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

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 language
How to prepare text data for downstream NLP and machine learning tasks

🧠 What You’ll Learn in This Course:
Introduction to NLP: Understand what Natural Language Processing is, why it’s useful, and where it’s applied in the real world
Tokenization Techniques: Learn multiple approaches for breaking text into smaller units, including word and sentence tokenization
Regular Expressions: Use Python’s re module to extract text patterns such as hashtags, dates, and emails
Text Preprocessing Steps: Perform basic text normalization, remove stopwords, apply stemming and lemmatization, and explore how tools like spaCy streamline these steps

📕 Video Highlights
00:00:00 – Introduction: NLP in Python Crash Course Overview
00:00:41 – NLP & Regular Expressions: An Overview
00:01:31 – Regex Fundamentals: Patterns, Wildcards & Character Classes
00:04:36 – Tokenization Techniques & NLTK Example
00:10:50 – Data Visualization with Matplotlib
00:13:25 – Chapter Two: Bag of Words & Text Preprocessing
00:18:36 – Introduction to Gensim & Tf-Idf Modeling
00:26:01 – Named Entity Recognition (NER): Concepts & Examples
00:29:01 – NER in Action: Spacy and Polyglot Demonstrations
00:34:18 – Supervised Machine Learning for NLP Tasks
00:38:12 – Building Text Classifiers with Scikit-Learn
00:40:54 – Naive Bayes Classification & Model Evaluation
00:45:24 – Challenges in NLP: Fake News Detection & Beyond
00:48:05 – Sentiment Analysis: Concepts, Applications & Data Exploration
00:56:01 – TextBlob for Sentiment: Polarity and Subjectivity
00:56:30 – Creating Word Clouds in Python
01:00:37 – Transforming Text Data: Bag of Words & N-Grams
01:05:17 – N-Gram Features & Vocabulary Management
01:09:05 – Feature Engineering: Token Counts & Language Detection
01:12:17 – Filtering Techniques: Stopwords and Regex Refinements
01:24:09 – Stemming vs. Lemmatization: Reducing Words to Roots
01:28:33 – Tf-Idf Vectorization for Text Analysis
01:32:34 – Supervised Classification: Logistic Regression Basics
01:38:03 – Model Evaluation & Regularization Techniques
01:44:25 – Course Summary & Final Remarks
01:48:36 – Closing Remarks & Next Steps

🖇️ Resources & Documentation
Take the full NLP skill track on DataCamp: https://www.datacamp.com/tracks/natur...
Introduction to NLP with Python: https://www.datacamp.com/courses/intr...
Regular Expressions for Pattern Matching: https://www.datacamp.com/courses/regu...
Text Preprocessing with NLTK & spaCy: https://www.datacamp.com/tutorial/tex...

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#NLP #TextPreprocessing #PythonNLP #Tokenization #Regex #NLTK #spaCy #DataScience #MachineLearning #NaturalLanguageProcessing