What every engineer should know about data-driven analytics
著者
書誌事項
What every engineer should know about data-driven analytics
（What every engineer should know）
CRC Press, 2023
- : pbk
大学図書館所蔵 件 / 全1件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and index
内容説明・目次
内容説明
* Utilizes case studies from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision making
* Introduces various approaches to build models that exploits different algorithms
* Discusses predictive models that can be built through machine learning and used to mine patterns from large datasets
* Explores the augmentation of technical and mathematical materials with explanatory worked examples
* Includes a glossary, lecture notes, self-assessments, and worked-out practice exercises
目次
1. Data Collection and Cleaning. 2. Mathematical Background for Predictive Analytics. 3. Introduction to Statistics, Probability, and Information Theory for Analytics. 4. Introduction to Machine Learning. 5. Unsupervised Learning. 6. Supervised Learning. 7. Natural Language Processing for Analyzing Unstructured Data. 8. Predictive Analytics Using Deep Neural Networks. 9. Convolutional Neural Networks (CNN) for Predictive Analytics. 10. Recurrent Neural Networks (RNNs) for Predictive Analytics. 11. Recommender Systems for Predictive Analytics. 12. Architecting Big Data Analytical Pipeline.
「Nielsen BookData」 より