This course introduces natural language processing (NLP) from scratch using Python and spaCy, one of the most popular and efficient NLP libraries. Starting with text preprocessing and tokenization, learners understand how to clean and structure raw text data for analysis. The course covers part-of-speech (POS) tagging, named entity recognition (NER), and dependency parsing, providing hands-on experience with extracting meaningful patterns from text.

Students will also learn to build simple NLP pipelines, perform text classification, and implement practical projects such as sentiment analysis and entity extraction. With real-world examples, the course emphasizes how NLP is applied in search engines, chatbots, and recommendation systems. By the end of the course, learners will have the skills to analyze, interpret, and model text data using Python and spaCy, equipping them with practical tools for careers in data science, AI, and NLP engineering.

The course is beginner-friendly, requiring basic Python knowledge only, making it accessible for anyone interested in diving into NLP and text analytics.