What you would learn in Essentials for Natural Language Processing course?
In this class, you will be introduced to the basics of natural language processing and how to create a trained and pre-trained model. Learn how to use NLP libraries like Huggingface transformers, Tensorflow Hub and Textblob.
Also, you will begin creating basic models for speech and text analysis.
Natural Language Processing
Before the 1980s, most natural processing systems for language were built on a complicated set of handwritten rules. In the latter half of the 1980s, however, there was a paradigm shift in natural language processing, with the introduction of machine-learning algorithms for processing language. The reason for this was the constant growth in the computational power of computers thanks to Moore's Law and the gradual decrease in the influence of Chomskyan theories of language (e.g., the transformational grammar) with their philosophical foundations that disapproved of the kind of corpus linguistics, which is the basis of the machine-learning model of processing of language.
Neural networks
The most prevalent techniques are words embedded to obtain word semantics and a rise in the end-to-end learning of higher-level tasks (e.g., answering questions) instead of using a pipeline that performs independent interrelated tasks (e.g., Part-of-speech tagging and dependency processing). In specific fields, the shift has brought about significant changes to the way NLP methods are built so that deep neural networks-based methods could be considered an entirely different approach from the natural statistical processing of language. For instance, "NMT" refers to neural machine translation (NMT) is a reference to the importance of deep learning-based methods for machine translation can learn transformations from sequence to sequence, eliminating the necessity for intermediate steps like the alignment of words and modeling language employed for the statistical machine translation (SMT).
Course Content:
- Natural Language Processing: Token Tagging, Stemming, and Tagging.
- NLP Modelling and Testing.
- Transformers-Hugging face.
- Textblob
- Tensorflowhub
- Text Analysis using Natural Language Processing.
- Speech Analysis with Natural Language Processing.
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