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Automated machine learning for business / Kai R. Larsen and Daniel S. Becker.

By: Contributor(s): Material type: TextTextPublication details: New York: Oxford University Press, 2021.Description: xvii, 328 p.: illustrationsISBN:
  • 9780190941666
Subject(s): DDC classification:
  • 006.31 LAR/A 
Summary: "In this book, we teach the machine learning process using a new development in data science; automated machine learning. AutoML, when implemented properly, makes machine learning accessible to most people because it removes the need for years of experience in the most arcane aspects of data science, such as the math, statistics, and computer science skills required to become a top contender in traditional machine learning. Anyone trained in the use of AutoML can use it to test their ideas and support the quality of those ideas during presentations to management and stakeholder groups. Because the requisite investment is one semester-long undergraduate course rather than a year in a graduate program, these tools will likely become a core component of undergraduate programs, and over time, even the high-school curriculum"--
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Holdings
Item type Current library Home library Call number Status Date due Barcode
Book Book Institute of Management General Stacks Institute of Management 006.31 LAR/A (Browse shelf(Opens below)) Available IMK16927

Includes bibliographical references (pages 315-317) and index.

"In this book, we teach the machine learning process using a new development in data science; automated machine learning. AutoML, when implemented properly, makes machine learning accessible to most people because it removes the need for years of experience in the most arcane aspects of data science, such as the math, statistics, and computer science skills required to become a top contender in traditional machine learning. Anyone trained in the use of AutoML can use it to test their ideas and support the quality of those ideas during presentations to management and stakeholder groups. Because the requisite investment is one semester-long undergraduate course rather than a year in a graduate program, these tools will likely become a core component of undergraduate programs, and over time, even the high-school curriculum"--

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