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What is Machine Learning?

By   /  November 13, 2017  /  No Comments

Machine LearningMachine Learning (ML) “…explores the construction and study of learning algorithms.” (DAMA DMBOK).

Furthermore, Machine Learning:

“…is about building programs with adaptable parameters that automatically adjust based on the data the programs receive. By adapting to previously seen data, the programs are able to improve their behavior. They also generalize data, meaning that the programs can perform functions on previously unseen datasets.” (Alejandro Correa Bahnsen)

According to Keith D. Foote:

“Machine Learning, at its most basic, is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is ‘trained’ using large amounts of data and algorithms that give it the ability to learn how to perform the task.”

Machine Learning combines the following (DMBOK):

  • Supervised learning: Based on generalized rules; for example, separating SPAM from non-SPAM email.
  • Unsupervised learning: Based on identifying hidden patterns (i.e., data mining).
  • Reinforcement learning: Based on achieving a goal (e.g., beating an opponent at chess).

Machine Learning also includes programming machines to quickly learn from queries and adapt to changing data sets.

Other definitions of Machine Learning include:

  • Advanced algorithms “composed of many technologies (such as deep learning, neural networks and natural-language processing, guided by lessons from existing information).” (Gartner IT Glossary)
  • “Systems that update their knowledge base as a result of experience with data.” (Adrian Bowles)”
  • “A method of data analysis that automates analytical model building. “ (SAS)
  • Algorithms that have built-in smarts to use available data to answer questions. (Paramita Ghosh)
  • Teaching computers to learn the same way we do, by interpreting data from the world around us, classifying it and learning from its successes and failures. (Forbes)
  • A subfield of Artificial Intelligence or AI where machines take data and “learn” for themselves. (TechRepublic)
  • The use of Artificial Intelligence (AI) for digital transformation. (Harvard Business Review)

Businesses use Machine Learning to:

  • “Reduce time to answer dramatically and guide organizational insights“ (DMBOK)
  • Deal with threats or security issues with computer systems
  • Forecast from or perform Predictive Analytics (McKinsey)
  • Catch up to the growth of data volumes (ComputerWorld)
  • Automate the trickier aspects of developing AI algorithms (MIT Technology Review)
  • Expand top-line growth while improving employee engagement and increasing customer satisfaction (Harvard Business Review)
  • Improve efficiency and speed of programming (James Kobielus)
  • Enhance and develop numerous different use cases across multiple industries (Paramita Ghosh)

 

Photo Credit: AlexLMX /Shutterstock.com

About the author

Michelle Knight enjoys putting her information specialist background to use by writing technical articles on enhancing Data Quality, lending to useful information. Michelle has written articles on W3C validator for SiteProNews, SEO competitive analysis for the SLA (Special Libraries Association), Search Engine alternatives to Google, for the Business Information Alert, and Introductions on the Semantic Web, HTML 5, and Agile, Seabourne INC LLC, through AboutUs.com. She has worked as a software tester, a researcher, and a librarian. She has over five years of experience, contracting as a quality assurance engineer at a variety of organizations including Intel, Cigna, and Umpqua Bank. During that time Michelle used HTML, XML, and SQL to verify software behavior through databases Michelle graduated, from Simmons College, with a Masters in Library and Information with an Outstanding Information Science Student Award from the ASIST (The American Society for Information Science and Technology) and has a Bachelor of Arts in Psychology from Smith College. Michelle has a talent for digging into data, a natural eye for detail, and an abounding curiosity about finding and using data effectively.

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