Multilingual NLP with BERT: Applying Models to Other Languages

Language technology should not be limited to English. To build global applications, modern developers and data enthusiasts must understand how to leverage multilingual BERT (mBERT) and related models for cross-lingual tasks. This course guides you through the foundational concepts of multilingual natural language processing, explaining how BERT handles multiple languages simultaneously and how to apply these concepts to your own text processing pipelines. What you'll learn: Understand the core architecture and training principles of Multilingual BERT (mBERT); Master multilingual tokenization and how subword vocabularies span different languages; Explore zero-shot cross-lingual transfer to apply English-trained models to other languages; Compare mBERT with newer architectures like XLM-RoBERTa for diverse language tasks; Practice your knowledge with comprehensive written conceptual reviews and self-assessment exercises. You will start by learning the core terminology of multilingual NLP before diving into tokenization, training strategies, and transfer learning. The course concludes with written scenarios and review questions to solidify your understanding. Designed for beginners in NLP and machine learning, this text-based course requires no prior experience with complex deep learning frameworks. Start building your foundation in multilingual language models today.