Certified Artificial Intelligence Specialist Digital Badge

Certified Artificial Intelligence Specialist

A Certified Artificial Intelligence Specialist has demonstrated proficiency in artificial intelligence (AI) approaches and algorithms, and proven skills for designing and validating AI solutions and modeling neural networks.

Artificial Intelligence Certificate

About The Certification

The Artificial Intelligence (AI) Specialist track is comprised of three courses that develop skills in AI practices and learning approaches, as well as Neural Network architectures, cell types and activation functions. The final course module consists of a series of lab exercises that require participants to apply their knowledge of the preceding courses in order to fulfill project requirements and solve real world problems. Completion of these courses as part of a virtual or on-site workshop results in each participant receiving an official Digital Certificate of Completion, as well as a Digital Training Badge from Acclaim/Credly. To achieve the Artificial Intelligence Specialist Certification, Exam AI90.01 must be completed with a passing grade.

A Certified Artificial Intelligence Specialist understands how AI practices can be utilized to perform data analysis and autonomous data processing with unprecedented functionality and business value. In addition to a demonstrated proficiency of AI learning approaches and functional designs, the Certified Artificial Intelligence Specialist has comprehensive knowledge of Neural Network architecture models, associated layers and neuron cell types.

Buy a single module for $99 USD OR buy the certification training bundle and get a 20% discount of the total training cost if bought individually. Exam Costs are not included.

Certification Exams need to be purchased separately.

Artificial Intelligence Training Courses

Artificial Intelligence Module 1

Module 1: Fundamentals of Artificial Intelligence

This module provides essential coverage of artificial intelligence and neural networks in easy-to-understand, plain English. The course provides concrete coverage of the primary parts of AI, including learning approaches, functional areas that AI systems are used for and a thorough introduction to neural networks, how they exist, how they work and how they can be used to process information. The course establishes the five primary business requirements AI systems and neural networks are used for, and then maps individual practices, learning approaches, functionalities and neural network types to these business categories and to each other, so that there is a clear understanding of the purpose and role of each topic covered. The course further establishes a step-by-step process for assembling an AI system, thereby illustrating how and when different practices and components of AI systems with neural networks need to be defined and applied. Finally, the course provides a set of key principles and best practices for AI projects.

Artificial Intelligence Module 2

Module 2: Advanced Artificial Intelligence

This module covers a series of practices for preparing and working with data for training and running contemporary AI systems and neural networks. It further provides techniques for designing and optimizing neural networks, including approaches for measuring and tuning neural network model performance. The practices and techniques are documented as design patterns that can be applied individually or in different combinations to address a range of common AI system problems and requirements. The patterns are further mapped to the learning approaches, functional areas and neural network types that were introduced in Module 1: Fundamental Artificial Intelligence.

Artificial Intelligence Module 3

Module 3: Artificial Intelligence Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will further improve proficiency in AI systems, neural network architectures and related learning and functional practices and patterns, as they are applied and combined to solve a series of real-world problems.