Natural Language Processing For Text Analysis With spaCy
Learn step-by-step Natural Language Processing (NLP) in Python using spCY! Work on practical NLP Projects!
Created by Minerva Singh | 2.5 hours on-demand video course
Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) to enable computers to comprehend spoken and written human language. NLP has several applications, including text-to-voice and speech-to-text conversion, chatbots, automatic question-and-answer systems (Q&A), automatic image description creation, and video subtitles. With the introduction of ChatGPT, NLP will become more and more popular, potentially leading to increased employment opportunities in this branch of AI. The SpaCy framework is the workhorse of the Python NLP ecosystem owing to (a) its ability to process large text datasets, (b) information extraction, (c) pre-processing text for subsequent use in AI models, and (d) Developing production-level NLP applications.
IF YOU ARE A NEWCOMER TO NLP, ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT NATURAL LANGUAGE PROCESSING (NLP) AND TO DEVELOP NLP MODELS USING SPACY
What you’ll learn
- Understand the basic concepts of natural language processing, including: part-of-speech, lemmatization, stemming, named entity recognition, and stop words
- Implement text summarisation and keyword search
- Understand more advanced concepts, such as: dependency parsing, tokenization, word and sentence similarity
- Implement text summarisation and keyword search
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