Industrial Machine Learning [electronic resource] : Using Artificial Intelligence as a Transformational Disruptor / by Andreas François Vermeulen.

Vermeulen, Andreas François. author., Author,
1st ed. 2020.
Berkeley, CA : Apress : Imprint: Apress, 2020.
1 online resource (652 pages)
Artificial intelligence.
Big data.
Local subjects:
Artificial Intelligence. (search)
Big Data. (search)
System Details:
text file
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.
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
Publisher Number:
10.1007/978-1-4842-5316-8 doi
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