Guides the hands-on development of a generative language model from scratch, including building text completion, content filtering, and text generation applications.

Covers building and managing AI agent systems using the actor model, including practical techniques for developing, coordinating, and supervising agents and subagents across modern programming environments.

Provides an overview of transformer-based models and large language models, including their evolution, capabilities, and key concepts such as scaling laws and model differences.

Explores advanced prompting strategies for AI applications, including Chain-of-Thought and Tree-of-Thought, and explains how different approaches support more complex problem-solving.

Explores how generative AI enables personalized learning by automating content creation, supporting adaptive learning workflows, and helping design tailored learning experiences at scale.

Explains how text-to-image generation works, including diffusion models, model components, and practical implementation, along with performance considerations, limitations, and ethical concerns.

Shows how to use generative AI to improve public speaking by supporting speech development, audience analysis, practice, and feedback to enhance delivery and confidence.