Applied Natural Language Processing in Engineering Part 2

This course is best suited for software engineers, data scientists, and graduate students in computer science or engineering fields who wish to develop expertise in building and deploying natural language processing systems to solve real-world language understanding challenges. You will master core NLP tasks such as Part-of-Speech tagging, Named Entity Recognition, sentiment analysis, and Neural Machine Translation while implementing various neural architectures from Recurrent Neural Networks and bidirectional RNNs to Conditional Random Fields and state-of-the-art transformer models. The course emphasizes practical application through extensive laboratory work and projects, where you will develop complete NLP pipelines using frameworks like PyTorch and Hugging Face, learning to preprocess data, train models, and evaluate performance using industry-standard metrics. By the end of the course, you will be equipped with both theoretical understanding and practical skills to design, implement, and optimize NLP solutions for real-world engineering applications, from chatbots and translation systems to information extraction and text analysis tools. The curriculum culminates in a comprehensive capstone project where you will apply multiple techniques learned throughout the course to solve a complex language processing challenge. You will be equipped with both theoretical knowledge to tackle complex language processing problems in industry settings, enabling you to build production-ready NLP applications that can understand, interpret, and generate human language effectively.