
We now turn to the consideration of cases of working specifically with text data. The goal is to identify the relevant information within the text, such as words or phrases, and convert them into a format that can be easily understood by a computer. This process involves various techniques such as tokenization, stopword removal, stemming, and vectorization. The resulting features can be used to build predictive models for various natural language processing (NLP) tasks, such as sentiment analysis, topic modeling, and text classification.


There are several methods for feature extraction from text, but some of the most commonly used ones include:

1. *Bag of words (BoW)* - a method that represents text as a set of unique words, ignoring the order of words and the grammar of the sentences. It works by counting the frequency of each word in the text and creating a vector of those frequencies.

2. *Term frequency*-inverse document frequency (TF-IDF) - a method that takes into account the importance of each word in the text by calculating the frequency of a word in a document (term frequency) and the frequency of that word in the entire corpus of documents (inverse document frequency). This results in a vector of importance scores for each word in the text.

3. *Word embeddings* - a method that represents words in a continuous vector space, capturing the semantic relationships between words. This is achieved by training a neural network on a large corpus of text to predict the context in which a word appears.

4. *Latent Dirichlet allocation (LDA)* - a method for topic modeling, which represents each document as a mixture of topics, where each topic is represented by a distribution of words. LDA can be used to extract features from the text by identifying the most relevant topics for a given document or corpus.

Now we will not delve into the mathematical theory of each method but only mention that the most efficient text representation at the moment is performed based on the word embeddings method.


Creating a machine learning model seems to be your most challenging and essential task. But first, we have to work with data! Learn how to process datasets and fully prepare them for use. Numerical, categorical, and temporal data await you in our course.

Different types of data? How to work with them? If your eyes are wide open, don't worry, let's start with a brief overview of the pandas library and learn how to work with it in the future.

This chapter discusses in detail how to work with quantitative data, what methods it is processed with, how data scaling and normalization differ, and much more.

Is categorical data as simple as you think it is? Find out what is the complexity of processing and working with it.


Time series data processing is the process of handling, analyzing, and preparing data that is presented as a sequence of temporally ordered values. Find out what steps it includes in this section.

Did you know that you can extract even more values from your data and create more informative features? In this section, you will learn how to work with feature engineering.

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Feature Extraction from Text

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Feature Extraction from Text