Satish Mahadevan Srinivasan – författare

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 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 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 makingIntroduces various approaches to build models that exploits different algorithmsDiscusses predictive models that can be built through machine learning and used to mine patterns from large datasetsExplores the augmentation of technical and mathematical materials with explanatory worked examplesIncludes a glossary, self-assessments, and worked-out practice exercisesWritten 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 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 makingIntroduces various approaches to build models that exploits different algorithmsDiscusses predictive models that can be built through machine learning and used to mine patterns from large datasetsExplores the augmentation of technical and mathematical materials with explanatory worked examplesIncludes a glossary, self-assessments, and worked-out practice exercisesWritten 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.

Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging toward the adoption of distributed open‑source computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. In addition to focusing on theory, this book shares real‑life experiences building AI and big data analytics systems of value to practitioners.Features practical case studies on building big data and AI models for large‑scale enterprise solutionsDiscusses the use of design patterns for architecting AI that are safe, secure, and testableCovers an array of concepts, including deep big data analytics, natural language processing, transformer architecture, and evolution of ChatGPT, swarm intelligence, and genetic programmingInformed by the authors’ many years of teaching ML and AI and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies.

Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging toward the adoption of distributed open‑source computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. In addition to focusing on theory, this book shares real‑life experiences building AI and big data analytics systems of value to practitioners.Features practical case studies on building big data and AI models for large‑scale enterprise solutionsDiscusses the use of design patterns for architecting AI that are safe, secure, and testableCovers an array of concepts, including deep big data analytics, natural language processing, transformer architecture, and evolution of ChatGPT, swarm intelligence, and genetic programmingInformed by the authors’ many years of teaching ML and AI and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies.

Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging toward the adoption of distributed open source computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. In addition to focusing on theory, this book shares real life experiences building AI and big data analytics systems of value to practitioners. Features practical case studies on building big data and AI models for large scale enterprise solutions Discusses the use of design patterns for architecting AI that are safe, secure, and testable Covers an array of concepts, including deep big data analytics, natural language processing, transformer architecture, and evolution of ChatGPT, swarm intelligence, and genetic programming Informed by the authors' many years of teaching ML and AI and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies.