Lecture 2.3 | Bayesian Learning | Concept Learning | Bayes Optimal Classifier | Bayes theorem #mlt

In this machine learning tutorial, you'll learn about the concepts of Bayesian learning, concept learning, and the Bayes optimal classifier. Bayesian learning is a powerful statistical framework that allows us to make probabilistic predictions based on our prior knowledge and the data we observe. Concept learning is a key part of machine learning, where we aim to learn general concepts from specific examples. And the Bayes optimal classifier is a classification algorithm that uses Bayesian decision theory to make predictions. In this video, we'll cover the following topics: 00:00 - Agenda 00:40 - Introduction to Bayesian learning and its applications 03:38 - Baye's Theorem 05:45 - Concept learning 09:50 - Bayes optimal classifier and how it works 17:00 - Example of Baye's optimal classifier Complete Machine learning course :- https://www.youtube.com/playlist?list=PLu4yXgpA6Wg5HTUfU4xh8eN5E8hpmRCNK Whether you're a beginner or an experienced data scientist, this tutorial will provide you with a solid understanding of these powerful statistical methods. Make sure to subscribe to our channel for more machine learning tutorials and hit the bell icon to be notified when we post new videos. And if you have any questions or comments, please leave them below—we'd love to hear from you! #BayesianLearning #ConceptLearning #BayesOptimalClassifier #MachineLearning #Statistics #DataScience #NaiveBayes #BayesianNetworks #ModelTraining #ModelTesting #RealWorldApplications #Tutorial #Beginners #Experienced #Classification #Probability #ArtificialIntelligence #AI #machinelearningtechniques