What Every Engineer Should Know About Data Driven Analytics
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What Every Engineer Should Know About Data-Driven Analytics
Author: Satish Mahadevan Srinivasan
language: en
Publisher: CRC Press
Release Date: 2023-04-13
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.
What Every Engineer Should Know About Data-Driven Analytics
Author: Satish Mahadevan Srinivasan
language: en
Publisher: CRC Press
Release Date: 2023-04-13
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.
What Every Engineer Should Know About Python
Engineers across all disciplines can benefit from learning Python. This powerful programming language enables engineers to enhance their skill sets and perform more sophisticated work in less time, whether in engineering analysis, system design and development, integration and testing, machine learning and other artificial intelligence applications, project management, or other areas. What Every Engineer Should Know About Python offers students and practicing engineers a straightforward and practical introduction to Python for technical programming and broader uses to enhance productivity. It focuses on the core features of Python most relevant to engineering tasks, avoids computer science jargon, and emphasizes writing useful software while effectively leveraging generative AI. Features examples tied to real-world engineering scenarios that are easily adapted Explains how to leverage the vast ecosystem of open-source Python packages for scientific applications, rather than developing new software from scratch Covers the incorporation of Python into engineering designs and systems, whether web-based, desktop, or embedded Provides guidance on optimizing generative AI with Python, including case study examples Describes software tool environments and development practices for the rapid creation of high-quality software Demonstrates how Python can improve personal and organizational productivity through workflow automation Directs readers to further resources for exploring advanced Python features This practical and concise book serves as a self-contained introduction for engineers and readers from scientific disciplines who are new to programming or to Python.