Machine Learning with Scikit-Learn

11.

Working with Text Data

11.1.

Text Preprocessing

11.1.1.

11.1.1.1.

Removing HTML Tags

11.1.1.2.

Handling Special Characters

11.1.1.3.

Unicode Normalization

11.1.2.

11.1.2.1.

Word Tokenization

11.1.2.2.

Sentence Tokenization

11.1.3.

Text Normalization

11.1.3.1.

11.1.3.2.

11.1.3.3.

11.1.4.

Stop Word Removal

11.1.4.1.

Standard Stop Words

11.1.4.2.

Custom Stop Words

11.2.

Feature Extraction from Text

11.2.1.

Bag of Words Model

11.2.1.1.

CountVectorizer

11.2.1.1.1.

Vocabulary Building

11.2.1.1.2.

11.2.1.1.3.

11.2.1.1.4.

Binary Occurrence

11.2.2.

TF-IDF Vectorization

11.2.2.1.

11.2.2.2.

Inverse Document Frequency

11.2.2.3.

TfidfVectorizer

11.2.2.4.

TfidfTransformer

11.2.3.

Hashing Vectorizer

11.2.3.1.

Memory Efficiency

11.2.3.2.

Hash Collisions

11.2.4.

Character-level Features

11.2.4.1.

Character N-grams

11.3.

Text Classification

11.3.1.

Document Classification Pipeline

11.3.2.

Feature Selection for Text

11.3.3.

Handling Large Vocabularies

11.3.4.

Sparse Matrix Operations

11.4.

Text Clustering

11.4.1.

Document Clustering

11.4.2.

Topic Modeling Preparation

11.5.

Advanced Text Processing

11.5.1.

Handling Multiple Languages

11.5.2.

Custom Tokenizers

11.5.3.

Feature Hashing

Previous

10. Building Machine Learning Pipelines

Go to top

Next

12. Advanced Topics