In this introductory lecture, instructor Raunak explains the fundamentals of feature engineering for natural language processing (NLP) using Python. He begins by emphasizing that machine learning models require numerical, tabular data, illustrating this with the Iris dataset and explaining how categorical variables can be transformed into numeric form via one‑hot encoding using pandas' get_dummies function. The lecture then outlines the two‑step process needed to make raw text (e.g., movie reviews) usable for ML: first, text standardization (lowercasing and lemmatization) and second, vectorization to convert the cleaned text into numeric feature vectors. Raunak also describes simple document‑level features such as word count, character count, average word length, and tweet‑specific metrics like hashtag count. Finally, he introduces word‑level features, covering part‑of‑speech (POS) tagging and named entity recognition (NER) with concrete examples. The session sets the stage for later lessons that will dive deeper into preprocessing, feature extraction, and vectorization techniques.
Chapters
Welcome to Feature Engineering for NLP in Python. I am Raunak and I will be your instructor for this course. In this course you will learn to extract useful features out of text and convert them into formats that are suitable for machine learning algorithms. For any ML algorithm, data fed into it must be in tabular form and all the training features must must be numerical. Consider the IRIS dataset. Every training instance has exactly four numerical features. The ML algorithm uses these four features to train and predict if an instance belongs to class Iris Virginica, Iris Setosa or Iris Versicolor. ML algorithms can also work with categorical data provided they are converted into numerical form through one hot encoding. Let's say you have a categorical feature, sex, with two categories, male and female. One HOT encoding will convert this feature into two features, sex male and sex female, such that each male instance has a sex male value of one and sex female value of zero. For females it is the vice versa. To do this in code, we use Pandas getdummies function. Let's import pandas using the alias pd. We can then pass our DataFrame DF into the PD GetDummies function and pass a list of features to be encoded as the columns argument. Not mentioning columns will lead Pandas to automatically encode all non numerical features. Finally, we overwrite the original dataframe with the encoded version by assigning the DataFrame returned by GetDummies back to DF. Consider a movie reviews dataset. This data cannot be utilized by any machine learning or ML algorithm. The training feature review isn't numerical, neither is it categorical. To perform one hot encoding on, we need to perform two steps to make this dataset suitable for ML. The first is to standardize the text. This involves steps like converting words to lowercase and their base form. For instance, reduction gets lowercased and then converted to its base form reduce. We will cover these concepts in more detail in subsequent lessons. After pre processing, the reviews are converted into a set of numerical training features through a process known as vectorization. After vectorization, our original review data set gets converted into something like this. We will learn techniques to achieve this in later lessons. We can also extract certain basic features from text. It may be useful to know the word count, character count and average word length of a particular text. While working with niche data such as tweets, it may also be useful to know how many hashtags have been used in a tweet. This tweet by Silverado Records for instance, uses two. So far we have seen how to extract features out of an entire body of text. Some NLP applications may require you to to extract features for individual words. For instance, you may want to do part of speech tagging to know the different parts of speech present in your text, as shown. As an example, consider the sentence I have a dog. Pos Tagging will label each word with its corresponding part of speech. You may also want to know how to perform named entity recognition to find out if a particular noun is referring to a person, organization, or country. For instance, consider the sentence Brian works at datacamp. Here there are two nouns, Brian and datacamp. Brian refers to a person, whereas datacamp refers to an organization. Therefore, broadly speaking, this course will teach you how to conduct text pre processing, extract certain basic features, word features, and convert documents into a set of numerical features using a process known as vectorization. Great.
