What every engineer should know about data-driven analytics
What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the machine learning theoretical concepts and approaches that are used in predictive data analytics through practical applications and case studies.
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, Florida :
CRC Press
[2023]
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| Edición: | First edition |
| Colección: | What every engineer should know.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009757938406719 |
| Sumario: | What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the machine learning theoretical concepts and approaches that are used in predictive data analytics through practical applications and case studies. |
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| Notas: | 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. |
| Descripción Física: | 1 online resource (279 pages) |
| Bibliografía: | Includes bibliographical references and index. |
| ISBN: | 9781003278177 9781000859720 9781000859690 |
