- 中图分类号: TP3
- 语种: ENG
- 出版信息: Packt Publishing 2023 173页
- EISBN: 9781804610114
- PISBN-P: 9781804611210
- 原文访问地址:
KG评星
知识图谱评星,是一种基于用户使用的评价体系,综合图书的评论数量、引文数量、Amazon评分以及图谱网络中节点的PageRank值(即考虑相邻节点数量和重要性)等多种因素计算而得出的评价数值。星级越高,推荐值越高。CAT核心级
核心学术资源(CAR)项目作为教图公司推出的一项知识型服务,旨在打造一套科学、有效的图书评价体系,并协助用户制定相应的馆藏建设方案。CAR项目调查和分析12所世界一流大学的藏书数据,以收藏学校的数量确定书目的核心级,核心级越高,代表书目的馆藏价值越高。选取核心级在三级以上,即三校以上共藏的图书作为核心书目(CAT)。Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features: * Challenge yourself to start thinking like a Kaggle Grandmaster * Fill your portfolio with impressive case studies that will come in handy during interviews * Packed with exercises and notes pages for you to enhance your skills and record key findings Book Description: More than 80,000 Kaggle novices currently participate in Kaggle competitions. To help them navigate the often-overwhelming world of Kaggle, two Grandmasters put their heads together to write The Kaggle Book, which made plenty of waves in the community. Now, they’ve come back with an even more practical approach based on hands-on exercises that can help you start thinking like an experienced data scientist. In this book, you’ll get up close and personal with four extensive case studies based on past Kaggle competitions. You’ll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. Get into computer vision by discovering different solutions for identifying the type of disease present on cassava leaves. And see how the Kaggle community created predictive algorithms to solve the natural language processing problem of subjective question-answering. You can use this workbook as a supplement alongside The Kaggle Book or on its own alongside resources available on the Kaggle website and other online communities. Whatever path you choose, this workbook will help make you a formidable Kaggle competitor. What you will learn: * Take your modeling to the next level by analyzing different case studies * Boost your data science skillset with a curated selection of exercises * Combine different methods to create better solutions * Get a deeper insight into NLP and how it can help you solve unlikely challenges * Sharpen your knowledge of time-series forecasting * Challenge yourself to become a better data scientist Who this book is for: If you’re new to Kaggle and want to sink your teeth into practical exercises, start with The Kaggle Book, first. A basic understanding of the Kaggle platform, along with knowledge of machine learning and data science is a prerequisite. This book is suitable for anyone starting their Kaggle journey or veterans trying to get better at it. Data analysts/scientists who want to do better in Kaggle competitions and secure jobs with tech giants will find this book helpful.