Naïve Bayes Classifier Explained | Classification, Laplace Smoothing & Worked Examples

Description: Learn how the Naïve Bayes Classifier works and why it remains one of the most effective and widely used machine learning algorithms. In this lecture, you'll explore the conditional independence assumption, calculate class probabilities, handle categorical and continuous features, and avoid the zero-probability problem using Laplace smoothing. 📌 Topics covered: Bayes Classifier vs. Naïve Bayes Classifier The conditional independence assumption Computing posterior probabilities Categorical vs. continuous-valued features Gaussian Naïve Bayes Golf-playing prediction example Computer purchase prediction example Likelihood tables and classification Zero-probability problem Laplace (Laplacian) smoothing Strengths and weaknesses of Naïve Bayes By the end of this lecture, you'll understand how Naïve Bayes makes predictions from data and why it is often surprisingly competitive despite its simple assumptions. #MachineLearning #NaiveBayes #BayesClassifier #DataMining #DataScience #ArtificialIntelligence #Classification #Statistics #Python #ComputerScience #LaplaceSmoothing #GaussianNaiveBayes #MLAlgorithms