Introduction to NLP

Introduction to NLP

0%

1. Text Preprocessing Fundamentals

We will start our journey with learning and implementing the most common text preprocessing techniques used in NLP to convert the initial raw text into a clean, standardized form.

NLP and Its Applications

Understanding Text Preprocessing

Tokenize a Sentence

Tokenization Using Regular Expressions

Tokenize Using Regex

Removing Stop Words

Applying Text Preprocessing in Practice

2. Stemming and Lemmatization

Without further ado, let's explore stemming and lemmatization. These techniques may enhance efficiency and effectiveness of some NLP tasks, especially when working with large text corpora and treating different words forms as the same word.

Stem the Tokens

Part of Speech Tagging

Lemmatization with POS Tagging

3. Basic Text Models

Preprocessed text should then be transformed into a numerical representation to be used in machine learning or deep learning models for various tasks such as prediction, classification, or clustering. Here, we will learn to implement the most basic yet popular text models that transform text data into numbers.

Overview of Vector Space Models

Key Types of Vector Space Models

Customizing Bag of Words

Create a Bag of Words

Implementing TF-IDF

4. Word Embeddings

Time to unlock the power of word embeddings and delve into advanced techniques for capturing semantic relationships between words. We will explore various embedding models such as Word2Vec, GloVe, and FastText, with a particular focus on the Word2Vec model and its implementation.

Word Embeddings Basics

CBoW and Skip-gram Models (Optional)

Implementing Word2Vec

Create Word Embeddings

Contenido del Curso

Introduction to NLP

Introduction to NLP

Tokenize Using Regex

Tarea

Given a string named message, convert it lowercase, then tokenize it into words using regular expression tokenization and the corresponding nltk class. A word is a sequence of only alphanumeric characters (letters and numbers). '#Conference2023!', for example, contains one word: Conference2023.

Tarea

Given a string named message, convert it lowercase, then tokenize it into words using regular expression tokenization and the corresponding nltk class. A word is a sequence of only alphanumeric characters (letters and numbers). '#Conference2023!', for example, contains one word: Conference2023.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 1. Capítulo 6

toggle bottom row

Tokenize Using Regex

Tarea

Given a string named message, convert it lowercase, then tokenize it into words using regular expression tokenization and the corresponding nltk class. A word is a sequence of only alphanumeric characters (letters and numbers). '#Conference2023!', for example, contains one word: Conference2023.

Tarea

Given a string named message, convert it lowercase, then tokenize it into words using regular expression tokenization and the corresponding nltk class. A word is a sequence of only alphanumeric characters (letters and numbers). '#Conference2023!', for example, contains one word: Conference2023.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 1. Capítulo 6

toggle bottom row

Tokenize Using Regex

Tarea

Given a string named message, convert it lowercase, then tokenize it into words using regular expression tokenization and the corresponding nltk class. A word is a sequence of only alphanumeric characters (letters and numbers). '#Conference2023!', for example, contains one word: Conference2023.

Tarea

Given a string named message, convert it lowercase, then tokenize it into words using regular expression tokenization and the corresponding nltk class. A word is a sequence of only alphanumeric characters (letters and numbers). '#Conference2023!', for example, contains one word: Conference2023.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Tarea

Given a string named message, convert it lowercase, then tokenize it into words using regular expression tokenization and the corresponding nltk class. A word is a sequence of only alphanumeric characters (letters and numbers). '#Conference2023!', for example, contains one word: Conference2023.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 1. Capítulo 6

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

some-alt