This Natural Language Processing (NLP) course provides a clear and structured journey from basic language understanding to modern deep learning models used in AI today. It starts by explaining how machines process language, covering essential linguistic concepts that help computers interpret human text effectively.
The course then introduces foundational text processing techniques such as tokenization, n-grams, and vector representations like Bag of Words (BoW) and TF-IDF. These methods are crucial for converting text into machine-readable formats.
Next, you will explore word embeddings such as Word2Vec and GloVe, which help machines understand semantic meaning and relationships between words. The course also explains sequence models like RNNs, LSTMs, and GRUs, which are designed to handle sequential language data.
Finally, it covers advanced deep learning innovations including attention mechanisms and transformers, explaining how modern models like BERT and GPT revolutionized NLP.
This course is perfect for beginners and intermediate learners who want to understand how natural language processing evolved into today’s powerful AI systems. By the end, you will have a strong conceptual understanding of both classical NLP technique
