课程

Feature Engineering for NLP in Python

高级技能水平

更新时间 2024年11月

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

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PythonMachine Learning4 小时15 视频52 练习4,200 经验值28,897成就声明

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课程描述

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!

先决条件

Introduction to Natural Language Processing in Python Supervised Learning with scikit-learn

1

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.

Introduction to NLP feature engineering

50 经验值

Data format for ML algorithms

50 经验值

One-hot encoding

100 经验值

Basic feature extraction

50 经验值

Character count of Russian tweets

100 经验值

Word count of TED talks

100 经验值

Hashtags and mentions in Russian tweets

100 经验值

Readability tests

50 经验值

Readability of 'The Myth of Sisyphus'

100 经验值

Readability of various publications

100 经验值

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.

Tokenization and Lemmatization

50 经验值

Identifying lemmas

50 经验值

Tokenizing the Gettysburg Address

100 经验值

Lemmatizing the Gettysburg address

100 经验值

Text cleaning

50 经验值

Cleaning a blog post

100 经验值

Cleaning TED talks in a dataframe

100 经验值

Part-of-speech tagging

50 经验值

POS tagging in Lord of the Flies

100 经验值

Counting nouns in a piece of text

100 经验值

Noun usage in fake news

100 经验值

Named entity recognition

50 经验值

Named entities in a sentence

100 经验值

Identifying people mentioned in a news article

100 经验值

3

N-Gram models

Learn about n-gram modeling and use it to perform sentiment analysis on movie reviews.

Building a bag of words model

50 经验值

Word vectors with a given vocabulary

50 经验值

BoW model for movie taglines

100 经验值

Analyzing dimensionality and preprocessing

100 经验值

Mapping feature indices with feature names

100 经验值

Building a BoW Naive Bayes classifier

50 经验值

BoW vectors for movie reviews

100 经验值

Predicting the sentiment of a movie review

100 经验值

Building n-gram models

50 经验值

n-gram models for movie tag lines

100 经验值

Higher order n-grams for sentiment analysis

100 经验值

Comparing performance of n-gram models

100 经验值

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.

Building tf-idf document vectors

50 经验值

tf-idf weight of commonly occurring words

50 经验值

tf-idf vectors for TED talks

100 经验值

Cosine similarity

50 经验值

Range of cosine scores

50 经验值

Computing dot product

100 经验值

Cosine similarity matrix of a corpus

100 经验值

Building a plot line based recommender

50 经验值

Comparing linear_kernel and cosine_similarity

100 经验值

Plot recommendation engine

100 经验值

The recommender function

100 经验值

TED talk recommender

100 经验值

Beyond n-grams: word embeddings

50 经验值

Generating word vectors

100 经验值

Computing similarity of Pink Floyd songs

100 经验值

Congratulations!

50 经验值

Feature Engineering for NLP in Python

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