Done

I learned in this lecture about 1-Gaussian Naïve Bayes ( used when features are continuous and normally distributed) 2-Multinomial Naive Bayes ( document classification, when the frequencies of the words or tokens in the documents ) 3-Bernoulli Navie Bayes ( when features are binary 0 and 1 )

I learned in this video 1-Gaussian Naïve Bayes ( used when features are continuous and normally distributed) 2-Multinomial Naive Bayes ( document classification, when the frequencies of the words or tokens in the documents ) 3-Bernoulli Navie Bayes ( when features are binary 0 and 1 )

We learned about naive bayes algorithm in this lecture....Types of (NB)1-Gaussian NB 2-Multinomial NB 3-bernoulli NB ....Application of NB algorithm : 1-Email spam filtering 2-Sentiment analysis 3-Document categorization 4-Medical diagnosis 5-Text classification 6-Face recognition 7-Weather prediction......Advantages of naive bayes 1- It is simple and easy to implement. 2-It doesn't require as much training data. 3-It handles both continuous and discrete data. 4-It is highly scalable with the number of predictors and data points. 5-It is fast and can be used to make real-time prediction.... Limitations of NB: 1-The Naive Bayes Algorithm has trouble with the 'zero-frequency problem'. ... 2-It will assume that all the attributes are independent, which rarely happens in real life. ... 3-It will estimate things wrong sometimes, so you shouldn't take its probability outputs seriously.

AOA, I learned in this lecture about the Naïve Bayes algorithm (NB) for best model selection, which is used for classification tasks ( spam filtering, basic image classification, text-based sentiment analysis) and its types1-Gaussian Naïve Bayes ( used when features are continuous and normally distributed)2-Multinomial Naive Bayes ( document classification, when the frequencies of the words or tokens in the documents )3-Bernoulli Navie Bayes ( when features are binary 0 and 1 )And also learned the application of NB which are1-Email spam filtering 2-Sentiment analysis 3-Document categorization 4-Medical diagnosisAnd also learned the advantages of NB which are1-Simplicity ( straightforward to implement and understand ) 2-Efficiency ( requires a small amount of training data ) 3-Speed ( very fast, making them suitable for real-time prediction ) 4-Good performance ( often performs well in multi-class prediction )And also learned the disadvantages of NB which are1-Features independence 2-Data scarcity ( not suitable for small data ) 3-Highly correlated features (it does not work well with highly correlated features )ALLAH PAK aap ko sahat o aafiyat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey aur aap ke walid-e-mohtram ko karwat karwat jannat ata farmaye,Ameen.

The discussion in this lecture is about the different types, advantages, and limitations of the Naive Bayes algorithm.