[Coursera] Natural Language Processing

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About this course: This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection. Throughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. For example, we will discuss word alignment models in machine translation and see how similar it is to attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks. Some materials are based on one-month-old papers and introduce you to the very state-of-the-art in NLP research.

Who is this class for: This course is for those who are interested in NLP field and want to know the current state-of-the-art in research and production. We expect that you have already taken some courses on machine learning and deep learning, but probably have never applied those models to texts and want to get a quick start.

Created by:  National Research University Higher School of Economics
National Research University Higher School of Economics
  • Taught by:  Anna Potapenko, Researcher

    HSE Faculty of Computer Science
  • Taught by:  Alexey Zobnin, Accosiate professor

    HSE Faculty of Computer Science
  • Taught by:  Anna Kozlova, Team Lead

  • Taught by:  Sergey Yudin, Analyst-developer

  • Taught by:  Andrei Zimovnov, Senior Lecturer

    HSE Faculty of Computer Science
Basic Info
Course 6 of 7 in the Advanced Machine Learning Specialization
Level Advanced
Commitment 5 weeks of study, 4-5 hours per week
How To Pass Pass all graded assignments to complete the course.
User Ratings
4.7 stars
Average User Rating 4.7See what learners said

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