Text Data Preprocessing in Python
Learn to clean and prepare textual data for machine learning models using Python. This course teaches you to apply basic preprocessing tasks such as text lowercasing, removing stopwords, tokenization, and stemming on the SMS Spam Collection dataset. By the end of this course, you’ll have the skills to transform raw text into a format that's ready for NLP tasks.
Lessons and practices
Lesson 1: Lowercasing Text for Uniformity in NLP
Introduction to Lowercase Text Conversion
Lowercasing Spam Dataset Messages
Transforming Text to Lowercase for Data Uniformity
Mastering Text Lowercasing in Python
Lesson 2: Punctuating Punctuation: Streamlining Text for NLP
Removing Text Punctuation Simplified
Removing Commas from Text
Debugging Punctuation Removal Exercise
Crafting Clean Text Data
Lesson 3: Tokenizing Text Data in NLP with Python and NLTK
Efficient Text Preprocessing with NLTK
Streamlining Text Processing with NLTK
Implementing Tokenization Basics
Mastering Tokenization with NLTK
Lesson 4: Demystifying Stop Words in Natural Language Processing
Stop Words Demystified in NLP
Adapting Stop Words Removal for Spanish
Debugging Stop Words Removal
Setting the Stage for Stop Words Removal in Text Data
Mastering Stop Words Removal
Lesson 5: Mastering Stemming in NLP with NLTK
Putting Stemming into Action
Debugging Data Preprocessing Steps
Applying Stemming to Normalize Text
Mastering Text Preprocessing Techniques
Interested in this course? Learn and practice with Cosmo!
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