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Workshops not currently scheduled

We are currently planning the workshops for this course. You may purchase an eLearning kit for immediate access or contact us for further details.



USD $589.47 $294.73 eLearning Kit only

Whether it’s a chat bot that is indistinguishable from a human or a self-driving car, artificial intelligence is here. We have moved on from the days when machines would just follow algorithms, we now need them to make decisions to keep up with the pace of technology and the amount of information. In this course you’ll learn all about Artificial Intelligence.

In this course you’ll learn about types of AI, AI Techniques, Neural Networks, building AI, architectural models and design patterns.

The eLearning kit bundle provides access to a 3 Module course bundle that will be available for 1 year from the date of purchase. You can also take the on-line exam to get a Digital Badge to show your expertise in this field.

  • Module 1: Fundamental Artificial Intelligence
  • Module 2: Advanced Artificial Intelligence
  • Module 3: Artificial Intelligence Lab

Further description of the contents are included in the Details tab.


 

Intended for
Enterprise Architects, Solution Architects, Security Architects, Business Analysts and any person interested in Artificial Intelligence and its technology, uses and methods.


Pre-requisites
An understanding of IT concepts


Learning Outcomes
A comprehensive understanding of artificial intelligence, its uses and applicability to business as well as understanding of techniques and concepts. The following topics are some of the ones that are covered during the course:

  • 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 
  • 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

And more…


Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.

Topics Covered 
The following topics are covered during the course 

Module 1 Fundamental Artificial Intelligence

This course 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.

The following primary topics are covered:
– 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 course 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.

The following primary topics are covered:
– Data Wrangling Patterns for Preparing Data for Neural Network Input
– Feature Encoding for Converting Categorical Features
– Feature Imputation for Inferring Feature Values
– Feature Scaling for Training Datasets with Broad Features
– Text Representation for Converting Data while Preserving Semantic and Syntactic Properties
– 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 Technology 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.


  Exams & Certification

  • You may take this exam from the comfort of your home or office via Pearson VUE Online Proctoring. 

 

Note: All quoted pricing is excluding GST. For customers with a billing address in Australia GST will be added during the check-out process.

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