The Kaggle Book : Data Analysis and Machine Learning for Competitive Data Science / Konrad Banachewicz, Luca Massaron, and Anthony Goldbloom.

Format:

Book

Author/Creator:

Banachewicz, Konrad, author.

Massaron, Luca, author.

Goldbloom, Anthony, author.

Series:

Expert insight.

Expert insight

Language:

English

Subjects (All):

Machine learning.

Big data.

Physical Description:

1 online resource (531 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2022]

Biography/History:

Banachewicz Konrad: Konrad Banachewicz is the author of the bestselling, The Kaggle Book and The Kaggle Workbook. He is a data science manager with experience stretching longer than he likes to ponder on. He holds a PhD in statistics from Vrije Universiteit Amsterdam, where he focused on problems of extreme dependency modeling in credit risk. He slowly moved from classic statistics towards machine learning and into the business applications world. Massaron Luca: Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms.

Summary:

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.

Contents:

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.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781801812214

1801812217

OCLC:

1313909123