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.