# The Kaggle Book, 2nd edition, is a guide to data science competitions

> Original title: 🏆The Kaggle Book — Master Data Analysis and Data Science Competitions with Machine Learning, GenAI, and LLMs [2nd Ed.]: http://amzn.to/4...

- Source: X search: AI launch posts (introducing, just launched)
- Published: 2026-11-08T00:02:45.000Z
- Bad Influence: https://badinfluence.news/items/azk2vin1blgd5erp95gp5216u
- Original: https://x.com/KirkDBorne/status/2119219031949353362

## Summary

A 2nd edition of The Kaggle Book is promoted, with a table of contents covering Kaggle notebooks, datasets, discussion forums, validation schemes, hyperparameter optimization, ensembling, and image classification and segmentation. A reviewer on Amazon calls it a roughly 700-page resource for Kaggle competitions, including about 500 pages on modeling techniques and evaluation metrics, and says it also helps with building a Kaggler portfolio.

## Post

🏆The Kaggle Book — Master Data Analysis and Data Science Competitions with Machine Learning, GenAI, and LLMs [2nd Ed.]: http://amzn.to/4pxJpTC 

Table of Contents:
🔶Introducing Data Science Competition
🔷Organizing Data with Datasets
🔶Work & Learn with Kaggle Notebooks
🔷Kaggle Models
🔶Leveraging Discussion Forums
🔷Detailing Competition Tasks & Metrics
🔶Designing Good Validation Schemes
🔷Modeling for Tabular Competitions
🔶Hyperparameter Optimization
🔷Ensembling & Stacking Solutions
🔶Modeling Image Classification & Segmentation

My Review (on Amazon):
This 700-page masterpiece of writing covers everything you need—start to finish—to be a competitive coder, specifically for Kaggle data science competitions. The book covers the mechanics of the competitions (platform, resources, rankings, leaderboards), then the infrastructure (notebooks, GitHub, data sets, frameworks, discussion forums), and then nearly 500 pages devoted to "Elevating Your Game" (in-depth coverage of modeling techniques, evaluation metrics, validation strategies, hyperparameter optimization, ensembles, stacking, and various categories of competitions: tabular data, computer vision, NLP, Gen AI, simulations). The book concludes with a valuable section on building your Kaggle portfolio for career advancement and new opportunities. This is an outstanding data science / AI / Machine Learning training resource for anyone, even if you are not into the competitions, though especially if you are a dedicated Kaggler.

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