README.md

Welcome to My Repo. for NLP Specialization by DeepLearning.ai

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In this course I learned all about text, how to interpret it and how to manipulate it.

I learned all about the techniques that's used in natural language processing, like:

* CBOW
* Probability models
* Sentiment Analysis
* Word Embeddings
* LSH 
* POS
* N-grams
* GRU's
* RNN's
* LSTM's
* Transformers
* Attention layers
* Siamese model
* Triplet loss
* Reformers

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The projects I've done in this course:

The basic part :

Course 1

* Words and Frequency representation
* Sentemint Analysis using Naive Bayes
* Predict relationships among words using PCA
* Naive Machine Translation and LSH

Course 2

* Auto Correct system
* Parts-of-Speech Tagging (POS)
* Language Models Auto-Complete System
* Word Embeddings and the CBOW model

The Advanced models, using Trax Framework :

Course 3

* Sentiment Analysis with Deep Neural Networks
* Deep N-grams
* Named Entity Recognition (NER)
* Question duplicates

Course 4

* Calculating the Bilingual Evaluation Understudy (BLEU) score
* Neural Machine Translation
* Transformer Summarizer
* SentencePiece and BPE
* T5 SQuAD Model using Colab
* Question Answering using T5
* BERT Loss Model using Colab
* Chatbot using trax reformer