NLP Engineering Course (Pre-Order)

The NLP Engineering course consists of three modules that provide in-depth coverage of natural language processing, NLP linguistics, text preprocessing and normalization and semantic analysis techniques, as well as Transformer models, sentiment analysis and emotion detection, dialogue systems and machine translation and transliteration.

Each module has a set of lessons and is further supplemented with exercises and practice questions to help reinforce your understanding of key topics. Completing this course also prepares you for the official certification exam, as explained on the NLP Engineer Certification page.

Further resources are available to assist you with the completion of this course and preparation for the certification exam. These include downloadable digital course PDFs, printed course materials that can be shipped to your location, as well as coaching and instructor-led training services available by Arcitura and its training partner network. You can purchase coaching time on an hourly basis and instructor-led training workshops are available for individuals and groups.

This course is available via two separate eLearning platforms, each of which has different features and benefits. Upon clicking the Enroll button, you will be able to choose the eLearning option that works best for you.

Contact [email protected] with any questions.

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Workbook Lessons

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Video Lessons

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Reference Posters

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Interactive Exercises

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Graded Self-Test

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Completion Certificate

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Course Modules

MODULE 16 | Fundamental NLP Engineering
This course module begins with an exploration of how NLP solutions can relate to and business businesses, as well as common associated challenges and risks. The module then continues with covering essential topics, such as NLP text processing, tokenization, stemming, and lemmatization, as well as linguistic data preparation, common NLP models and libraries, and addressing bias and fairness concerns.

MODULE 17 | Advanced NLP Engineering
This course module cover advanced topics, such as contextual embeddings, attention mechanisms, transformer models, as well as sequence-to-sequence models for tasks like summarization and translation. Also covered are methods for handling linguistic subtleties, sarcasm, and ambiguity in natural language, and strategies to address challenges such as cross-lingual NLP and domain-specific language understanding.

MODULE 18 | NLP Engineering Lab
This course module provides a series of case-study driven, lab-style exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous modules. Completing this lab helps reinforce understanding of preceding topics and further demonstrates how different practices and technologies can be applied together as part of greater solutions.