Course
Feature Engineering for NLP in Python
AdvancedSkill Level
Updated 11/2024PythonMachine Learning4 hr15 videos52 Exercises4,200 XP28,952Statement of Accomplishment
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Prerequisites
Introduction to Natural Language Processing in PythonSupervised Learning with scikit-learn1
Basic features and readability scores
Learn 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.
2
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.
3
N-Gram models
Learn about n-gram modeling and use it to perform sentiment analysis on movie reviews.
4
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.
Feature Engineering for NLP in Python
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FAQs
What NLP feature engineering techniques will I learn?
You learn POS tagging, named entity recognition, readability scores, n-gram models, tf-idf weighting, cosine similarity, and word embeddings. Each technique converts text into features suitable for ML models.
What projects or applications are built during the course?
You predict movie review sentiment, build a movie recommender, and create a TED Talk recommender. You also analyze noun usage in fake news and compare Pink Floyd songs using word vectors.
Which Python libraries are used for the feature engineering tasks?
You use scikit-learn for vectorization and modeling, and spaCy for POS tagging, named entity recognition, and text preprocessing. Both are industry-standard NLP tools.
What are readability scores and why do they matter?
Readability scores measure how much education is needed to understand a text. Chapter 1 teaches you to compute them as basic features that quantify text complexity for machine learning.
What prior NLP knowledge is needed before starting?
You need Introduction to Natural Language Processing in Python plus seven other prerequisites including pandas, scikit-learn, and statistics. This is an advanced course for experienced Python users.
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