
Natural Language Processing
Course Description
This course is an introduction to practical applications of Natural Language Processing, focusing on real-world rather than algorithm development. This course is intended for learners with enough practical knowledge of their field of expertise with regard to their specific applications, and an awareness of the limitations of their specific domains/technology.
This is an asynchronous, self-paced course with no live instructor. The flexible schedule allows learners to complete coursework and assignments at their own pace and time.
Note: Learners will be awarded a course completion certificate upon submitting the five module quizzes with an 80% completion score.
Outcomes
At the end of this course, you should be able to:
- Describe the capabilities of existing NLP systems.
- Analyze the gap that exists between a stated scenario and the existing capabilities of NLP systems.
- Test solutions by measuring improvements introduced by NLP systems.
Course Schedule and Duration
It is recommended that you work through one module per week. The entire course is estimated to take 15.5 hours to complete. You should complete the modules in order. Complete one module per week.
Module |
Topic & Readings |
1 |
History of Natural Language Processing Words vs. Concepts & Explicit vs. Implicit General Domain & Specific Domain |
2 |
Shallow NNs Contextual Embeddings LM Capabilities |
3 |
Testing in General Factual correctness and Reasoning Intro to Prompt Learning & Engineering |
4 |
Capstone Reflection Projection |

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