Naive Bayes Theorem | Maximum A Posteriori Hypothesis | MAP Brute Force Algorithm by Mahesh Huddar

Naive Bayes Theorem | Maximum A Posteriori Hypothesis | MAP Brute Force Algorithm by Mahesh Huddar
Bayes theorem is the cornerstone of Bayesian learning methods because it provides a way to calculate the posterior probability P(h|D), from the prior probability P(h), together with P(D) and P(D(h).
The learner considers some set of candidate hypotheses H and is interested in finding the most probable hypothesis h ϵ H given the observed data D (or at least one of the maximally probable if there are several).
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[@snehagowda1082]

I will forward this video to our ML sir, so tht he may learn few ML concepts atleast

[@siddharthmagadum16]

🤣

[@kennethkath6527]

lol

[@chelsiyareddy7242]

😂

[@bhagyashreesarwad3251]

😂😂

[@BACSShaileshShettar]

😂😂😂

[@codeindia4730]

Thank You sir. Got Many ML Jargons Cleared Only Bcz of Your Videos.

[@ARKPROCODER]

I saw your whole playlists it was very helpful for my ML Exam all concepts covered with great explaination thank you

[@ARKPROCODER]

@@MaheshHuddar sure sir

[@meshackamimo1945]

Prof, kindly do a lecture in kalman filters as used in time series analysis

[@meshackamimo1945]

Or bayesian filters as may be used in time series analysis

[@jevigudaganavar4258]

Naive Bayes classifier can be considered as a ranking classifier. What does this mean?

[@devanshdalwadi6015]

ig it si calssifier to your distribution as a normal distribution

[@CSEKailashPaswan]

sir ,
notes