Bayes theorem helps us to incorporate new evidences/information into our model.
Code from sklearn
from sklearn.naive_bayes import GaussianNB
clf=GaussianNB()
clf.fit(features_train,labels_train)
Bayesian Learning:
Linear in the number of variables.
Naive Bayes assumes conditional independence across attributes and doesn’t capture inter-relationship among attributes.
Gaussian naive Bayes assumes continues values associated with each class are distributed in Gaussian fashion.
Even if the probability of one of the attributes given label becomes zero, the whole thing ends up being zero.
Maximum Likelihood :
Ideasinplain 