Naive Bayes Algorithm Explained in 3 Minutes

Learn how the Naive Bayes machine-learning algorithm uses probability and Bayes' theorem for classification. This video explains the naive conditional-independence assumption, spam detection example, prediction process, common variants, advantages, and limitations. ⏱️ Chapters: 0:00 - How Does Ai Classify Spam? 0:09 - What Is Naive Bayes? 0:20 - Bayes' Theorem 0:31 - The Core Intuition 0:39 - A Real Spam Example 0:51 - Comparing Posteriors 1:00 - Why Is It Called 'Naive'? 1:07 - Simplifying Reality 1:19 - The Training Phase 1:30 - Prediction In Action 1:39 - Numerical Classification 1:51 - Common Applications 2:03 - Gaussian Naive Bayes 2:14 - Multinomial & Bernoulli 2:24 - Key Advantages 2:34 - Limitations & Correlated Features 2:45 - Handling Unseen Words 2:55 - Final Summary 🔔 Subscribe to Ashok Raj for daily deep dives into AI engineering, software architecture, and machine learning!