Feature Engineering for NLP in Python
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
- 4 hours
- 15 Videos
- 52 Exercises
- 7,842 Participants
- 4,200 XP
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Course Description
In this course, you will learn techniques that will allow you to extract useful information from text and process them into a format suitable for applying ML models. More specifically, you will learn about POS tagging, named entity recognition, readability scores, the n-gram and tf-idf models, and how to implement them using scikit-learn and spaCy. You will also learn to compute how similar two documents are to each other. In the process, you will predict the sentiment of movie reviews and build movie and Ted Talk recommenders. Following the course, you will be able to engineer critical features out of any text and solve some of the most challenging problems in data science!
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1
Basic features and readability scores
FreeLearn to compute basic features such as number of words, number of characters, average word length and number of special characters (such as Twitter hashtags and mentions). You will also learn to compute readability scores and determine the amount of education required to comprehend a piece of text.
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N-Gram models
Learn about n-gram modeling and use it to perform sentiment analysis on movie reviews.
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Text preprocessing, POS tagging and NER
In this chapter, you will learn about tokenization and lemmatization. You will then learn how to perform text cleaning, part-of-speech tagging, and named entity recognition using the spaCy library. Upon mastering these concepts, you will proceed to make the Gettysburg address machine-friendly, analyze noun usage in fake news, and identify people mentioned in a TechCrunch article.
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TF-IDF and similarity scores
Learn how to compute tf-idf weights and the cosine similarity score between two vectors. You will use these concepts to build a movie and a TED Talk recommender. Finally, you will also learn about word embeddings and using word vector representations, you will compute similarities between various Pink Floyd songs.
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1
Basic features and readability scores
FreeLearn to compute basic features such as number of words, number of characters, average word length and number of special characters (such as Twitter hashtags and mentions). You will also learn to compute readability scores and determine the amount of education required to comprehend a piece of text.
-
Text preprocessing, POS tagging and NER
In this chapter, you will learn about tokenization and lemmatization. You will then learn how to perform text cleaning, part-of-speech tagging, and named entity recognition using the spaCy library. Upon mastering these concepts, you will proceed to make the Gettysburg address machine-friendly, analyze noun usage in fake news, and identify people mentioned in a TechCrunch article.
-
N-Gram models
Learn about n-gram modeling and use it to perform sentiment analysis on movie reviews.
-
TF-IDF and similarity scores
Learn how to compute tf-idf weights and the cosine similarity score between two vectors. You will use these concepts to build a movie and a TED Talk recommender. Finally, you will also learn about word embeddings and using word vector representations, you will compute similarities between various Pink Floyd songs.
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Rounak Banik
Data Scientist at Fractal Analytics
Rounak is a Young India Fellow and the author of the book, Hands-on Recommendation Systems with Python. He currently works as a Data Scientist at Fractal Analytics. He obtained his B.Tech degree in Electronics & Communication Engineering from IIT Roorkee.
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Collaborators
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Hillary Green-Lerman
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Adrián Soto