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Applied Natural Language Processing in Engineering Part 2

Ramin Mohammadi
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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.

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What's inside

Syllabus

Tokenization
This module delves into the critical preprocessing step of tokenization in NLP, where text is segmented into smaller units called tokens. You will explore various tokenization techniques, including character-based, word-level, Byte Pair Encoding (BPE), WordPiece, and Unigram tokenization. Then you’ll examine the importance of normalization and pre-tokenization processes to ensure text uniformity and improve tokenization accuracy. Through practical examples and hands-on exercises, students will learn to handle out-of-vocabulary (OOV) issues, manage large vocabularies efficiently, and understand the computational complexities involved. By the end of the module, you will be equipped with the knowledge to implement and optimize tokenization methods for diverse NLP applications.
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Learners who complete Applied Natural Language Processing in Engineering Part 2 will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Applied Natural Language Processing in Engineering Part 2.
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Basic Concepts and Models for Interpreter and...
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Neural Machine Translation
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Deep Learning for Natural Language Processing
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Generative AI with Python and PyTorch, Second...
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Deep Learning for Coders with fastai and PyTorch
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Natural Language Processing with PyTorch
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Python Deep Learning
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Course image
Via
Coursera
Institution
Northeastern University
Instructor
Ramin Mohammadi
Session
May 4, 2026 - June 29, 2026
Language
English
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