This repository contains my personal solutions and learning progress for the Natural Language Processing Specialization on Coursera, taught by
Łukasz Kaiser, Eddy Shyu, Younes Bensouda Mourri and offered by DeepLearning.AI.
- Performed sentiment analysis on tweets using logistic regression and Naïve Bayes classifiers.
- Built vector space models to explore word relationships.
- Used PCA for dimensionality reduction and visualization of embeddings.
- Implemented an English-to-French translation using pre-trained word embeddings and LSH for approximate k-NN.
- Built a spell corrector using minimum edit distance and dynamic programming.
- Performed part-of-speech tagging with the Viterbi algorithm.
- Created an autocomplete model using N-gram language modeling.
- Developed Word2Vec from scratch using the CBOW model.
- Built sentiment classifiers using word embeddings and RNNs.
- Generated text in Shakespeare’s style using GRUs.
- Trained LSTMs for Named Entity Recognition.
- Built Siamese LSTM networks to detect semantically equivalent questions.
- Developed English-to-Portuguese translation using encoder-decoder with attention.
- Built a Transformer-based text summarization model.
- Applied pretrained T5 and BERT models for question answering.
📁 Certificates can be found in the Certificates/ folder.
📝 This repository is intended for educational purposes and personal reference only. Please do not copy and submit these solutions as your own.