Natural Language Processing

Course 1: Introduction to Natural Language Processing

Introduction to Natural Language Processing : What is NLP ? What is it used for ?

Text Preprocessing : Preprocessing in Natural Language Processing (NLP) is the process by which we try to “standardize” the text we want to analyze.

Course 2: Core NLP Libraries

Introduction to NLTK: Todo

Introduction to SpaCy: Todo

Course 3: Text Embedding

Text Embedding with Bag-Of-Words and TF-IDF : In order to analyze text and run algorithms on it, we need to embed the text. The notion of embedding simply means that we’ll conver the input text into a set of numerical vectors that can be used into algorithms. In this article, we’ll cover BOW and TF-IDF, two simple techniques for embedding.

Text Embedding with Word2Vec : A deeper dive into the state of the art embedding technique : Word2Vec.

Data Augmentation in NLP : Details of the implementation of “Easy Data Augmentation” paper.

Course 4: Text Similarity

What is text similarity used for?: Todo

Similarity metrics and implementation: Todo

Course 5: Text Classification

Classical methods for text classification: Todo

Deep Learning Methods for text classification: Todo

Course 6: Sequence Models and Transformers

What are sequence models?: Todo

What are transformers?: Todo

Introduction to Hugging Face’s library: Todo

All codes and exercises are accessible on this repo. Don’t hesitate to show your suppot and star the repo:

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