Natural Language Processing Specialization

• In this folder, Natural Language Processing Specialization projects and notes can be found.

WHAT I 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.

There are 4 Courses in this Specialization

Course 1 - Natural Language Processing with Classification and Vector Spaces

• In the first course of the Natural Language Processing Specialization

• I performed sentiment analysis of tweets using logistic regression and then naïve Bayes,

• I used vector space models to discover relationships between words and used PCA to reduce the dimensionality of the vector space and visualize those relationships, and

• I wrote 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.

Projects

• Sentiment Analysis of Tweets using Logistic Regression
• Sentiment Analysis of Tweets using Naïve Bayes
• Vector Space Models
• Naive Machine Translation and LSH

Course 2 - Natural Language Processing with Probabilistic Models

• In the second course of the Natural Language Processing Specialization

• I wrote a simple auto-correct algorithm using minimum edit distance and dynamic programming,

• I applied the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics,

• I wrote a better auto-complete algorithm using an N-gram language model, and

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

Projects

• Auto-correct algorithm using minimum edit distance and dynamic programming
• Parts-of-Speech Tagging (POS)
• Auto-complete algorithm using an N-gram language model
• Word Embeddings- Continuous Bag of Words (CBOW) model

Course 3 - Natural Language Processing with Sequence Models

• In the third course of the Natural Language Processing Specialization

• I trained a neural network with GLoVe word embeddings to perform sentiment analysis of tweets,

• I generated synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model,

• I trained a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and

• I used so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning.

Projects

• Sentiment Analysis with Deep Neural Networks
• Synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model
• Named Entity Recognition (NER) using LSTM
• Question duplicates using Siamese LSTM models

Course 4 - Natural Language Processing with Attention Models

• In the fourth course of the Natural Language Processing Specialization

• I translated complete English sentences into German using an encoder-decoder attention model,

• I built a Transformer model to summarize text,

• I used T5 and BERT models to perform question-answering, and

• I built a chatbot using a Reformer model.

Projects

• Neural Machine Translation using an Encoder-Decoder Attention Model
• Transformer Summarizer
• Question Answering using Text to Text Transfer from Transformers and BERT models
• Chatbot

Disclaimer

• DeepLearning.AI makes course notes available for educational purposes.
• Project solutions are just for educational purposes. I highly recommend trying and solving project/program assignments on your own.

All the best 🤘