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

Format:

Book

Author/Creator:

Vermeulen, Andreas François, Author.

Language:

English

Subjects (All):

Artificial intelligence.

Big data.

Artificial Intelligence.

Big Data.

Local Subjects:

Artificial Intelligence.

Big Data.

Physical Description:

1 online resource (652 pages)

Edition:

1st ed. 2020.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2020.

System Details:

text file

Summary:

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.

Contents:

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

.

Notes:

Includes bibliographical references.

ISBN:

9781523150489

1523150483

9781484253168

1484253167

OCLC:

1140553237