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Feature Engineering for NLP in Python

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

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4 Hours15 Videos52 Exercises18,213 Learners4200 XPMachine Learning Scientist TrackNatural Language Processing Track

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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!
  1. 1

    Basic features and readability scores

    Free

    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.

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    Introduction to NLP feature engineering
    50 xp
    Data format for ML algorithms
    50 xp
    One-hot encoding
    100 xp
    Basic feature extraction
    50 xp
    Character count of Russian tweets
    100 xp
    Word count of TED talks
    100 xp
    Hashtags and mentions in Russian tweets
    100 xp
    Readability tests
    50 xp
    Readability of 'The Myth of Sisyphus'
    100 xp
    Readability of various publications
    100 xp
  2. 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.

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In the following tracks

Machine Learning ScientistNatural Language Processing

Collaborators

adriansoto
Adrián Soto
hillary-green-lerman
Hillary Green-Lerman
Rounak Banik Headshot

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 Science Fellow with the QuantumBlack division of McKinsey and Company. He obtained his B.Tech degree in Electronics & Communication Engineering from IIT Roorkee.
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