| 书目名称 | Thinking Data Science |
| 副标题 | A Data Science Pract |
| 编辑 | Poornachandra Sarang |
| 视频video | |
| 概述 | Written for both aspiring and working data scientists to develop and improve their AI applications.Teaches how to handle numeric, text and image datasets, GOFAI and ANN/DNN development, and use automa |
| 丛书名称 | The Springer Series in Applied Machine Learning |
| 图书封面 |  |
| 描述 | .This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”..The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing effici |
| 出版日期 | Book 2023 |
| 关键词 | ANN; DNN; Machine Learning algorithms; Machine Learning mode selection; Naive Bayes; SVM |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-02363-7 |
| isbn_softcover | 978-3-031-02365-1 |
| isbn_ebook | 978-3-031-02363-7Series ISSN 2520-1298 Series E-ISSN 2520-1301 |
| issn_series | 2520-1298 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 17:57:19
