You're reading from AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition

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Published inFeb 2024

PublisherPackt

ISBN-139781835082201

Edition2nd Edition

Concepts

Machine Learning

Authors (2):

Somanath Nanda

Weslley Moura

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Dealing with text data

You have already learned how to transform categorical features into numerical representations, either using label encoders, ordinal encoders, or one-hot encoding. However, what if you have fields containing long pieces of text in your dataset? How are you supposed to provide a mathematical representation for them in order to properly feed ML algorithms? This is a common issue in Natural Language Processing (NLP), a subfield of AI.

NLP models aim to extract knowledge from texts; for example, translating text between languages, identifying entities in a corpus of text (also known as Name Entity Recognition, or NER for short), classifying sentiments from a user review, and many other applications.

Important note

In Chapter 8, AWS Application Services for AI/ML, you will learn about some AWS application services that apply NLP to their solutions, such as Amazon Translate and Amazon Comprehend. During the exam, you might be asked to think about the fastest...

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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition

Published in: Feb 2024Publisher: PacktISBN-13: 9781835082201

Authors (2)

Somanath Nanda

Somanath has 10 years of working experience in IT industry which includes Prod development, Devops, Design and architect products from end to end. He has also worked at AWS as a Big Data Engineer for about 2 years.
Read more about Somanath Nanda

Weslley Moura

Weslley Moura has been developing data products for the past decade. At his recent roles, he has been influencing data strategy and leading data teams into the urban logistics and blockchain industries.
Read more about Weslley Moura

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Dealing with text data

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