Introduction to Natural Language Processing

About this course

Natural Language Processing (NLP) powers today's most innovative intelligent systems—chatbots, search engines, voice assistants, sentiment analysis tools, and cutting-edge generative AI models. This comprehensive course takes you on a complete journey from NLP fundamentals to advanced language modeling and embeddings, with a strong focus on hands-on learning and real-world applications.

You'll master essential preprocessing techniques including tokenization, stemming, lemmatization, stop-word removal, text normalization, and regular expressions. The course introduces powerful NLP resources like WordNet, NLTK, spaCy, Hugging Face, and Stanford NLP, enabling you to work confidently with industry-standard language tools and frameworks.

As you progress, you'll explore Bag-of-Words models, TF-IDF vectorization, Zipf's law, N-gram language models, Hidden Markov Models, Maximum Entropy models, and topic modeling using LDA. You'll understand graph-based approaches like TextRank for text summarization, compute text similarity using multiple metrics, and work with widely used word and sentence embeddings such as Word2Vec, GloVe, FastText, and BERT.The course culminates in building a complete sentiment analysis project, applying everything you've learned to create a practical, production-ready NLP application. Throughout your learning journey, you'll follow clear explanations, guided coding sessions, and step-by-step demonstrations that make complex concepts easier to understand and implement.

Whether you're a student, researcher, software developer, data scientist, or AI enthusiast, this course equips you with essential NLP skills, proven real-world techniques, and the confidence to build intelligent text-processing applications. Transform your understanding of language technology and unlock new career opportunities in artificial intelligence and machine learning today.

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