Industrial Machine Learning: Using Artificial Intelligence as a Transformational Disruptor
Authors: Andreas François Vermeulen
ISBN-10: 1484253159
ISBN-13: 9781484253151
Edition 版次: 1st ed.
Publication Date 出版日期: 2019-12-01
Print Length 页数: 637 pages
Book Description
By finelybook
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.
What You Will Learn
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
1. Introduction
2. Background Knowledge
3. Classic Machine Learning
4. Supervised Learning: Using Labeled Data for Insights
5. Supervised Learning: Advanced Algorithms
6. Unsupervised Learning: Using Unlabeled Data
7. Unsupervised Learning: Neural Network Toolkits
8. Unsupervised Learning: Deep Learning
9. Reinforcement Learning: Using Newly Gained Knowledge for Insights
10. Evolutionary Computing
11. Mechatronics: Making Different Sciences Work as One
12. Robotics Revolution
13. Fourth Industrial Revolution(4lR)
14. Industrialized Artificial Intelligence
15. Final Industrialization Project
