NLP Tutorial for Machine Learning

Natural Language Processing (NLP) consists of developing applications and services capable of understanding human languages. Some practical examples of NLP are speech recognition, for example, Google voice search, understanding content or analyzing feelings, etc.

Natural Language Processing (NLP) consists of developing applications and services capable of understanding human languages. Some practical examples of NLP are speech recognition, for example, Google voice search, understanding content or analyzing feelings, etc.

Use of Natural Language Processing (NLP) in Machine Learning

Nowadays, most of us have smartphones with voice recognition. These smartphones use NLP to understand what is being said. Besides, many people use laptops whose operating system has built-in speech recognition like Cortana.

Machine Learning Jobs

In this article, I will take you through some very common uses of Natural Language Processing (NLP), by practising these applications of NLP with Machine Learning you can easily master yourself with working on the tasks of NLP.

Named Entity Recognition (NER)

Named entity means anything that is a real-world object such as a person, place, organization, product that has a name. For example — “My name is Aman and I and a machine learning trainer”. In this sentence, the name “Aman”, the field or subject “Machine Learning” and the profession “Trainer” are named entities.

In machine learning, named entity recognition (NER) is a task of natural language processing (NLP) to identify named entities in a certain piece of text. You can learn the practical implementation of Named Entity Recognition from here.

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