AI Engineering Course (Pre-Order)

The AI Engineering course consists of five modules that cover a range of fundamental and advanced AI engineering topics, including a neural network design, data preprocessing and feature engineering, model evaluation, validation and scaling, as well as predictive and generative AI models, explainability techniques and transfer learning.

Each module has a set of lessons and is further supplemented with exercises and practice questions to help reinforce your understanding of key topics. Completing this course also prepares you for the official certification exam, as explained on the AI Engineering Certification page.

Further resources are available to assist you with the completion of this course and preparation for the certification exam. These include downloadable digital course PDFs, printed course materials that can be shipped to your location, as well as coaching and instructor-led training services available by Arcitura and its training partner network. You can purchase coaching time on an hourly basis and instructor-led training workshops are available for individuals and groups.

This course is available via two separate eLearning platforms, each of which has different features and benefits. Upon clicking the Enroll button, you will be able to choose the eLearning option that works best for you.

Contact [email protected] with any questions.

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Workbook Lessons

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Video Lessons

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Reference Posters

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Interactive Exercises

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Graded Self-Test

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Completion Certificate

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Course Modules

MODULE 01 | 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.

MODULE 04 | 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.

MODULE 07 | 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.

MODULE 08 | 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.

MODULE 09 | AI Engineering Lab
This course module provides a series of case-study driven, lab-style exercises and problems that are designed to test their ability to apply their 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.