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

Sampul Depan
Packt Publishing Ltd, 22 Apr 2022 - 534 halaman

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.

 

Halaman terpilih

Isi

Introducing Kaggle and Other Data Science Competitions
3
Organizing Data with Datasets
37
Working and Learning with Kaggle Notebooks
53
Leveraging Discussion Forums
79
Sharpening Your Skills for Competitions
95
Competition Tasks and Metrics
97
Designing Good Validation
149
Modeling for Tabular Competitions
195
Modeling for Computer Vision
335
Modeling for NLP
389
Simulation and Optimization Competitions
425
Leveraging Competitions for Your Career
445
Creating Your Portfolio of Projects and Ideas
447
Finding New Professional Opportunities
469
Other Books You May Enjoy
489
Index
495

Hyperparameter Optimization
241
Ensembling with Blending and Stacking Solutions
303
Hak Cipta

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Data Analysis and Machine Learning with Kaggle: How to Win Competitions on ...
Konrad Banachewicz,Luca Massaron
Pratinjau tidak tersedia - 2022

Istilah dan frasa umum

adversarial validation algorithm autoencoders average Bayesian optimization build categorical chapter classification competition platforms computer vision create cross-validation data analysis data science competition data scientists deep learning encoding ensembling evaluation metric example experience exploratory data analysis F1 score feature engineering Figure fold GitHub Google gradient boosting Grandmaster ground truth https://www hyperparameters idea import inexperienced Kagglers insights instance iterations Kaggle competitions Kaggle Datasets Kaggle helped Kaggle Notebooks Kagglers often overlook labels layers leakage LightGBM machine learning multiple Netflix neural networks numpy NVIDIA objective function output overfitting parameters participants performance pipeline problem public leaderboard random forests random seed rankings regression ROC-AUC sample Scikit-learn score solution specialty on Kaggle specific split stacking started submission tabular competitions tabular data target task TensorFlow test data test set topics training data training set validation strategy values variable What’s XGBoost

Tentang pengarang (2022)

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.

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.

Informasi bibliografi

JudulThe Kaggle Book: Data analysis and machine learning for competitive data science
PengarangKonrad Banachewicz, Luca Massaron
KontributorAnthony Goldbloom
PenerbitPackt Publishing Ltd, 2022
ISBN1801812217, 9781801812214
Tebal534 halaman
  
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