dilawarm / nlp

Natural Language Processing Specialization

Natural Language Processing

This repository contains my assignments for the Natural Language Processing Specialization offered at Coursera by DeepLearning.AI.

• Course link

• My certificate of completion

A little about NLP

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.

Courses

There are 4 courses in this specialization:

Natural Language Processing with Classification and Vector Spaces

• 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.

Natural Language Processing with Probabilistic Models

• 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.
• Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model.

Natural Language Processing with Sequence Models

• Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets.
• Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model.
• Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers.
• Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning.

Natural Language Processing with Attention Models

• Translate complete English sentences into German using an encoder-decoder attention model.
• Build a Transformer model to summarize text.
• Use T5 and BERT models to perform question-answering.
• Build a chatbot using a Reformer model.

Enjoy! 🙂