August 2018
Intermediate to advanced
438 pages
12h 3m
English
Content preview from Hands-On Transfer Learning with Python
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Text preprocessing
Textual data requires careful and diligent preprocessing before any feature extraction/engineering can be performed. There are various steps involved in preprocessing textual data. The following is a list of some of the most widely used preprocessing steps for textual data:
- Tokenization
- Lowercasing
- Removal of special characters
- Contraction expansions
- Stopword removal
- Spell corrections
- Stemming and lemmatization
We will be covering most techniques in detail in the chapters related to use cases. For a better understanding, readers may refer to Chapter 4 and Chapter 7 of Practical Machine Learning with Python, Sarkar and their co-authors, Springer, 2017.
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