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.

Membangun model bag-of-words

Vektor kata dengan kosakata yang diberikan

Model BoW untuk tagline film

Menganalisis dimensi dan prapemrosesan

Memetakan indeks fitur dengan nama fitur

Membangun klasifier Naive Bayes BoW

Vektor BoW untuk ulasan film

Memprediksi sentimen ulasan film

Membangun model n-gram

Model n-gram untuk slogan film

N-gram tingkat lebih tinggi untuk analisis sentimen

Membandingkan kinerja model n-gram

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.

Building tf-idf document vectors

tf-idf weight of commonly occurring words

tf-idf vectors for TED talks

Cosine similarity

Range of cosine scores

Computing dot product

Cosine similarity matrix of a corpus

Building a plot line based recommender

Comparing linear_kernel and cosine_similarity

Plot recommendation engine

The recommender function

TED talk recommender

Beyond n-grams: word embeddings

Generating word vectors

Computing similarity of Pink Floyd songs

Congratulations!

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.

Membangun model n-gram

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