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What every engineer should know about data-driven analytics /
What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of machine learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press,
2023.
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| Edición: | 1st. |
| Colección: | What Every Engineer Should Know
ISSN |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of machine learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood by every engineering discipline. It offers a detailed and focused treatment of the important machine learning approaches and concepts that can be exploited to build models to enable decision making in different domains. Utilizes practical examples 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, self-assessments, and worked-out practice exercises Written to be accessible to non-experts in the subject, this comprehensive introductory text is suitable for students, professionals, and researchers in engineering and data science. |
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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 (260 pages) : illustrations (black and white). |
| Bibliografía: | Includes bibliographical references and index. |
| ISBN: | 9781000859720 100085972X 9781000859690 100085969X 9781003278177 1003278175 |