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.

Düzenli ifadelere giriş

Hangi desen?

Düzenli ifadeler pratiği: re.split() ve re.findall()

Tokenizasyona giriş

NLTK ile sözcük parçalama

re.search() ile daha fazla regex

NLTK ve regex ile ileri düzey tokenleştirme

Bir belirteçleyici (tokenizer) seçme

NLTK ile regex kullanarak tokenleştirme

Ascii olmayan metinlerde tokenizasyon

NLTK ile sözcük uzunluklarını görselleştirme

Grafik pratikleri

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

Charting practice

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.

Classifying fake news using supervised learning with NLP

Which possible features?

Training and testing

Building word count vectors with scikit-learn

CountVectorizer for text classification

TfidfVectorizer for text classification

Inspecting the vectors

Training and testing a classification model with scikit-learn

Text classification models

Training and testing the "fake news" model with CountVectorizer

Training and testing the "fake news" model with TfidfVectorizer

Simple NLP, complex problems

Improving the model

Improving your model

Inspecting your model

Building a "fake news" classifier

English stopwords

Monty Python and the Holy Grail

News articles

Wikipedia articles

Bu kursta, kelimeleri belirleme ve ayırma, bir metindeki konuları çıkarma ve kendi sahte haber sınıflandırıcını oluşturma gibi doğal dil işleme (NLP) temellerini öğreneceksin. Ayrıca NLTK gibi temel kütüphanelerin yanı sıra, yaygın NLP problemlerini çözmek için derin öğrenmeden yararlanan kütüphaneleri de kullanmayı öğreneceksin. Bu kurs, Python öğrenimine devam ederken metinleri işlemek ve ayrıştırmak için sağlam bir temel sağlayacak.

Python Toolbox

Python ile temel doğal dil işleme tekniklerini öğrenin ve gerçek metinlerden içgörü çıkarın.

Python ile Doğal Dil İşlemeye Giriş

Founder, kjamistan

Advanced tokenization with regex

Regex groups using or "|"

Regex ranges and groups

Character range with `re.match()`

Let's practice!

Regular expressions & word tokenization - NLTK ve regex ile ileri düzey tokenleştirme

NLTK ve regex ile ileri düzey tokenleştirme

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