This tutorial on “Naive Bayes Classifier” will help you to comprehensively learn all the topics w.r.t to this classification algorithm. Naive Bayes is the most simple algorithm that you can apply to your data. As the name suggests, here this algorithm makes an assumption as all the variables in the dataset is “Naive” i.e not correlated to each other.

Naive Bayes is the most straightforward and fast classification algorithm, which is suitable for a large chunk of data. Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for prediction of unknown class

The underlying principles in the Naive Bayes Classifier are quite intuitive, and this model performs surprisingly well in many cases and its variations are used in many problems. So, in this video we will be talking about Naive Bayes Classifier Concept, Demo on Naive Bayes, and much more along with the visuals for better understanding.

This tutorial will comprise of the following topics:

• 0:00 Introduction
• 0:51 - Naive Bayes Classifier Concept
• 21:32 - Demo on Naive Bayes

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