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A Naïve Bayes classifier is an algorithm that uses Bayes' theorem to classify objects. Naive Bayes classifiers assume strong, or naive, independence between attributes of data points.

This session will be taken by Great Learning Faculty Professor Mukesh Rao who has over 20 years of industry experience in Market Research, Project Management, and Data Science.

Popular uses of Naïve Bayes classifiers include spam filters, text analysis and medical diagnosis. These classifiers are widely used for machine learning because they are simple to implement. This theorem is the foundation of deductive reasoning, which focuses on determining the probability of an event occurring based on prior knowledge of conditions that might be related to the event. Naïve Bayes can be used for sentiment analysis, weather prediction, medical diagnosis and multiple other things. So, understanding the importance of naïve bayes, we have come with this comprehensive tutorial.

These are topics covered in this session:
1. 1:01- Intro to Naïve Bayes Classifier
2. 5:29- Joint Probability
3. 36:23- Advantages and Disadvantages of Naïve Bayes Classifier
4. 47:35 – Demo with Scikit-learn

Learn more about Naive Bayes :
https://www.mygreatlearning.com/blog/introduction-to-naive-bayes/

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