The actual course materials are available here.

This Natural Language Processing course provides a comprehensive journey into the fascinating world of computational linguistics and text processing. The course is structured to build your understanding from fundamental concepts to cutting-edge applications in NLP.

Starting with the basics, you'll learn essential NLP concepts including tokenization, part-of-speech tagging, and syntactic parsing. You'll gain hands-on experience with popular Python libraries like NLTK and spaCy, learning how to process and analyze text data effectively. The course covers crucial topics in text preprocessing, including cleaning, normalization, and feature extraction techniques.

As you progress, you'll dive into more advanced topics such as Named Entity Recognition (NER), sentiment analysis, and topic modeling. You'll learn both traditional statistical approaches and modern deep learning techniques for these tasks. The course includes detailed sections on word embeddings, exploring models like Word2Vec and GloVe, and understanding their applications in various NLP tasks.

A significant portion of the course focuses on transformer-based architectures, which have revolutionized NLP. You'll study the inner workings of BERT, GPT, and other transformer models, learning how to fine-tune these models for specific tasks. Through practical exercises, you'll implement solutions for text classification, question answering, and text generation.

The course also covers important applications like machine translation, text summarization, and chatbot development. You'll learn about sequence-to-sequence models, attention mechanisms, and how to evaluate NLP models effectively. Real-world case studies and projects will help you understand how these technologies are applied in industry settings.

By the end of this course, you'll have developed a strong theoretical foundation in NLP concepts and gained practical experience implementing various NLP solutions. You'll be well-prepared to tackle complex text processing challenges and apply state-of-the-art NLP techniques in real-world applications.