In this video, we are going to understand the Naive Bayes algorithm in detail.

The assumption of Naive Bayes is that each feature makes an equal & independent contribution to the outcome.

The Naive Bayes algorithm is widely utilized to address issues in academia, government, and business thanks to a recent surge in popularity. A Naive Bayes classifier combines a variety of useful characteristics in real-world machine learning. We'll explain the underlying assumptions later on.

The two popular classifiers Naive Bayes are Multinomial Naive Bayes & Bernoulli Naive Bayes.

Other points that we can consider when studying Naive Bayes is that:

1) This classifier works well in many real-world situations. They require a small amount of training data to estimate the necessary parameters.

2) Naive Bayes learners and classifiers can be extremely fast compared to more sophisticated methods. 

Naive Bayes Algorithm
https://www.geeksforgeeks.org/naive-bayes-classifiers/