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

Data format for ML algorithms

One-hot encoding

Basic feature extraction

Character count of Russian tweets

Word count of TED talks

Hashtags and mentions in Russian tweets

Readability tests

Readability of 'The Myth of Sisyphus'

Readability of various publications

Basic features and readability scores

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

Identifying lemmas

Tokenizing the Gettysburg Address

Lemmatizing the Gettysburg address

Text cleaning

Cleaning a blog post

Cleaning TED talks in a dataframe

Part-of-speech tagging

POS tagging in Lord of the Flies

Counting nouns in a piece of text

Noun usage in fake news

Named entity recognition

Named entities in a sentence

Identifying people mentioned in a news article

Text preprocessing, POS tagging and NER

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

Building a bag of words model

Word vectors with a given vocabulary

BoW model for movie taglines

Analyzing dimensionality and preprocessing

Mapping feature indices with feature names

Building a BoW Naive Bayes classifier

BoW vectors for movie reviews

Predicting the sentiment of a movie review

Building n-gram models

n-gram models for movie tag lines

Higher order n-grams for sentiment analysis

Comparing performance of n-gram models

N-Gram models

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.

Membangun vektor dokumen tf-idf

Bobot tf-idf untuk kata yang sering muncul

Vektor tf-idf untuk TED Talk

Kemiripan kosinus

Rentang skor kosinus

Menghitung dot product

Matriks kemiripan kosinus dari sebuah korpus

Membangun sistem rekomendasi berbasis alur cerita

Membandingkan linear_kernel dan cosine_similarity

Mesin rekomendasi plot

Fungsi rekomendasi

Rekomendasi TED Talk

Melampaui n-gram: word embeddings

Menghasilkan vektor kata

Menghitung kemiripan lagu-lagu Pink Floyd

Selamat!

TF-IDF and similarity scores

Russian Troll Tweets

Movie Overviews and Taglines

Preprocessed Movie Reviews

TED Talk Transcripts

Real and Fake News Headlines

Dalam kursus ini, Anda akan mempelajari teknik-teknik untuk mengekstrak informasi bermanfaat dari teks dan memprosesnya ke dalam format yang sesuai untuk penerapan model ML. Secara khusus, Anda akan mempelajari POS tagging, named entity recognition, skor keterbacaan, model n-gram dan tf-idf, serta cara mengimplementasikannya menggunakan scikit-learn dan spaCy. Anda juga akan belajar menghitung seberapa mirip dua dokumen satu sama lain. Dalam prosesnya, Anda akan memprediksi sentimen ulasan film serta membangun sistem rekomendasi film dan Ted Talk. Setelah mengikuti kursus ini, Anda akan mampu merekayasa fitur-fitur penting dari teks apa pun dan menyelesaikan beberapa masalah paling menantang dalam data science!

Introduction to Natural Language Processing in Python

Supervised Learning with scikit-learn

Pelajari teknik Python untuk mengekstrak informasi dari teks dan memprosesnya untuk model pembelajaran mesin.

Rekayasa Fitur untuk NLP di Python

Pelajari teknik untuk mengekstrak informasi berguna dari teks dan memprosesnya ke dalam format yang sesuai untuk pembelajaran mesin.

Melampaui n-gram: word embeddings

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