This Natural Language Processing (NLP) course using spaCy and Python is a complete beginner-friendly guide to understanding how modern NLP systems work in real-world applications. The course starts with an introduction to NLP concepts and explains how text data is processed using Python libraries.

Learners will explore spaCy, one of the most powerful NLP libraries, and understand how to install and use it for different tasks. The course covers important topics such as linguistic annotations, tokenization, and text preprocessing, which are essential for transforming raw text into structured data.

It also dives into advanced NLP techniques including Named Entity Recognition (NER), word vectors, and spaCy pipelines. These concepts help machines understand relationships between words and extract meaningful information from text.

Additionally, learners will work with EntityRuler, Matcher patterns, custom pipeline components, and regular expressions to build more flexible and intelligent NLP systems. The course also includes practical applications such as financial text analysis and real-world NLP problem-solving.

By the end of this course, learners will be able to build complete NLP pipelines using spaCy and Python and apply them to tasks like entity recognition, text classification, and information extraction.