The Certified AI Specialist course provides IT professionals with fundamental knowledge in artificial intelligence. The course offers concrete coverage of the primary parts of AI including learning approaches and the functional areas that AI systems are used for. There is also 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. 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. Finally, participants are presented with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Because the course is positioned as vendor-neutral, the course materials and skills proven by passing the Artificial Intelligence Specialist Certification Exam (AI90.01) are applicable to any vendor.
Completion of the Certified AI Specialist course and passing the Artificial Intelligence Specialist Certification Exam (AI90.01) proves proficiency in artificial intelligence practices and technologies, as they are utilized and provide real-world business solutions.
The course includes a 12-month subscription to the following digital course materials:
- Video Lessons (modules 1 & 2)
- Workbook (modules 1, 2 & 3)
- Exam Preparation Guide with Sample Questions (modules 1, 2 & 3)
- Neural Networks Supplement
- Algorithms and Practices Supplement
- Mind Map Poster (modules 1, 2 & 3)
- Neural Networks and Neuron Types Mapping Poster
- Problem Types and Neural Networks Mapping Poster
- Neural Networks and Practices Mapping Poster
- Problem Types and Practices Mapping Poster
- Symbol Legend Poster
- Flashcards with Sample Questions (module 1, 2 & 3)
Arcitura Certified AI Specialist Course Outline
Module 1: Fundamental Artificial Intelligence
- 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 designs
- 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, convolution, 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)
Module 2: Advanced Artificial Intelligence
- 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 modelling 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 deepening a neural network in synch with current data
- Transfer learning for accelerating neural network training
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.
Who Should Take the Course
- Junior AI Engineer
- Junior AI Developer
- AI Architect
- Data Scientist
- Data Analyst
