Certified Artificial Intelligence Specialist
 /  Certified Artificial Intelligence Specialist

A Certified Artificial Intelligence Specialist understands how AI technologies, mechanisms and techniques can be utilized to design business automation solutions with unprecedented functionality and value. In addition to a demonstrated proficiency of AI principles and approaches, the Certified Artificial Intelligence Specialist has comprehensive knowledge of artificial neural networks and associated layers and components, as well as continuous and reinforcement learning. A Certified Artificial Intelligence Specialist further has an understanding of how AI relates to and can be utilized together with business intelligence, deep learning and machine learning.

Module 1: Fundamental artificial intelligence

This course covers foundational AI topics and concepts and provides an understanding of essential AI techniques, the basics of neural networks and fundamental neural network architectural layers.
The primary topics covered by this course are:
• A Brief History of AI and Synthetic Intelligence
• AI in Different Forms and Shapes
• Modern AI and Cognitive AI
• The Evolution of AI and Machine Learning
• AI, Machine Learning and Deep Learning
• Artificial Intelligence and Business Intelligence
• Cybernetics and Brain Simulation
• Symbolic, Sub-Symbolic and Statistical
• Language Understanding, Learning and Adaptive Systems and Problem Solving
• Computer Visions, Pattern Recognition, Expert Systems and Robotics
• Natural Language Processing (NLP) and Speech Recognition
• Hybrid Intelligent Systems
• Key Principles of Artificial Intelligence
• Frictionless Integration and Fault Tolerance Model Integration
• Types of AI (Narrow AI, Artificial General Intelligence (AGI) and Superintelligence)
• Artificial Intelligence Techniques
• Heuristics and Support Vector Machines
• Artificial Neural Networks and Markov Decision Process
• Understanding Artificial Neural Networks
• Neural Network Layers (Input, Output and Hidden)
• Artificial Neural Network Components (Neurons, Connections, Weights, Propagation Functions and Learning Rules)
• Training Neural Networks and Neural Network Activation Functions

Module 2: Advanced Artificial Intelligence

This course covers important areas of AI application that further delve into the
relationships of AI with machine learning and deep learning, as well as the relationship
between reinforcement learning and artificial learning. Also provided is comprehensive
coverage of neural networks, including different neural network architectural models.
The following primary topics are covered:
• Understanding the Inter-relationships of AI, Machine Learning and Deep Learning
• Continuous Learning and Reinforcement Learning
• Building AI, General Intelligence, Reasoning and Knowledge Representation
• Motion and Manipulation, Social Intelligence and Creativity
• AI Design (Value Creation, Value Realization and Defensibility)
• AI Architecture Models and Design Patterns
• AI Mechanisms (AI Complete, AI Box, Percept, Rule-based System, etc.)
• Computational Humor, Soft Computing and Description Logic
• Understanding and Working with Neural Network Architectures
• Input/Output Cells, Key Cells and Architectural Layers
• Fundamental Neural Network Architectures (P, FF, RBF, DFF, RBM, AE, SAE, etc.)
• Recurrent Cell-based Architectures (RNN, ESN)
• Influenced by Hidden Cells (VAE, DAE)
• Memory-Influenced Architectures (LSTM, GRU)
• Parabolicity Cell-Driven Architectures (MC, BM, DBN)
• Backfed Cell-based Architecture (Hopfield Network)
• Pool and Kernel Influenced Architectures (DCN, DN, DCIGN)
• Influenced by Match Input (Generative Adversarial Networks)
• Influenced by Spiking Hidden Cells (Liquid State Machine)
• Influenced by Hidden Cells (ELM, DRN, KN, SVM, NTM, etc.)

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 help highlight areas that require further attention and
will further prove proficiency in AI, machine learning and deep learning systems and
neural network architectures, as they are applied and combined to solve
real-world problems.

This course is targeted for the following audience:

• Analytics Managers

• Business Analysts

• Information Architects

• Individuals seeking a career in Artificial Intelligence

To achieve this certification, Exam AI90.01 must be completed with a passing grade.
For more information on exam format / preparation / policies, visit

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