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

Name: The Kaggle Book: Data analysis and machine learning for competitive data science
Rating: 1 (2 reviews)

Konrad Banachewicz · Luca Massaron

Apr 2022 · Packt Publishing Ltd

2 reviews

Ebook

534

Pages

Tools

About this ebook

Get a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters. Discover tips, tricks, and best practices for competing effectively on Kaggle and becoming a better data scientist. Purchase of the print or Kindle book includes a free eBook in the PDF format.Key Features

Learn how Kaggle works and how to make the most of competitions from over 30 expert Kagglers
Sharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoML
A concise collection of smart data handling techniques for modeling and parameter tuning

Book DescriptionMillions 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 an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the 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, tabular, 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. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!What you will learn

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

Who this book is for

This book is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this book useful. A basic understanding of machine learning concepts will help you make the most of this book.

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Ratings and reviews

2 reviews

Abhi Kumar

August 17, 2022

Bad

About the author

Konrad Banachewicz holds a PhD in statistics from Vrije Universiteit Amsterdam. His academic work focused on extreme dependency modeling in credit risk. In addition to his research activities, he was a tutor and supervised master's students. He transitioned from classical statistics to data mining and machine learning before “data science” became a buzzword. Over the next decade, he tackled quantitative analysis problems in various financial institutions, becoming an expert in the full life cycle of a data product. His work spanned high-frequency trading to credit risk, predicting potato prices, and analyzing anomalies in the performance of large-scale industrial equipment. He is a believer in knowledge sharing and also competes on Kaggle.

Luca Massaron is a data scientist with over a decade of experience in transforming data into high-impact, innovative artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is the author of numerous bestselling books on AI, machine learning, and algorithms. Luca is also a 3x Kaggle Grandmaster who reached number 7 in the worldwide user rankings for his performance in data science competitions. Additionally, he is recognized as a Google Developer Expert (GDE) in AI, Kaggle, and the cloud.

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