NLP: Tokenization, Stop Word Removal, Stemming, Lemmatization & Vectorization with Sklearn

Опубликовано: 04 Февраль 2026
на канале: Code With Sachin Sirohi
267
1

In this video, we dive deep into essential Natural Language Processing (NLP) techniques using Python and Sklearn. Learn how to perform Tokenization, remove Stop Words, and understand the differences between Stemming and Lemmatization. We also explore how to convert text into numerical data using Count Vectorization and TF-IDF Vectorization with Sklearn. Whether you're a beginner or an experienced developer, this tutorial will help you master key NLP concepts.

📌 Topics Covered:

What is Tokenization?
Removing Stop Words
Stemming vs. Lemmatization
Vectorization using Count Vectorizer & TF-IDF
Practical implementation with Python & Sklearn
💻 Code & Resources: [Link to code or resources if available]

👉 Don't forget to like, share, and subscribe for more tutorials!

#NLP #NaturalLanguageProcessing #Tokenization #Stemming #Lemmatization #StopWords #CountVectorizer #Sklearn #MachineLearning #DataScience #PythonProgramming #TextProcessing