Hands-On NLP with Python: Building Text Classifiers and Summarizers

Text data is everywhere, from social media posts to customer reviews, but turning unstructured language into actionable insights requires specialized skills. Python provides the perfect ecosystem to clean, analyze, and extract meaning from written text efficiently. In this text-based course, you will transition from understanding basic text processing to building functional natural language processing applications. You will learn how to process raw language data, analyze textual patterns, and build models that can automatically classify sentiments and summarize long articles. What you'll learn: - Understand foundational NLP concepts, including tokenization, stop-word removal, stemming, and lemmatization. - Build a text classification model to perform sentiment analysis on real-world social media data. - Create an automatic article summarizer that extracts key information from web-based text. - Apply modern NLP workflows using industry-standard libraries like spaCy, NLTK, and Hugging Face transformers. - Clean and preprocess noisy text data using regular expressions and modern Python text processing techniques. - Implement text vectorization techniques such as TF-IDF and word embeddings to represent text numerically. The course starts with essential linguistic concepts and basic text cleaning before guiding you through step-by-step explanations and code snippets to construct fully working classification and summarization models. This course is designed for beginners to NLP and data science. Basic familiarity with Python programming is the only prerequisite. Start reading today to unlock the power of text analytics and build your first natural language processing applications.