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Classifying the natural languages is one of the huge challenges in the modern day NLP. It involves the techniques that can effectively distinguish between the target text and normal text. Other services like chatbots are also heavily dependent on the incoming text from user. They need to process a tons of data in order to determine the user need and guide through the right path.
This video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and more. Python, NLTK, & Jupyter Notebook are used to demonstrate the concepts.
In this video we are going to learn about Python Natural Language Processing (NLP) in 2 Hours. there are different topics that we are going to cover in this video like tokenization, stemming, lemmatization, parts of speech tagging, named entity recognition, sentiment analysis, language translation and many more. Python Natural Language Processing (NLP) in 2 Hours
Natural language processing (NLP) is a specialized field for analysis and generation of human languages. Human languages, rightly called natural language, are highly context-sensitive and often ambiguous in order to produce a distinct meaning. (Remember the joke where the wife asks the husband to "get a carton of milk and if they have eggs, get six," so he gets six cartons of milk because they had eggs.) NLP provides the ability to comprehend natural language input and produce natural language output appropriately.
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Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge.NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition