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

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.

在超過30位Kaggle大師和特級大師的指導下，提前一步超越競爭對手，獲得洞察力。發現在Kaggle上有效競爭和成為更好的數據科學家的技巧、訣竅和最佳實踐。

主要特點：

- 從30多位專家Kaggler那裡學習Kaggle的運作方式以及如何充分利用比賽
- 通過集成、特徵工程、對抗驗證和AutoML來提高建模技能
- 簡明扼要地介紹了建模和參數調整的智能數據處理技術

書籍描述：

來自世界各地的數據愛好者在Kaggle上進行競爭，這是最著名的數據科學競賽平台。參加Kaggle競賽是提高數據分析技能、與一個驚人的數據科學家社區建立聯繫以及獲得寶貴經驗以幫助您發展職業生涯的可靠途徑。

作為首本此類書籍，《Kaggle Book》將您在競賽、數據科學項目以及其他方面取得成功所需的技巧和技能集結在一起。兩位Kaggle特級大師將帶您深入了解建模策略，這些策略在其他地方很難找到，以及他們在這一過程中積累的知識。除了Kaggle特定的技巧，您還將學習更通用的方法，以應對基於圖像、表格、文本數據和強化學習的任務。您將設計更好的驗證方案，並更舒適地使用不同的評估指標。

無論您是想在Kaggle上爬升排名，增加一些數據科學技能，還是提高現有模型的準確性，本書都適合您。

您將學到什麼：

- 熟悉Kaggle作為競賽平台
- 充分利用Kaggle筆記本、數據集和討論論壇
- 創建一個項目和想法的作品集，以在職業生涯中更上一層樓
- 設計k-fold和概率驗證方案
- 掌握常見和從未見過的評估指標
- 理解二元和多類別分類以及物體檢測
- 更有效地處理NLP和時間序列任務
- 在Kaggle上處理模擬和優化競賽

本書適合對Kaggle新手、老手和中間人士。數據分析師/科學家試圖在Kaggle競賽中表現更好並與科技巨頭獲得工作的人會發現本書很有用。

對機器學習概念的基本理解將幫助您充分利用本書。

1. Introducing Kaggle and Other Data Science Competitions
2. Organizing Data with Datasets
3. Working and Learning with Kaggle Notebooks
4. Leveraging Discussion Forums
5. Competition Tasks and Metrics
6. Designing Good Validation
7. Modeling for Tabular Competitions
8. Hyperparameter Optimization
9. Ensembling with Blending and Stacking Solutions
10. Modeling for Computer Vision
11. Modeling for NLP
12. Simulation and Optimization Competitions
13. Creating Your Portfolio of Projects and Ideas
14. Finding New Professional Opportunities

1. 介紹 Kaggle 和其他數據科學競賽
2. 使用數據集組織數據
3. 使用 Kaggle Notebooks 進行工作和學習
4. 利用討論論壇
5. 競賽任務和評估指標
6. 設計良好的驗證方法
7. 面向表格競賽的建模
8. 超參數優化
9. 使用混合和堆疊解決方案進行集成
10. 面向計算機視覺的建模
11. 面向自然語言處理的建模
12. 模擬和優化競賽
13. 創建您的項目和想法組合
14. 尋找新的職業機會