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Note: This new course is coming soon and is currently available for pre-order.

Complete the AI Engineering course and, optionally, get accredited as a Certified AI Engineer by passing the certification exam. You can purchase the course now and get the exam later, or you can get them together at a discount as part of the Certification Bundle.

Upon completing the course you will receive a digital certificate of completion, as well as a digital training badge from Acclaim/Credly. Because this course encompasses both the AI Professional and AI Engineer certifications, upon passing the exam you will also receive official AI Professional and AI Engineer digital accreditation certificates and certification badges from Acclaim/Credly, along with an account that can be used to verify your certification status.

If you already completed the AI Professional course modules, you can purchase a partial course (or a partial bundle) with only the modules specific to the AI Engineer track here.

The AI Engineering course is comprised of the following 5 course modules, each of which has an estimated completion time of 10 hours:

  • Module 1: Fundamental Predictive AI
  • Module 4: Fundamental Generative AI
  • Module 7: Fundamental AI Engineering
  • Module 8: Advanced AI Engineering
  • Module 9: AI Engineering Lab

Choose the Certification Bundle to receive the entire course together with the online-proctored certification exam and a set of practice exam questions, all at a bundle discount.

Upon purchasing this course, you will automatically receive access via the Online Interactive eLearning platform. To provide you with the greatest flexibility, you will also have the option to access the course materials via two additional eLearning formats, at no extra cost. All three eLearning formats are briefly described below. A more detailed comparison can be found here.
  1. For everyday learning: An online interactive eLearning platform with individual lessons, as well as interactive and automatically graded exercises and practice questions.
  2. For learning on-the-go: A study kit platform with access to full course documents that support online/offline synching, annotations, comments, custom bookmarks and cross-document searches.
  3. For your reference: A set of printable PDF documents that you can keep (for all course workbooks and posters).
All three forms of access are subject to Arcitura’s *. Upon purchase, access to the online interactive eLearning platform (1) is provided within one business day. Access to the study kits (2) and the PDF documents (3) is provided upon request.

Shown below are the digital contents and the topic outline for each course module:

Module 1: Fundamental Predictive AI

This course module illustrates how predictive AI can be used and applied in a range of business applications, as well as essential coverage of predictive AI practices and systems. The module explores the most common learning approaches and functional areas that AI systems are used for. All of the content is authored in easy-to-understand, plain English.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster

  • Symbol Legend Poster
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Predictive AI Business and Technology Drivers
  • Predictive AI Benefits
  • Common Risks and Challenges of Using Predictive AI
  • Business Problem Categories Addressed by AI
  • Types of Predictive AI
  • Common Predictive AI Learning Approaches
  • Understanding Predictive AI Learning and Model Training
  • Step-by-Step Training Loop Process

  • Supervised Learning, Unsupervised Learning, Continuous Learning
  • Heuristic Learning, Semi-Supervised Learning, Reinforcement Learning
  • Common Predictive AI Functional Designs, Computer Vision, Pattern Recognition
  • Robotics, Natural Language Processing (NLP)
  • Speech Recognition, Natural Language Understanding (NLU)
  • Understanding AI Models and Neural Networks

Module 4: Fundamental Generative AI

This course module explores the application of generative AI within a range of business scenarios and provides fundamental coverage of generative AI concepts, models, best practices and neural networks, including Generative Adversarial Networks (GANs), Variational Encoders (VAEs) and Transformer models. All of the content is authored in easy-to-understand, plain English.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster

  • Symbol Legend Poster
  • Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Generative AI Business and Technology Drivers
  • Generative AI Benefits
  • Common Risks and Challenges of Using Generative AI
  • Business Problem Categories Addressed by Generative AI
  • Understanding Models, Algorithms and Neural Networks
  • Types of Generative AI

  • Training Generative Models and Understanding the Training Loop
  • Understanding Generative Adversarial Networks (GANs)
  • Understanding Variational Encoders (VAE)
  • Understanding Transformers
  • Steps to Building AI Systems
  • Generative AI Best Practices

Module 7: Fundamental AI Engineering

This course module delves into a range of AI engineering practices and techniques, and further provides a detailed introduction of neural network architecture components. The course module 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 module provides a set of key principles and best practices for AI projects.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster

  • Symbol Legend Poster
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Model Evaluation and Validation Techniques
  • Data Preprocessing Techniques, Overfitting and Regularization
  • Practical AI Ethics and Bias Mitigation
  • Optimization Techniques and Advanced Learning Algorithms
  • Imbalanced Datasets Handling Techniques
  • Natural Language Processing (NLP) with Deep Learning
  • Advanced Feature Engineering, Data Augmentation Techniques
  • Fine-Tuning Strategies, Reinforcement Learning
  • Frictionless Integration, Fault Tolerance Model Integration
  • Model Explainability and Interpretability
  • Model Deployment, Monitoring and Maintenance

  • Understanding Neural Networks and Models
  • Neural Network Types, Neurons, Layers, Links, Weights
  • 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.)
  • Neural Network Architectures for Predictive AI and Generative AI
  • How to Build an AI System (Step-by-Step)
  • Common AI System Design Principles and AI Project Best Practices

Module 8: Advanced AI Engineering

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 can be applied individually or in different combinations to address a range of common AI system problems and requirements.

Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)

  • Mind Map Poster
  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • Data Wrangling 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 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 9: AI Engineering Lab

This course module provides a series of case-study driven, lab-style exercises and problems that are designed to test your ability to apply your knowledge of topics covered in previous modules. Completing this lab helps reinforce understanding of preceding topics and further demonstrates how different practices and technologies can be applied together as part of greater solutions.

Course Module Contents

  • Lab Exercise Booklet
  • Mind Map Poster

  • Practice Exam Questions
  • PDFs of Exercise Booklet and Poster (printable)

Learn About Arcitura: Take the Video Tour

Watch these helpful informational videos to learn about Arcitura programs, courses and certifications.

About Arcitura

About Arcitura Courses

About Arcitura Certifications

What’s in an Arcitura Course


Each course provides a comprehensive curriculum with 2-8 modules and 20-80 hours of training.

More Than Just
Video Lessons

In addition to standard video lessons, courses include full-color workbooks and reference posters for all lessons.

Interactive & Graded

Courses also include interactive and graded exercises, interactive and graded self-tests and other supplements.

The Arcitura Difference


  • is authored by a dedicated courseware development team
  • has a self-test, accreditation exam and professional certification
  • is available via two different eLearning platforms


  • undergo a common development process
  • are authored to be consistent in quality, structure and style
  • share a common vocabulary and symbol notation
  • are authored in collaboration with subject matter experts

Take Your Skills Anywhere

Regardless of whether you are an individual looking to boost your career or an organization looking to up-skill a team, Arcitura courses and certifications provide a sound investment.

Because both courses and accreditations are vendor-neutral, they empower you with skills and credentials that you can take to wherever you need to go.

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