Natural Language Processing
485 learners
Introduction 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.
Pandas
Python
Scikit-learn
4 lessons
19 practices
2 hours
Feature Engineering and Text Representation
Lessons and practices
Running TF-IDF Vectorization
Debugging TF-IDF Vectorization
Transforming Messages to TF-IDF vectors
Implementing Complete TF-IDF Vectorization Pipeline
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
Customized TF-IDF Vectorization Parameters
Updating TF-IDF Parameters for Text Vectorization
Fixing TF-IDF Vectorization Bugs
Customizing TF-IDF Vectorization Parameters
Mastering Custom TF-IDF Vectorization
Run TF-IDF With Stop Words Removal
Fixing TF-IDF Vectorization Stop Words
Customizing Stop Words for TF-IDF Vectorization
Adding Stop Words Removal to TF-IDF Vectorizer
Mastering TF-IDF Vectorization
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