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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