This chapter will introduce some basic NLP concepts, such as word tokenization and regular expressions to help parse text. You'll also learn how to handle non-English text and more difficult tokenization you might find.

Introduction to regular expressions

Which pattern?

Practicing regular expressions: re.split() and re.findall()

Introduction to tokenization

Word tokenization with NLTK

More regex with re.search()

Advanced tokenization with NLTK and regex

Choosing a tokenizer

Regex with NLTK tokenization

Non-ascii tokenization

Charting word length with NLTK

Charting practice

Regular expressions & word tokenization

This chapter will introduce you to topic identification, which you can apply to any text you encounter in the wild. Using basic NLP models, you will identify topics from texts based on term frequencies. You'll experiment and compare two simple methods: bag-of-words and Tf-idf using NLTK, and a new library Gensim.

Word counts with bag-of-words

Bag-of-words picker

Building a Counter with bag-of-words

Simple text preprocessing

Text preprocessing steps

Text preprocessing practice

Introduction to gensim

What are word vectors?

Creating and querying a corpus with gensim

Gensim bag-of-words

Tf-idf with gensim

What is tf-idf?

Tf-idf with Wikipedia

Simple topic identification

This chapter will introduce a slightly more advanced topic: named-entity recognition. You'll learn how to identify the who, what, and where of your texts using pre-trained models on English and non-English text. You'll also learn how to use some new libraries, polyglot and spaCy, to add to your NLP toolbox.

Named Entity Recognition

NER with NLTK

Stanford library with NLTK

Introduction to SpaCy

Comparing NLTK with spaCy NER

spaCy NER Categories

Multilingual NER with polyglot

French NER with polyglot I

French NER with polyglot II

Spanish NER with polyglot

Named-entity recognition

You'll apply the basics of what you've learned along with some supervised machine learning to build a "fake news" detector. You'll begin by learning the basics of supervised machine learning, and then move forward by choosing a few important features and testing ideas to identify and classify fake news articles.

Mengklasifikasikan berita palsu menggunakan supervised learning dengan NLP

Fitur mana yang mungkin?

Pelatihan dan pengujian

Membangun vektor hitung kata dengan scikit-learn

CountVectorizer untuk klasifikasi teks

TfidfVectorizer untuk klasifikasi teks

Memeriksa vektor

Melatih dan menguji model klasifikasi dengan scikit-learn

Model klasifikasi teks

Melatih dan menguji model "fake news" dengan CountVectorizer

Melatih dan menguji model "fake news" dengan TfidfVectorizer

NLP sederhana, masalah kompleks

Meningkatkan model

Meningkatkan model Anda

Memeriksa model Anda

Building a "fake news" classifier

English stopwords

Monty Python and the Holy Grail

News articles

Wikipedia articles

Dalam kursus ini, Anda akan mempelajari dasar-dasar natural language processing (NLP), seperti cara mengidentifikasi dan memisahkan kata, mengekstrak topik dari teks, serta membangun pengklasifikasi berita palsu Anda sendiri. Anda juga akan belajar menggunakan pustaka dasar seperti NLTK, bersama pustaka yang memanfaatkan deep learning untuk menyelesaikan masalah NLP umum. Kursus ini akan memberi Anda landasan untuk memproses dan mengurai teks saat Anda melanjutkan pembelajaran Python.

Python Toolbox

Pelajari teknik dasar NLP dengan Python dan terapkan untuk mendapatkan wawasan dari data teks nyata.

Pengantar Natural Language Processing di Python

Pelajari teknik dasar pemrosesan bahasa alami menggunakan Python dan cara menerapkannya untuk mengekstrak wawasan dari data teks dunia nyata.

Founder, kjamistan

Training and testing a classification model with scikit-learn

Naive Bayes classifier

Naive Bayes with scikit-learn

Confusion matrix

Let's practice!

Building a "fake news" classifier - Melatih dan menguji model klasifikasi dengan scikit-learn

Melatih dan menguji model klasifikasi dengan scikit-learn

Create Your Free Account