Introduction to TF-IDF Vectorization in Python

Introduction to Natural Language ProcessingIntroduction to TF-IDF Vectorization in Python

Venture into the world of text vectorization with a focus on TF-IDF (Term Frequency-Inverse Document Frequency) in Python. Through this course, you'll learn how to convert text into numerical features that machine learning models can work with. Using the SMS Spam Collection dataset, you will understand how to apply TF-IDF to prepare text data for predictive modeling.

Lessons and practices

Lesson 1: Transforming Text into Insights: An Introduction to TF-IDF Vectorization in Python

Running TF-IDF Vectorization

Debugging TF-IDF Vectorization

Transforming Messages to TF-IDF vectors

Implementing Complete TF-IDF Vectorization Pipeline

Lesson 2: Navigating the Weights of Words: Analyzing TF-IDF Scores in NLP

Identifying High-Impact Words with TF-IDF Vectorization

Unveiling Other Significant Words

Debugging the TF-IDF Vectorizer

Discovering Top TF-IDF Terms

Mastering TF-IDF Feature Extraction