Wiley
Core Machine Learning Practices
This unit develops practical skills for preparing data, training and evaluating core ML models, and maintaining secure, reproducible workflows. Students […]
Free
Foundations of Applied Machine Learning
This unit establishes practical foundations in machine learning by introducing core concepts, common algorithms (perceptron, decision trees), data roles, and […]
Free
Optimizing and Operationalizing LLMs
This unit covers practical techniques to optimize inference performance and operationalize large language model solutions, including hardware utilization, batch tuning, […]
Free
Small and Domain-Specific Models
This unit examines the roles, strengths, and trade-offs of compact and domain-specific language models compared to large general-purpose models. Students […]
Free
Efficient Model Inference and Tuning
This unit teaches techniques for producing concise outputs, scaling inference efficiently, and improving model performance with minimal parameter updates. Students […]
Free
Customizing and Optimizing LLMs
This unit introduces practical techniques for adapting large language models to domain-specific needs and for optimizing inference behavior. Students explore […]
Free
Advanced Creative Applications with GenAI
This culminating unit builds on foundational generative AI principles to focus on advanced creative workflows, multi-format production, and quality assurance. […]
Free
Applied Generative Content Creation
This unit focuses on applying generative AI techniques to create, refine, and evaluate long-form and multimedia content across audiences and […]
Free
Practical Generative AI Content Creation
This unit develops applied skills for using generative AI to produce, refine, and govern a range of creative and professional […]
Free
Generative AI Foundations and Prompting
This unit introduces core concepts, capabilities, and limitations of generative AI while grounding students in practical prompt strategies, creative workflows, […]
Free
Deploying Generative AI and RAG on AWS
This capstone unit covers end-to-end implementation, evaluation, and operationalization of retrieval-augmented generation (RAG) and generative AI solutions on AWS. Students […]
Free
Customizing and Integrating FMs on AWS
This unit covers Amazon Bedrock and related AWS services for deploying, customizing, and operationalizing generative AI solutions. Students will learn […]
Free
Foundations and AWS GenAI Tools
This unit introduces foundational concepts and practical AWS tools for building generative AI solutions, covering foundation model types (including LLMs), […]
Free
Foundations of Generative AI
This unit surveys the historical evolution of artificial intelligence and the core technical foundations of machine learning, deep learning, and […]
Free
Enterprise AI Monitoring and Optimization
This unit covers operationalizing, monitoring, and optimizing enterprise AI/ML systems in cloud environments, with hands-on use of monitoring templates, endpoint […]
Free
Enterprise AI MLOps and Monitoring
This unit covers end-to-end machine learning operations and data practices for deploying and maintaining enterprise AI in the cloud. Students […]
Free
Deploying and Operationalizing AI
This unit focuses on taking trained models and AI services into production and operationalizing them at enterprise scale. Students learn […]
Free
Foundations of Enterprise AI Adoption
This unit introduces the foundational concepts, organizational considerations, and technical patterns required to adopt AI at enterprise scale. Students will […]
Free
Applied Deep Learning Solutions
This capstone unit builds on core deep learning principles to apply, optimize, and deploy models across real-world domains. Students will […]
Free
Deep Learning Model Engineering
This unit focuses on engineering practical deep learning systems: implementing models in frameworks (TensorFlow, Chainer, Torch), optimizing learning and inference, […]
Free