NLP Demystified” is a complete course designed to guide learners from the fundamentals of Natural Language Processing (NLP) to advanced applications. The course begins with an introduction to NLP and its real-world applications, followed by text tokenization and basic preprocessing techniques such as case-folding, stop words removal, stemming, and lemmatization. Advanced preprocessing concepts like part-of-speech tagging, entity tagging, and parsing are then introduced to prepare text for computational analysis. Students learn foundational NLP techniques including Bag-of-Words, document similarity measures, and TF-IDF for text representation and simple document search. The course also covers machine learning modeling basics, including bias, variance, and evaluation, before diving into practical applications like text classification using Naive Bayes with precision and recall metrics. The final sections focus on unsupervised learning with Latent Dirichlet Allocation (LDA) for automatic topic discovery in large document collections. Through detailed explanations, hands-on examples, and real-world NLP tasks, learners develop a strong understanding of how NLP works and gain the practical skills needed to implement NLP pipelines in AI, machine learning, and data science projects. This course is ideal for beginners, developers, and data enthusiasts aiming to master NLP