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Demystifying Artificial Intelligence
CPE Self-study

Demystifying Artificial Intelligence

A practical overview of artificial intelligence, machine learning (ML), and deep learning (DL).

$115 - $145
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NASBA Field of Study

Information Technology



CPE Credits



Vadas Gintautas


1 year

Product Number


 Business & partner 
Product Details

Time to demystify artificial intelligence.

The term AI has been in the news for decades, inspiring a mystique that has rarely lived up to the expectations . . . until now!

AI now offers the potential to revolutionize the way we do business − from reinventing customer experiences to predictive forecasting to prescriptive marketing approaches.

A practical understanding

Demystifying Artificial Intelligence provides a practical overview of artificial intelligence, machine learning (ML), and deep learning (DL).

In this CPE self study course, you will be introduced to key concepts and capabilities, and the fundamental knowledge needed to understand the AI revolution from a business perspective.

Real-world examples and use cases will be used to explain and illustrate concepts throughout this course

Key Topics
  • Differences between AI, ML and DL
  • Benefits of artificial intelligence
  • AI applications and capabilities and how AI drives a competitive advantage.
  • Explanation of the importance of data in AI applications
  • Industry drivers and real-world examples of AI
Learning Outcomes
  • Recall basic concepts related to AI.
  • Recognize the importance of AI.
  • Identify current practical applications of AI (including use cases in audit, accounting and finance).
  • Recognize AI capabilities.
  • Recognize the importance of data in AI applications
Credit Info
CPE Credits
NASBA Field of Study
Information Technology
This is a digital product. With full paid access the content will be available to you for 1 year after purchase date.
For more information, please refer to CPE requirements and NASBA sponsorship information
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Contact us
Vadas Gintautas
Vadas Gintautas completed a Ph.D. in Physics from the University of Illinois, specializing in complex systems and nonlinear dynamics. His postdoctoral work was at Los Alamos National Laboratory, working on biologically inspired neural network models using Roadrunner, which was the most powerful supercomputer in the world at the time. After working as a physics faculty member at Chatham University for 4 years, he switched to a career in industry, at BAE Systems FAST Labs as a research scientist. He currently leads the Emerging Machine Learning Analytics section, working on topics that span information theory, machine learning, and artificial intelligence.

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