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

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.

The Kaggle Book: Master data science competitions with machine learning, GenAI, and LLMs: 9781835083208: Medicine & Health Science Books @ Amazon.com

Source: https://www.amazon.com/Kaggle-Book-science-competitions-Learning/dp/183508320X?&linkCode=sl1&tag=kirkdborne-20&linkId=58968b4590824e08893e673e02142ce8&language=en_US&ref_=as_li_ss_tl Stay one step ahead of your competitors with proven tips, strategies, and insights from over 30 Kaggle Masters and Grandmasters and become a better data scientist.

This new edition features updated content and new chapters on Kaggle Models, time series, and Generative AI competitions.

Key Features

• Learn how Kaggle works to make the most of every competition with winning strategies from 30+ expert Kagglers
• Sharpen your modeling skills with feature engineering, adversarial validation, gradient boosting, tabular deep learning, ensembling, and AutoML
• Master data handling techniques for smarter modeling and parameter tuning
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Kaggle has become the proving ground for millions of data enthusiasts worldwide, offering what no classroom tutorial can match: battle-tested skills built through real-world challenges and the hands-on experience that employers seek. Every competition sharpens your data analysis skills, expands your network within the data scientist community, and gives compelling proof of expertise to unlock career opportunities.

The first book of its kind, The Kaggle Book brings together everything you need to excel in competitions, data science projects, and beyond. This new edition includes fresh content and new chapters on Kaggle Models, time series, and Generative AI competitions, with three Kaggle Grandmasters guiding you through modeling strategies and sharing hard-earned insights accumulated over years of competition.

The book extends far past competition tactics, revealing techniques for tackling image, tabular, and textual data as well as reinforcement learning tasks. You’ll also discover tips for designing better validation schemes and working confidently with both standard and unconventional evaluation metrics.

Whether you want to climb the Kaggle leaderboard, accelerate your data science career, or improve the accuracy of your models, this book is for you.

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What you will learn

• Get acquainted with Kaggle as a competition platform
• Make the most of Kaggle Notebooks, Datasets, Models and Discussion forums
• Build a compelling portfolio of projects and ideas to advance your career
• Understand binary and multi-class classification, as well as object detection
• Approach NLP and time series problems with greater efficiency
• Design k-fold and probabilistic validation schemes and experiment with multiple approaches
• Get to grips with common and never-before-seen evaluation metrics
• Handle simulation, optimization, and the new Generative AI competitions on Kaggle

Who this book is for

This book is for anyone interested in Kaggle, whether you’re just starting out, a veteran user, or somewhere in between. Data analysts and data scientists looking to improve their performance in Kaggle competitions and improve their job prospects with tech giants will find this book useful.

A basic understanding of machine learning concepts will help you get the most out of this book.

Table of Contents

1. Introducing Kaggle and Other Data Science Competitions
2. Organizing Data with Datasets
3. Working and Learning with Kaggle Notebooks
4. Kaggle Models
5. Leveraging Discussion Forums
6. Competition Tasks and Metrics
7. Designing Good Validation
8. Modeling for Tabular Competitions
9. Hyperparameter Optimization
10. Ensembling with Blending and Stacking Solutions
11. Modeling for Computer Vision
12. Modeling for NLP

(N.B. Please use the Read Sample option to see further chapters)