When you work with machine learning, one important step is to define a baseline model. Imagine you have the following two sentences: These feature vectors are a crucial piece in data science and machine learning, as the model you want to train depends on them.

In a feature vector, each dimension can be a numeric or categorical feature, like for example the height of a building, the price of a stock, or, in our case, the count of a word in a vocabulary.

The resulting vector is also called a feature vector. The resulting vector will be with the length of the vocabulary and a count for each word in the vocabulary. You would then take the sentence you want to vectorize, and you count each occurrence in the vocabulary. This enables you to create a vector for a sentence. The vocabulary in this case is a list of words that occurred in our text where each word has its own index. The collection of texts is also called a corpus in NLP. You would start by taking the data and creating a vocabulary from all the words in all sentences. One way you could do this is to count the frequency of each word in each sentence and tie this count back to the entire set of words in the data set. Take a quick moment to think about how you would go about predicting the data. With this data set, you are able to train a model to predict the sentiment of a sentence. label 1 source yelp Name: 0, dtype: object

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