This NLP Demystified course provides a complete and structured journey through Natural Language Processing, starting from the fundamentals and progressing to advanced deep learning and transformer-based models. It is designed to help learners understand both the intuition and technical foundations behind modern NLP systems.
The course begins with the basics of text processing, including tokenization and preprocessing techniques such as case folding, stop word removal, stemming, and lemmatization. It then moves into advanced preprocessing methods like part-of-speech tagging, entity recognition, and syntactic parsing.
Learners are introduced to classical NLP representations such as Bag-of-Words and TF-IDF, along with document similarity and information retrieval techniques. The course also explains how machine learning models are built, including concepts like bias, variance, and model evaluation.
It continues with text classification using Naive Bayes and performance metrics such as precision and recall. Later sections introduce neural networks from scratch, word embeddings, and deep learning training techniques.
Advanced topics include recurrent neural networks, sequence-to-sequence models, attention mechanisms, and machine translation. The course concludes with transformers, pre-training, and transfer learning using models like BERT and GPT, which power modern AI systems.
