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
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, 3Dprinting, 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
