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Industrial machine learning = using ...

Vermeulen, Andreas Francois.

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Industrial machine learning = using artificial intelligence as a transformational disruptor /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Industrial machine learning/ by Andreas Francois Vermeulen.
Reminder of title:	using artificial intelligence as a transformational disruptor /
Author:	Vermeulen, Andreas Francois.
Published:	Berkeley, CA :Apress : : 2020.,
Description:	xxiii, 637 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction -- Chapter 2: Background Knowledge -- Chapter 3: Classic Machine Learning -- Chapter 4: Supervised Learning: Using labeled data for Insights -- Chapter 5: Supervised Learning: Advanced Algorithms -- Chapter 6: Unsupervised Learning: Using Unlabeled Data -- Chapter 7: Unsupervised Learning: Neural Network Toolkits -- Chapter 8: Unsupervised Learning: Deep Learning -- Chapter 9: Reinforcement Learning: Using Newly Gained Knowledge for Insights -- Chapter 10: Evolutionary Computing -- Chapter 11: Mechatronics -- Chapter 12: Robotics Revolution -- Chapter 13: Fourth Industrial Revolution (4IR ) -- Chapter 14: Industrialized Artificial Intelligence -- Chapter 15: Final Industrialization Project -- Appendix: Reference Material.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-5316-8
ISBN:	9781484253168

Industrial machine learning = using artificial intelligence as a transformational disruptor /
Vermeulen, Andreas Francois.

Industrial machine learningusing artificial intelligence as a transformational disruptor /[electronic resource] :by Andreas Francois Vermeulen. - Berkeley, CA :Apress :2020. - xxiii, 637 p. :ill., digital ;24 cm.

Chapter 1: Introduction -- Chapter 2: Background Knowledge -- Chapter 3: Classic Machine Learning -- Chapter 4: Supervised Learning: Using labeled data for Insights -- Chapter 5: Supervised Learning: Advanced Algorithms -- Chapter 6: Unsupervised Learning: Using Unlabeled Data -- Chapter 7: Unsupervised Learning: Neural Network Toolkits -- Chapter 8: Unsupervised Learning: Deep Learning -- Chapter 9: Reinforcement Learning: Using Newly Gained Knowledge for Insights -- Chapter 10: Evolutionary Computing -- Chapter 11: Mechatronics -- Chapter 12: Robotics Revolution -- Chapter 13: Fourth Industrial Revolution (4IR ) -- Chapter 14: Industrialized Artificial Intelligence -- Chapter 15: Final Industrialization Project -- Appendix: Reference Material.

Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI) You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science. Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakes. Industrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory, supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptors. You will: Generate and identify transformational disruptors of artificial intelligence (AI) Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environment Hone the skills required to handle the future of data engineering and data science.

ISBN: 9781484253168

Standard No.: 10.1007/978-1-4842-5316-8doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .V476 2020

Dewey Class. No.: 006.31

Industrial machine learning = using artificial intelligence as a transformational disruptor /
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505 0 $a Chapter 1: Introduction -- Chapter 2: Background Knowledge -- Chapter 3: Classic Machine Learning -- Chapter 4: Supervised Learning: Using labeled data for Insights -- Chapter 5: Supervised Learning: Advanced Algorithms -- Chapter 6: Unsupervised Learning: Using Unlabeled Data -- Chapter 7: Unsupervised Learning: Neural Network Toolkits -- Chapter 8: Unsupervised Learning: Deep Learning -- Chapter 9: Reinforcement Learning: Using Newly Gained Knowledge for Insights -- Chapter 10: Evolutionary Computing -- Chapter 11: Mechatronics -- Chapter 12: Robotics Revolution -- Chapter 13: Fourth Industrial Revolution (4IR ) -- Chapter 14: Industrialized Artificial Intelligence -- Chapter 15: Final Industrialization Project -- Appendix: Reference Material.
520 $a Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI) You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science. Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakes. Industrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory, supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptors. You will: Generate and identify transformational disruptors of artificial intelligence (AI) Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environment Hone the skills required to handle the future of data engineering and data science.
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