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This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what the Naive Bayes algorithm is and Bayes theorem in general. You will understand conditional probability concepts, where the Naive Bayes classifier is used and how the Naive Bayes algorithm works. By the end of this video, you will also implement the Naive Bayes algorithm for text classification in Python.

Dataset Link - https://drive.google.com/drive/folders/1yqGMb98BG2rdP2CP8o6dipuZBt7Hexfd

The topics covered in this Naive Bayes video are as follows:
00:00 - 01:06 Introduction and Agenda
01:06 - 05:45 What is Naive Bayes?
05:45 - 06:30 Why do we need Naive Bayes?
06:30 - 20:17 Understanding Naive Bayes Classifier
20:17 - 22:36 Advantages of Naive Bayes Classifier
22:36 - 43:45 Demo - Text Classification using Naive Bayes

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What is Naive Bayes Classifier?
Naive Bayes is a supervised learning algorithm that is based on applying Bayes’ theorem with the “naive” assumption. The Bayes Rule gives the formula for the probability of Y given X. It is called Naive because of the naive assumption that the X’s are independent of each other.

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