24. Bayes’ Theorem

Описание: 📌 Video Description

Bayes’ Theorem is a fundamental concept in probability, statistics, and data science that explains how we update our beliefs when new information becomes available. In this video, we break down Bayes’ Theorem in a clear and intuitive way.

You’ll learn how to calculate the posterior probability by combining prior knowledge (prior probability) with new evidence (likelihood). This approach is essential for reasoning under uncertainty and is widely used in machine learning, medical diagnosis, spam filtering, fraud detection, and risk analysis.

Rather than memorizing formulas, this lesson focuses on understanding how and why Bayes’ Theorem works.

🎯 What You’ll Learn in This Video

What Bayes’ Theorem is

Meaning of prior probability

Role of new evidence (likelihood)

How to compute posterior probability

Why probability updates matter in decision-making

Real-world intuition behind Bayesian reasoning

👨‍💻 Who This Video Is For

Statistics and data science beginners

Students preparing for exams

Data analysts and machine learning learners

Anyone interested in probabilistic thinking

🔑 Keywords (SEO)

Bayes Theorem, Bayesian Probability, Posterior Probability, Prior Probability, Conditional Probability, Statistics Basics, Data Science Fundamentals, Learn Probability

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📌 Hashtags

#BayesTheorem #Statistics #DataScience #Bayesian #Probability #MachineLearning #LearnStatistics #TechFundamentals