Cloud Computing Program

This course 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.

Provides insight into how predictive AI systems work by exploring common techniques for learning, data processing and manipulation, and AI system performance management. The course module does not cover any mathematical formulas or programming and is intended for general IT professionals.

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.

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.

Covers a range of common generative AI networks, models and techniques, including specialized neural networks and practices for managing and optimizing generative AI systems and model training processes. The course module does not cover any mathematical formulas or programming and is intended for general IT professionals.

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.

Delves into a range of predictive AI engineering practices and techniques, and further provides a detailed introduction of neural network architecture components. The course module illustrates 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 carrying out AI engineering techniques.

Covers a series of practices for preparing and working with data for training and running contemporary predictive 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 predictive AI system problems and requirements.

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.

Provides in-depth coverage of essential engineering practices for training and operating generative AI systems, including various data processing, filtering and management techniques specific to creative content generation. The module further covers commonly related topics, such as natural language processing (NLP), transfer learning and the use of pre-trained models

Covers a series of techniques for working with GANs, VAEs, diffusion models, autoregressive models, transforms, as well as transfer learning and reinforcement learning. Topics include managing large datasets, managing iterative model training cycles, fostering creative content output and working with different data formats for content generation purposes. The techniques can be applied individually or in different combinations to address a range of common generative AI system problems and requirements.

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.

Provides an essential understanding of AI system and solution architecture. This course module explains the different AI system architecture types, scopes and modes and provides detailed coverage of core AI system modules (including data ingestion, data preprocessing, feature engineering, inference engine and model repository) and monitors (including operations, data, model and ancillary monitors).

Explores a range of techniques and complex topics dedicated to AI system design and technology architecture, including scalability models, performance and optimization techniques and resiliency architectures.

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.

Establishes the core concepts behind intelligent AI agents that can perceive, reason and act autonomously. Key components of agentic systems are covered, including agentic system components, as well as agent types (such as reactive, deliberative and hybrid). The module also explains essential concepts like environments, sensors, actuators and the agent-environment interaction loop, along with the basics of knowledge representation for agents and how agents plan and make decisions at a high level.

Delves into the complexities and applications of agentic AI by exploring techniques for agent collaboration and coordination, as well as methods for enhancing agent reasoning and decision-making. The module examines planning algorithms, reinforcement learning for policy optimization and techniques for handling uncertainty, and further explains real-world applications of agentic AI across various domains, including robotics, autonomous systems, complex problem-solving and creative tasks. Key challenges are also tackled, including scalability, robustness, interpretability and ethical considerations.

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.