The following are the steps for executing the CountVectorizer:

1. Import the library required for the count vectorizer:

from sklearn.feature_extraction.text import CountVectorizer

2. Make a list of the text:

text = [" Machine translation automatically translate text from one human language to another text"]

3. Tokenize the list of the text and build the vocabulary:

vectorizer.fit(text)

You will get the following output:

4. Let's take a look at the vocabulary that was created:

print(vectorizer.vocabulary_)

We get the following output:

5. Now, we have to encode it, as follows:

vector = vectorizer.transform(text)

6. Let's get a summary of the vector and find out the term matrix:

print(type(vector))
print(vector.toarray())

We get the following output: