Getting Started with Google BERT

This comprehensive course dives into Google’s BERT architecture and its revolutionary role in natural language processing (NLP). You’ll start with BERT’s architecture and pre-training methods, then explore transformer fundamentals such as encoder–decoder components, self-attention, and multi-head attention. Through hands-on coding exercises and projects, you’ll fine-tune pretrained BERT models for tasks like sentiment analysis, question answering, and named entity recognition. You’ll also work with BERT variants including ALBERT, RoBERTa, ELECTRA, DistilBERT, TinyBERT, SpanBERT, and domain-specific models like ClinicalBERT, BioBERT, and FinBERT. Finally, you’ll apply BERT to text summarization with BERTSUM, multilingual and sentence-level models, and advanced applications such as VideoBERT and BART, using libraries like Hugging Face Transformers to build real-world NLP solutions.