Fundamentals of Artificial Intelligence (CPE Course)

CPE Credit: 5 hours

Course Type: Downloaded PDF materials with online test

Price (with PDF Textbook): $50

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Course Description
Fundamentals of Artificial Intelligence provides a comprehensive, practical overview of artificial intelligence, explaining how modern AI systems perceive data, learn from examples, reason about information, and make decisions across real-world applications. It covers the full AI lifecycle, from data types and quality considerations through machine learning models, natural language processing, computer vision, and deployment challenges. It also emphasizes responsible AI practices, including fairness, transparency, security, and data privacy, equipping professionals to evaluate and apply AI thoughtfully in organizational settings.

Author: Steven Bragg

Course Number: SP1021

Learning Objectives

  • Identify the best uses for different types of neural networks.

  • Specify the steps included in the machine learning project lifecycle.

  • Describe the differences between supervised and unsupervised learning.

  • Identify the drawbacks of deep learning models.

  • Specify the different types of data used to train AI models.

  • Identify the different types of data augmentation.

  • Recall the differences between overfitting and underfitting.

  • Specify the solutions to overfitting and underfitting.

  • Describe the types of metrics that can be used to evaluate AI models.

  • Specify why practitioners prefer ensemble methods over single decision trees.

  • Specify how text vectorization works in natural language processing.

  • Recall the different types of text preprocessing steps.

  • Recall the advantages and disadvantages of word-level and character-level tokenization.

  • Specify the best uses for a convolutional neural network (CNN).

  • Identify why a CNN uses a convolutional filter to process an image.

  • Recall how the optical flow concept applies to computer vision.

  • Recall how the concept of data poisoning impacts machine learning security.

  • Specify how the use of proxies can lead to indirect discrimination.

  • Identify the reasons supporting the use of data anonymization.

  • Specify the actions that can be taken to protect user data privacy in AI development.

Level: Intermediate

Instructional Method: QAS Self-Study

NASBA Category: Computer Software and Applications

Prerequisites: None

Advance Preparation: None

Latest Review Date: December 2025

Program Registration Requirements: Click on "Purchase Course" near the top of this page to pay for and access the course. You will then be able to download the course as a PDF file, then take an on-line examination, and then download a certificate of completion if you pass the examination.

Program Refund Policy: For more information regarding administrative policies concerning complaints, refunds, and other matters, see our policies page.

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AccountingTools, Inc. is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.

The NASBA sponsor identification number for Accountingtools, Inc. is 115881.

AccountingTools is an IRS Approved Continuing Education Provider. We are compliant with the requirements for continuing education providers (as described in sections 10.6 and 10.9 of the Department of Treasury’s Circular No. 230 and in other IRS guidance, forms, and instructions). Our IRS Approved Continuing Education Provider number is 72821.