Natural Language Processing For Text Analysis With spaCy

About this 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

The course is divided into three main parts:

Section 1-2: The course will introduce you to the primary Python concepts you need to build NLP models, including getting started with Google Colab (an online Jupyter implementation which will save the fuss of installing packages on your computers). Then the course will introduce the basic concepts underpinning NLP and the spaCy framework. By this end, you will gain familiarity with NLP theory and the spaCy architecture.

Section 3-5: These sections will focus on the most basic natural language processing concepts, such as: part-of-speech, lemmatization, stemming, named entity recognition, stop words, dependency parsing, word and sentence similarity and tokenization and their spaCy implementations.

Section 6: You will work through some practical projects to use spaCy for real-worl

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