Natural Language Processing
Master Natural Language Processing from core theoretical foundations to practical industry applications.
Core modules and technical concepts for Natural Language Processing.
Topic 1.1: Text Preprocessing and Classical Representations
Foundational and applied principles of Text Preprocessing and Classical Representations.
Tokenization, Stemming, Lemmatization, and Stop Words
Comprehensive technical guide and practical walkthrough of Tokenization, Stemming, Lemmatization, and Stop Words.
Vector Space Models: Bag-of-Words, TF-IDF, and Word2Vec
Comprehensive technical guide and practical walkthrough of Vector Space Models: Bag-of-Words, TF-IDF, and Word2Vec.
Topic 1.2: Modern Deep NLP Architectures
Foundational and applied principles of Modern Deep NLP Architectures.
Recurrent Neural Networks (RNNs) and LSTMs for Text
Comprehensive technical guide and practical walkthrough of Recurrent Neural Networks (RNNs) and LSTMs for Text.
The Transformer Architecture, Self-Attention, and BERT/GPT
Comprehensive technical guide and practical walkthrough of The Transformer Architecture, Self-Attention, and BERT/GPT.