Yes, you can use n-grams for both word and char.
+---------------------------+-------------------------+--------------------------------------------------------------+
| Parameter | Default Value | Description |
+===========================+=========================+==============================================================+
| use_shared_vocab | False | If set to 'True' a common vocabulary is used for labels |
| | | and user message. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| analyzer | word | Whether the features should be made of word n-gram or |
| | | character n-grams. Option 'char_wb' creates character |
| | | n-grams only from text inside word boundaries; |
| | | n-grams at the edges of words are padded with space. |
| | | Valid values: 'word', 'char', 'char_wb'. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| strip_accents | None | Remove accents during the pre-processing step. |
| | | Valid values: 'ascii', 'unicode', 'None'. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| stop_words | None | A list of stop words to use. |
| | | Valid values: 'english' (uses an internal list of |
| | | English stop words), a list of custom stop words, or |
| | | 'None'. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| min_df | 1 | When building the vocabulary ignore terms that have a |
| | | document frequency strictly lower than the given threshold. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| max_df | 1 | When building the vocabulary ignore terms that have a |
| | | document frequency strictly higher than the given threshold |
| | | (corpus-specific stop words). |
+---------------------------+-------------------------+--------------------------------------------------------------+
| min_ngram | 1 | The lower boundary of the range of n-values for different |
| | | word n-grams or char n-grams to be extracted. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| max_ngram | 1 | The upper boundary of the range of n-values for different |
| | | word n-grams or char n-grams to be extracted. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| max_features | None | If not 'None', build a vocabulary that only consider the top |
| | | max_features ordered by term frequency across the corpus. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| lowercase | True | Convert all characters to lowercase before tokenizing. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| OOV_token | None | Keyword for unseen words. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| OOV_words | [] | List of words to be treated as 'OOV_token' during training. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| alias | CountVectorFeaturizer | Alias name of featurizer. |
+---------------------------+-------------------------+--------------------------------------------------------------+
| use_lemma | True | Use the lemma of words for featurization. |
+---------------------------+-------------------------+--------------------------------------------------------------+
Option char_wb creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. This option can be used to create Subword Semantic Hashing.
For more technical explanations, I suggest you read online articles or watch the Rasa Algorithm Whiteboard.