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Book details
File Size: 22 MB
Format: epub
Print Length: 637 pages
Publisher: Apress; 1st ed. edition (November 30, 2019)
Publication Date: November 30, 2019
Sold by: Amazon.com Services LLC
Language: English
ASIN: B0827F36S6

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.

Benefits

Provides the link between theory and practical, real-world machine learning implementations at an industrialized level
Supplies a series of advanced practical examples of the autonomous transformational disruptors you can generate with machine learning and use as case studies for your future projects
Prepares you to gain an advantage from an unfavorable shift in the employment market as autonomous transformational disruptors unavoidably are altering the future job market
Gives you the information you need to improve your skills to transform data into business insights and to successfully survive the fourth industrial revolution
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.

Table of contents (15 chapters)

Introduction
Background Knowledge
Classic Machine Learning
Supervised Learning: Using Labeled Data for Insights
Supervised Learning: Advanced Algorithms
Unsupervised Learning: Using Unlabeled Data
Unsupervised Learning: Neural Network Toolkits
Unsupervised Learning: Deep Learning
Reinforcement Learning: Using Newly Gained Knowledge for Insights
Evolutionary Computing
Mechatronics: Making Different Sciences Work as One
Robotics Revolution
Fourth Industrial Revolution (4IR)
Industrialized Artificial Intelligence
Final Industrialization Project