This NLP course provides a complete step-by-step journey from basic language processing to modern transformer-based AI systems. It is designed for beginners who want to understand how machines read, process, and generate human language.
You start with core NLP foundations such as text processing, linguistic principles, and n-grams. These concepts explain how raw text is broken down and structured so machines can analyze it.
Next, you explore vector representations of language, including Bag of Words (BoW), TF-IDF, and word embeddings such as Word2Vec and GloVe. These techniques convert text into numerical form, enabling machine learning models to understand meaning.
The course then moves into deep learning models for NLP, including Recurrent Neural Networks (RNNs), LSTMs, and GRUs, which help models process sequences of text.
After that, you learn about the transformer architecture, which powers modern AI systems. Topics include attention mechanisms, BERT, GPT, and how these models revolutionized natural language understanding and generation.
Advanced sections cover fine-tuning, transfer learning, model efficiency, evaluation metrics, prompt engineering, and information extraction techniques like Named Entity Recognition (NER), relation extraction, and knowledge graphs.
By the end of this course, learners will understand the full pipeline of NLP systems—from raw text to modern generative AI models used in real-world applications.
