🕞Obtain The Kaggle Book: Data analysis and machine learning for competitive data science by Konrad Banachewicz
The Kaggle Book: Data analysis and machine learning for competitive data science by Konrad Banachewicz
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. 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 Description 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 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. 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. Table of Contents Introducing Kaggle and Other Data Science Competitions Organizing Data with Datasets Working and Learning with Kaggle Notebooks Leveraging Discussion Forums Competition Tasks and Metrics Designing Good Validation Modeling for Tabular Competitions Hyperparameter Optimization Ensembling with Blending and Stacking Solutions Modeling for Computer Vision Modeling for NLP Simulation and Optimization Competitions Creating Your Portfolio of Projects and Ideas Finding New Professional Opportunities Read more
I was wary of reading this book, let alone reviewing it. I've seen a LOT of people who are so into Kaggle that they don't understand that real-world data is FAR messier, complicated, and requires a lot of work before you get to the fun of modeling. I didn't want my review to help fuel more of that thinking. Thankfully, I was wrong. This book is so much more than a cheat sheet guide to winning Kaggle competitions. It helps the reader use Kaggle as a stepping stone toward an AI/ML-related profession, such as data science or ML engineering. It starts with the history of Kaggle before it spends most of the remaining pages talking about all respects of a competition. There are chapters on organizing data; using the Kaggle notebook environment; using their discussion forums; the various tasks and metrics seen in a competition; validating, modeling, and optimizing your models; bringing together different solutions for the best results; and specific advice for both computer vision, natural language processing, and optimization tasks. The book ends with chapters on building a portfolio both within and without Kaggle as well as finding professional work. If you're interested in Kaggle or machine learning, this is a great book to get you started.
Publisher -> Packt Publishing (April 22, 2022) Language -> English Paperback -> 530 pages ISBN-10 -> 1801817472 ISBN-13 -> 978-1801817479 Item Weight -> 1.98 pounds Dimensions -> 7.5 x 1.2 x 9.25 inches Best Sellers Rank: #36,363 in Books (See Top 100 in Books) #6 in Natural Language Processing (Books) #11 in Artificial Intelligence (Books) #41 in Artificial Intelligence & Semantics
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