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.

Nepnieuws classificeren met supervised learning en NLP

Welke mogelijke features?

Trainen en testen

Woordtelvectoren bouwen met scikit-learn

CountVectorizer voor tekstclassificatie

TfidfVectorizer voor tekstclassificatie

De vectoren inspecteren

Een classificatiemodel trainen en testen met scikit-learn

Modellen voor tekstclassificatie

Het "fake news"-model trainen en testen met CountVectorizer

Het "fake news"-model trainen en testen met TfidfVectorizer

Eenvoudige NLP, complexe problemen

Het model verbeteren

Je model verbeteren

Je model inspecteren

Building a "fake news" classifier

English stopwords

Monty Python and the Holy Grail

News articles

Wikipedia articles

In deze cursus leer je de basis van natural language processing (NLP), zoals hoe je woorden herkent en scheidt, hoe je onderwerpen uit een tekst haalt en hoe je je eigen nepnieuws-classifier bouwt. Je leert ook werken met basisbibliotheken zoals NLTK en met bibliotheken die deep learning gebruiken om veelvoorkomende NLP-problemen op te lossen. Deze cursus geeft je een basis om tekst te verwerken en te parsen terwijl je verdergaat met Python.

Python Toolbox

Leer basis NLP-technieken met Python en pas ze toe om inzichten uit echte tekstdata te halen.

Introductie tot Natural Language Processing in Python

Leer de basis van natuurlijke taalverwerking met Python en hoe je dit kunt gebruiken om dingen te ontdekken in echte tekstgegevens.

Founder, kjamistan

Classifying fake news using supervised learning with NLP

What is supervised learning?

Supervised learning with NLP

IMDB Movie Dataset

Supervised learning steps

Let's practice!

Building a "fake news" classifier - Nepnieuws classificeren met supervised learning en NLP

Nepnieuws classificeren met supervised learning en NLP

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