The Naive Bayes Classifier is a basic and effective classification method that aids in the development of fast machine learning models capable of making quick predictions. It's a probabilistic classifier, which means it makes predictions based on an object's probability.
Although intractable, the conditional probability can be determined using the joint probability. The Bayes Theorem establishes a consistent method for estimating conditional probability. The computation for Bayes Theorem in its simplest version is as follows: P(A|B) = P(B|A) * P(A) / P(A) / P(A) / P(A) / P(A) / P(A) (B)
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Muhammad Qasim
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Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world.
Extremely helpful and I recommend to my peer as well for this course.
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Shafi Akhtar
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Aniket Kumar prasad
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Very helpful and easy to understand all the concepts, best teacher for learning ML.
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Rishu Shrivastav
5
explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
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very nicely explained
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Vrushali Kandesar
5
Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Mohd Mushraf
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Amazing Teaching
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Juboraj Juboraj
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Easy to understand & explain details.
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Joydeb
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Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
5
Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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