Natural Language Processing Specialization

Natural Language Processing Specialization

What you will learn from this course

  • Use logistic regression, naive 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 network in TensorFlow & Trax 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
  • Perform sentiment analysis of tweets using logistic regression and then naïve Bayes
  • Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships
  • Write a simple English to French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbor search.
  • Create a simple auto-correct algorithm using minimum edit distance and dynamic programming
  • Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is important for computational linguistics
  • Write a better auto-complete algorithm using an N-gram language model
  • Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model
  • Build a Transformer model to summarize text
  • Build a chatbot using a Reformer model

By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future.

How to Enroll Natural Language Processing Specialization course?

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