Industrial Machine Learning Using Artificial Intelligence as a Transformational Disruptor
Industrial Machine Learning Using Artificial Intelligence as a Transformational Disruptor by Andreas François Vermeulen, published by Apress on December 1, 2019, is a comprehensive resource that explores the industrialization of machine learning (ML). This first edition, comprising 637 pages, delves into the application of ML within data lakes across various industries, equipping data professionals with the skills necessary to navigate the evolving landscape of data engineering and data science.
Readers will find practical examples from sectors such as finance, healthcare, and transportation, focusing on concepts like supervised and unsupervised learning, reinforcement learning, and the principles of evolutionary computing. The book emphasizes the significance of understanding and generating transformational disruptors of artificial intelligence (AI) while addressing the challenges posed by the vast amounts of unstructured data in modern data lakes. This edition serves as a valuable guide for intermediate to expert-level professionals in data science, data engineering, and machine learning.
Official synopsis Publisher
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
Who This Book Is For
Intermediate to expert level professionals in the fields of data science, data engineering, machine learning, and data management
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