Despite its practical applications, particularly in text mining, Naive Bayes is labelled "Naive" because it relies on an assumption that is nearly impossible to verify in real-world data: the conditional probability is calculated as the pure product of the individual probabilities of components. This necessitates complete feature independence, which is a criterion that is unlikely to be realised in real life.
Simply defined, machine learning allows a user to submit massive amounts of data to a computer algorithm, which then analyses and makes data-driven suggestions and decisions based only on the supplied data.
Learner's Ratings
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Reviews
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Muhammad Qasim
5
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
5
None
A
Aniket Kumar prasad
5
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
5
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
5
Amazing Teaching
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Juboraj Juboraj
5
Easy to understand & explain details.
J
Joydeb
5
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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