The Artificial Intelligence (AI) Specialist track is comprised of three modules 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. Those who achieve this certification receive an official Digital Certificate of Excellence, as well as a Digital Certification Badge from Acclaim/Credly, with an account that supports the online verification of certification status.
Duration
3 days
Course Outline:
Module 1: Fundamental Artificial Intelligence
This module provides essential coverage of artificial intelligence and neural networks in easy-to-understand, plain English. The module 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.
In addition, this module establishes 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.
Primary topics covered are:
- AI Business and Technology Drivers
- AI Benefits and Challenges
- Business Problem Categories Addressed by AI
- AI Types (Narrow, General, Symbolic, Non-Symbolic, etc.)
- Common AI Learning Approaches and Algorithms
- Supervised Learning, Unsupervised Learning, Continuous Learning
- Heuristic Learning, Semi-Supervised Learning, Reinforcement Learning
- Common AI Functional Designsl
- Computer Vision, Pattern Recognition
- Robotics, Natural Language Processing (NLP)
- Speech Recognition, Natural Language Understanding (NLU)
- Frictionless Integration, Fault Tolerance Model Integration
- Neural Networks, Neurons, Layers, Links, Weights
- Understanding AI Models and Training Models and Neural Networks
- Understanding how Models and Neural Networks Exist
- Loss, Hyperparameters, Learning Rate, Bias, Epoch
- Activation Functions (Sigmoid, Tanh, ReLU, Leaky RelU, Softmax, Softplus)
- Neuron Cell Types (Input, Backfed, Noisy, Hidden, Probabilistic, Spiking, Recurrent, Memory, Kernel, nvolution, Pool, Output, Match Input, etc.)
- Fundamental and Specialized Neural Network Architectures
- Perceptron, Feedforward, Deep Feedforward, AutoEncoder, Recurrent, Long/Short Term Memory
- Deep Convolutional Network, Extreme Learning Machine, Deep Residual Network
- Support Vector Machine, Kohonen Network, Hopfield Network
- Generative Adversarial Network, Liquid State Machine
- How to Build an AI System (Step-by-Step)
- Common AI System Design Principles and Common AI Project Best Practices
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.
Primary topics covered are:
- Data Wrangling Patterns for Preparing Data for Neural Network Input
- Feature Encoding for Converting Categorical Features
- Feature Imputation for Inferring Feature Values
- Text Representation for Converting Data while Preserving Semantic and Syntactic Properties
- Feature Scaling for Training Datasets with Broad Features
- Dimensionality Reduction to Reduce Feature Space for Neural Network Input
- Supervised Learning Patterns for Training Neural Network Models
- Supervised Network Configuration for Establishing the Number of Neurons in Network Layers
- Image Identification for using a Convolutional Neural Network
- Sequence Identification for using a Long Short Term Memory Neural Network
- Unsupervised Learning Patterns for Training Neural Network Models
- Pattern Identification for Visually Identifying Patterns via a Self Organizing Map
- Content Filtering for Generating Recommendations
- Model Evaluation Patterns for Measuring Neural Network Performance
- Training Performance Evaluation for Assessing Neural Network Performance
- Prediction Performance Evaluation for Predicting Neural Network Performance in Production
- Baseline Modeling for Assessing and Comparing Complex Neural Networks
- Model Optimization Patterns for Refining and Adapting Neural Networks
- Overfitting Avoidance for Tuning a Neural Network
- Frequent Model Retraining for Keeping a Neural Network in Synch with Current Data
- Transfer Learning for Accelerating Neural Network Training
Module 3: Artificial Intelligence Lab
In this module, participants are to solve a series of exercises and problems that are designed to test participants’ ability to apply their knowledge of topics covered in previous modules.
Upon completion of this lab, participants 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.
The Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion.
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