Natural Language Processing in Business

To learn natural language processing and its application in business. Regular expressions. Tokenization and text normalization. Part of speech tagging and grammar parsing. Extracting named entities from text. Feature engineering for text using count vector and term frequency-inverse document frequency (TF-IDF) representations of text. Mastering the art of text cleaning. Semantics and sentiment analysis. Interpreting patterns from text using laten Dirichlet allocation (LDA) and non-negative matrix factorization (NMF) topic models. Text generation with long short term memory algorithm. Creating chatbots.