NLP in Python Crash Course Part #1 | Tokenization, Regular Expressions, Text Preprocessing & More

DataCamp · Beginner ·🧠 Large Language Models ·1y ago
Learn the foundations of Natural Language Processing (NLP) with Python in this beginner-friendly crash course. This tutorial covers key text processing techniques including tokenization, regular expressions, and text cleaning. Whether you’re new to NLP or revisiting core concepts, this session provides a practical starting point for working with textual data in Python. In this tutorial, you’ll learn: How to tokenize text using Python’s built-in tools, NLTK, and spaCy How to apply regular expressions for pattern detection in text How to preprocess text by normalizing, cleaning, and simplifying…
Watch on YouTube ↗ (saves to browser)

Chapters (13)

Introduction: NLP in Python Crash Course Overview
0:41 NLP & Regular Expressions: An Overview
1:31 Regex Fundamentals: Patterns, Wildcards & Character Classes
4:36 Tokenization Techniques & NLTK Example
10:50 Data Visualization with Matplotlib
13:25 Chapter Two: Bag of Words & Text Preprocessing
18:36 Introduction to Gensim & Tf-Idf Modeling
26:01 Named Entity Recognition (NER): Concepts & Examples
29:01 NER in Action: Spacy and Polyglot Demonstrations
34:18 Supervised Machine Learning for NLP Tasks
38:12 Building Text Classifiers with Scikit-Learn
40:54 Naive Bayes Classification & Model Evaluation
45:24 Challenges in NLP: Fake News Detection & Be

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