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.
Taught by: Anna Potapenko, ResearcherHSE Faculty of Computer Science
Taught by: Alexey Zobnin, Accosiate professorHSE Faculty of Computer Science
Taught by: Anna Kozlova, Team LeadYandex
Taught by: Sergey Yudin, Analyst-developerYandex
Taught by: Andrei Zimovnov, Senior LecturerHSE Faculty of Computer Science
Course 6 of 7 in the Advanced Machine Learning Specialization
|5 weeks of study, 4-5 hours per week
|How To Pass
|Pass all graded assignments to complete the course.
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