NLP Course is an educational curriculum that teaches natural language processing through lectures, seminars, and hands-on assignments covering foundational and modern techniques.
The course addresses the need for structured learning in NLP by progressing from classical methods to contemporary approaches. It begins with word embeddings and distributional semantics, moves through language modeling and sequence-to-sequence architectures with attention mechanisms, then covers transfer learning approaches like ELMo and BERT. The curriculum continues with large language models, prompting strategies, parameter-efficient fine-tuning methods, model efficiency techniques, and retrieval-augmented generation. Each week combines theoretical lectures with practical seminars and homework assignments that require implementing concepts from scratch or applying pre-trained models to real tasks.
This material suits students and practitioners seeking a comprehensive introduction to NLP who prefer learning through a structured sequence rather than isolated tutorials. The course emphasizes both understanding foundational concepts and gaining practical experience, with assignments ranging from building n-gram language models to fine-tuning transformer models. The progression from classical to modern methods makes it appropriate for those without deep prior NLP knowledge, while the coverage of recent techniques like RLHF and model efficiency keeps it relevant for those already familiar with basics.
The project maintains active development with materials being added as the course progresses through its iteration. The repository includes organized week-by-week folders with clear README instructions for each section, and the maintainers actively manage issues for technical problems and contribution ideas, indicating responsive engagement with users working through the materials.
