This Natural Language Processing (NLP) tutorial covers core basics of NLP using the well–known Python package Natural Language Toolkit (NLTK). The course helps trainees become familiar with common concepts like tokens, tokenization, stemming, lemmatization, and using regex for tokenization or for stemming. It discusses classification, tagging, normalization of our input or raw text. It also covers some machine learning algorithms such as Naive Bayes.
After taking this course, you will be familiar with the basic terminologies and concepts of Natural Language Processing (NLP) and you should be able to develop NLP applications using the knowledge you gained in this course.
What is Natural Language Processing (NLP)?
Natural language processing, or NLP for short, is the ability of a computer program to understand, manipulate, analyze, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, topic segmentation, and spam detection.
What is NLTK?
The Natural Language Toolkit (NLTK) is a suite of program modules and data–sets for text analysis, covering symbolic and statistical Natural Language Processing (NLP). NLTK is written in Python. Over the past few years, NLTK has become popular in teaching and research.
Courses : 4
Specification: NLTK: Build Document Classifier & Spell Checker with Python
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