 | 7 | |a Problems and exercises. |2 lcgft | |
| 700 | 1 | |a Massaron, Luca, |e author. | |
| 776 | 0 | 8 | |i Print version: |z 9781804610114 |
| 776 | 0 | 8 | |i Print version: |z 1804611212 |z 9781804611210 |w (OCoLC)1338300682 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781804611210/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a YBP Library Services |b YANK |n 304473138 | ||
| 938 | |a EBSCOhost |b EBSC |n 3542594 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL30368279 | ||
| 994 | |a 92 |b IZTAP | ||