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)
Learner's Ratings
4.4
Overall Rating
70%
11%
11%
5%
3%
Reviews
R
Rishu Shrivastav
5
explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
V
VIKAS CHOUBEY
5
very nicely explained
V
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)
M
Mohd Mushraf
5
Amazing Teaching
J
Juboraj Juboraj
5
Easy to understand & explain details.
J
Joydeb
5
Awesome Course sir and your teaching style is very GOOD.
S
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
N
Neel Khairnar
5
Kushal is very good explainer he is covering all topics nicely 👍
Share a personalized message with your friends.