An Introduction to Natural Language Processing (NLP) With Python

Stanford Continuing Studies

Stanford Continuing Studies

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About the course

Most technology companies that track some level of human interaction collect and process vast amounts of textual data. How do such enterprises organize and make sense of these enormous troves of words and expressions? The term for this emerging field of machine learning is natural language processing (NLP). NLP professionals are eagerly sought after in today’s tech job market, helping companies to process large textual data sets quickly and to summarize, visualize, and digitize these data so as to reduce noise, highlight signals, and reveal insights. This course offers a comprehensive introduction to NLP terminology as well as an introduction to Python and other necessary tools of the NLP professional. Along the way, we will cover problems of text pre-processing, feature extraction, text classification, summarization, document clustering, sentiment analysis, and word vector representation. Students will develop intuition and skills for determining the correct NLP tool for the problem at hand and corresponding evaluation metrics to gauge and communicate their results to technical and nontechnical audiences. In weekly assignments, students will learn to apply NLP concepts in hands-on activities using Python, relevant libraries, and the Jupyter Notebook. Finally, each student will prepare and deliver a short presentation meant to prepare them for real-world job interviews, which will be evaluated and critiqued by the instructor and the class.

WHAT MAKES OUR ONLINE COURSES UNIQUE:

Course sizes are limited.

You won't have 5,000 classmates. This course's enrollment is capped at 26 participants.

Frequent interaction with the instructor.

You aren't expected to work through the material alone. Instructors will answer questions and interact with students on the discussion board and through weekly video meetings.

Study with a vibrant peer group.

Stanford Continuing Studies courses attract thoughtful and engaged students who take courses for the love of learning. Students in each course will exchange ideas with one another through easy-to-use message boards as well as optional weekly real-time video conferences.

Direct feedback from the instructor.

Instructors will review and offer feedback on assignment submissions. Students are not required to turn in assignments, but for those who do, their work is graded by the instructor.

Courses offer the flexibility to participate on your own schedule.

Course work is completed on a weekly basis when you have the time. You can log in and participate in the class whenever it's convenient for you. If you can’t attend the weekly video meetings, the sessions are always recorded for you and your instructor is just an email away.

Experts

Oleg Melnikov

Oleg Melnikov has taught quantitative financial risk management courses at Rice University in Python and in R. He received an MS in computer science from Georgia Institute of Technology, an MS in mathematics from UC Irvine, an MBA from UCLA, and a PhD in statistics from Rice University. Experien...

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An Introduction to Natural Language Processing (NLP) With Python at Stanford Continuing Studies

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Disclaimer

Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

Full disclaimer.

Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.

We are happy to help you find a suitable online alternative.