Programming assignments and quizzes from all courses in the Coursera Natural Language Processing specialization offered by deeplearning.ai.

Instructor: Andrew Ng

The code base, quiz questions and diagrams are taken from the Natural Language Processing Specialization on Coursera, unless specified otherwise.

• 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, and

• 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.

• Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.

• Machine Translation
• Word Embeddings
• Locality-Sensitive Hashing
• Sentiment Analysis
• Vector Space Models

• a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming,
• b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics,
• c) Write a better auto-complete algorithm using an N-gram language model, and
• d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model.
• Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.

• Word2vec
• Parts-of-Speech Tagging
• N-gram Language Models
• Autocorrect

• Used recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition.

• Word Embedding
• Sentiment with Neural Nets
• Siamese Networks
• Natural Language Generation
• Named-Entity Recognition

• Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.

• Reformer Models
• Neural Machine Translation
• Chatterbot
• T5+BERT Models
• Attention Models

Natural Language Processing -DeepLearning.AI

• SUMMARY - WHAT YOU LEARNED?

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 in 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.**