This NLP course is structured to take learners from basic concepts to applied language modeling techniques. You will begin with an overview of NLP, its roadmap, and real-world use cases. The course then dives deep into text preprocessing techniques such as tokenization, cleaning, stemming, lemmatization, POS tagging, and more—essential steps for preparing text data.

You will learn text representation techniques like One-Hot Encoding, Bag of Words, and TF-IDF, understanding their advantages, limitations, and business relevance. The course further introduces word embeddings, including Word2Vec (CBOW & Skip-Gram), GloVe, cosine similarity, and hands-on practical implementations.

Finally, you will explore language modeling concepts, including N-grams, Markovian properties, smoothing techniques, evaluation metrics, and probabilistic language models (PLM). Each module includes practical examples and business use cases to bridge theory with real-world NLP applications.