Natural Language Processing - Formula Sheet
Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.
Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.
Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.
Use recurrent neural networks, LSTMs, GRUs & Siamese networks for sentiment analysis, text generation & named entity recognition.
Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.
- Natural Language Processing with Classification and Vector Spaces.
- Natural Language Processing with Probabilistic Models.
- Natural Language Processing with Sequence Models.
- Natural Language Processing with Attention Models.
Credential Id: KU3MTJ3Q2NY9
Certificate Link: https://www.coursera.org/account/accomplishments/specialization/KU3MTJ3Q2NY9
(Certificate earned on December 7, 2020)