Calculus for Artificial Intelligence focuses on understanding how AI models learn and improve. It covers limits, derivatives, gradients, and optimization techniques that are essential for training models. The course explains how calculus is used in loss functions, gradient descent, and neural networks, providing the mathematical foundation behind learning, prediction, and decision-making in AI systems.

This course introduces the fundamental concepts of Discrete Mathematics essential for Artificial Intelligence and Computer Science. Topics include logic, sets, relations, functions, graph theory, combinatorics, and recursion. The course emphasizes logical reasoning, problem-solving, and algorithmic thinking required for AI tasks such as search algorithms, decision trees, graph-based models, and knowledge representation. It provides a strong theoretical foundation for advanced studies in Artificial Intelligence, Machine Learning, and Data Science.

Probability and Statistics for Machine Learning explains how uncertainty and data variability are modeled in ML systems. It covers probability distributions, random variables, expectation, variance, and statistical inference. The course shows how these concepts are used in data analysis, model evaluation, prediction, and decision-making under uncertainty, forming a key foundation for modern machine learning algorithms.

Linear Algebra for Machine Learning introduces the core mathematical concepts behind ML algorithms. It explains how vectors and matrices represent data, how transformations work, and why ideas like eigenvalues and matrix decompositions are important for tasks such as dimensionality reduction. The course connects theory with practical machine learning applications, building a strong foundation for understanding and improving models.

In this course, you'll learn how to stop repeating yourself and start teaching Claude once. You'll discover what Skills are and how they differ from other Claude Code customization options like CLAUDE.md, hooks, and subagents. You'll create your first Skill from scratch — writing the SKILL.md frontmatter, crafting effective descriptions that reliably trigger matching, and organizing your skill directory with progressive disclosure to keep context windows efficient. You'll also explore advanced configuration options like restricting tool access with allowed-tools and using scripts that execute without consuming context.

Beyond building individual Skills, you'll learn how to share them with your team by committing them to a repository, distribute them more broadly through plugins, and deploy them organization-wide using enterprise managed settings. You'll see how to wire Skills into custom subagents for isolated, expert task delegation, and you'll walk through a complete troubleshooting guide for diagnosing issues — from skills that won't trigger to priority conflicts and runtime errors. By the end, you'll have the knowledge to build a full Skills-based workflow that keeps Claude consistent, context-efficient, and aligned with your team's standards.