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A self-paced course with lectures from Stanford faculty that helps public servants understand the opportunities and impact of AI in government, including its technology, capabilities, and risks.

A comprehensive program designed to equip professionals with the skills to harness AI and machine learning for strategic decision-making. The course covers AI-driven decision-making strategies, data-driven decision-making with AI, and the application of predictive analytics in real-world business scenarios.

This course provides an introduction to graph representation learning, including matrix factorization-based methods, random-walk based algorithms, graph neural networks, and deep generative models of graphs. It covers both theoretical motivations and practical applications.

This course from Harvard University explores the concepts and algorithms at the foundation of modern artificial intelligence. It delves into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. The course covers topics including graph search algorithms, reinforcement learning, and neural networks.

A Linked In Learning course that explores the intersection of causal inference and machine learning, teaching how to build more robust and interpretable models.

An introductory course that provides an interactive environment to learn the basics of CUDA. It covers launching parallel CUDA Kernels, organizing thread execution, managing memory between the CPU and GPU, and profiling CUDA code to observe performance gains.

A comprehensive three-day virtual workshop designed for competitive intelligence professionals to harness the power of AI. The course covers foundational AI literacy, hands-on application of AI tools like ChatGPT and Claude.ai, crafting effective prompts, and building automated workflows. The workshop focuses on transforming AI-generated data into strategic narratives and provides frameworks for positioning CI professionals as indispensable strategic partners in the AI age.

Learn 10-601: Introduction to Machine Learning

Basic ideas and techniques underlying the design of intelligent computer systems. Topics include searching, game playing, knowledge representation, logical inference, planning, reasoning under uncertainty, machine learning, and applications.