To train a Naive Bayes classifier using maximum likelihood estimation, we first concatenate all documents belonging to category $c$ into a single text representation. Then, we use the word frequencies in this concatenated document to compute the maximum likelihood estimate of the probability of each word $w_i$ given the class $c$:

$P(w_i|c) = \frac{count(w_i,c)}{\sum_{w\in V}count(w,c)}$

where $V$ represents the vocabulary containing all unique words in the training dataset.