The Kaggle Book: Data analysis and machine

The Kaggle Book: Data analysis and machine learning for competitive data science by Konrad Banachewicz, Luca Massaron, Anthony Goldbloom

• The Kaggle Book: Data analysis and machine learning for competitive data science
• Konrad Banachewicz, Luca Massaron, Anthony Goldbloom
• Page: 428
• Format: pdf, ePub, mobi, fb2
• ISBN: 9781801817479
• Publisher: Packt Publishing

Download The Kaggle Book: Data analysis and machine learning for competitive data science

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Get a step ahead of your competitors with a concise collection of smart data handling and modeling techniques Learn how Kaggle works and how to make the most of competitions from two expert Kagglers Sharpen your modeling skills with ensembling, feature engineering, adversarial validation, AutoML, transfer learning, and techniques for parameter tuning Discover tips, tricks, and best practices for winning on Kaggle and becoming a better data scientist Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with the rest of the community, and gain valuable experience to help grow your career. The first book of its kind, Data Analysis and Machine Learning with Kaggle assembles the techniques and skills you'll need for success in competitions, data science projects, and beyond. Two masters of Kaggle walk you through modeling strategies you won't easily find elsewhere, and the tacit knowledge they've accumulated along the way. As well as Kaggle-specific tips, you'll learn more general techniques for approaching tasks based on image data, tabular data, textual data, and reinforcement learning. You'll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. Get acquainted with Kaggle and other competition platforms Make the most of Kaggle Notebooks, Datasets, and Discussion forums Understand different modeling tasks including binary and multi-class classification, object detection, NLP (Natural Language Processing), and time series Design good validation schemes, learning about k-fold, probabilistic, and adversarial validation Get to grips with evaluation metrics including MSE and its variants, precision and recall, IoU, mean average precision at k, as well as never-before-seen metrics Handle simulation and optimization competitions on Kaggle Create a portfolio of projects and ideas to get further in your career This book is suitable for Kaggle users and data analysts/scientists of all experience levels who are trying to do better in Kaggle competitions and secure jobs with tech giants. Introducing Data Science competitions Organizing Data with Datasets Working and learning with kaggle notebooks Leveraging Discussion forums Detailing competition tasks and metrics Designing good validation schemes Ensembling and stacking solutions Modelling for tabular competitions Modeling for image classification and segmentation Modeling for Natural Language Processing Handling simulation and optimization competitions Creating your portfolio of projects and ideas Finding new professional opportunities

Kaggle - Wikipedia
OverviewKaggle communityHow Kaggle competitions work1 of 3Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish Continue on en.wikipedia.org »2 of 3In June 2017, Kaggle announced that it passed 1 million registered users, or Kagglers, and as of 2021 has over 8 million registered users. The community spans 194 countries. It is a diverse community,Continue on en.wikipedia.org »3 of 3Alongside its public competitions, Kaggle also offers private competitions limited to Kaggle's top participants. Kaggle offers a free tool for data science teachers to run academic machine learning coContinue on en.wikipedia.org »
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