Build your first tool-enabled AI agent with Microsoft Semantic Kernel. Learn how a single agent retrieves external information, makes decisions, and performs controlled actions using automatic function calling.

Learn how Microsoft Semantic Kernel transforms isolated LLM calls into orchestrated AI workflows by coordinating reasoning, plugins, memory, and application logic.

A deep dive into the core concepts behind Microsoft Semantic Kernel, explaining how Kernel, Plugins, Memory, and Orchestration work together to move from prompt-based AI to production-grade AI systems.

Discover why Microsoft created Semantic Kernel and how it helps developers move beyond prompt engineering to build scalable AI systems using orchestration, memory, plugins, and AI agents.

Learn how to conduct a one-sample T-test for a population mean using hypothesis testing, T-scores, and critical values. Understand when and why to use the T-distribution.

Learn how to perform a one-tailed or two-tailed Z-test for a population proportion. This guide walks through hypothesis setup, Z-score calculation, and P-value interpretation with a real-world case.

Learn how to formulate null and alternative hypotheses, understand their role in statistical significance testing, and apply them through real-world examples like click-through rates and server response times.

Learn to calculate confidence intervals using two fresh examples — one for estimating an average (mean) and one for estimating a percentage (proportion) in real-world data science scenarios.

Learn how to calculate the ideal sample size needed for your survey or experiment using step-by-step examples and formulas. Perfect for means or proportions.

Understand 90%, 95%, and 99% confidence intervals with real-world examples, Python code, and ML use cases. Learn how confidence levels affect interval width and precision.