This course covers the fundamentals and some of the more advanced aspects of natural language processing (NLP). Are you ready to add NLP to your toolkit? Using the powerful NLTK package, explore the basics of text representation, cleaning, topic detection, regular expressions, and sentiment analysis before moving on to the PyTorch deep learning framework, text classification, and sequence-to-sequence models. Learn more about the transformer architectures underlying large language models (LLMs) like ChatGPT, Claude, and BERT. By the end of this course, you’ll be equipped with practical skills to leverage the extensive power of NLP tools and algorithms.
An ideal fit for data scientists with an interest in natural language processing, this course requires a working knowledge of basic algebra, calculus, and statistics, as well as basic programming experience.
This course was created by Pearson. We are pleased to host this training in our library.
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