Overview
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Explore the fundamentals of Natural Language Processing (NLP) and Topic Modeling in this 30-minute introductory video from Open Data Science. Gain insights into the growing demand for NLP experts and recent advancements in the field. Learn essential concepts and techniques for analyzing text data, including word vectors and topic modeling methodologies. Discover basic NLP modeling approaches, pre-processing techniques, and popular tools like Regular Expressions, Spacy, and NLTK. Delve into key processes such as stemming, lemmatization, and Part of Speech tagging. Examine advanced topics like TF vectorization, word embeddings, and Latent Dirichlet Analysis. By the end of this session, acquire a solid foundation in NLP and topic modeling, preparing you for more advanced applications in various business contexts.
Syllabus
Intro
Terminology: NLU vs. NLP vs. ASR
Applications of NLP
NLP Basics: Pre-processing
NLP Tools - Regular Expression IV
NLP Tools - Spacy vs. NLTK
Stemming
Lemmatization
Stopwords
Part of Speech (POS) Tagging
Terminology-Corpus
TF Vectorization !
TF Vectorization II - sklearn
Word Embedding - Learning • The basic idea of learning neural network word embeddings
FastText-gensim
Latent Dirichlet Analysis (LDA)
Contextualized Topic Models
Taught by
Open Data Science