Introduction to NLP

Introduction to NLP

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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

Challenge: Tokenizing a Sentence

Tokenization Using Regular Expressions

Challenge: Tokenizing 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.

Challenge: Stemming 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

Challenge: Creating 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

Challenge: Creating Word Embeddings

Course Content

Introduction to NLP

Challenge: Tokenizing Using Regex

Task

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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.

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Section 1. Chapter 6

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Challenge: Tokenizing Using Regex

Task

Swipe to show code editor

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.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 6

toggle bottom row

Challenge: Tokenizing Using Regex

Task

Swipe to show code editor

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.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Task

Swipe to show code editor

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.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 1. Chapter 6

Switch to desktop for real-world practiceContinue from where you are using one of the options below

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