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
Course 5: Text Classification
Course 6: Sequence Models and Transformers
What are transformers?: 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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