Natural Language Processing

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

( 110 Reviews )

Created by: Anna Potapenko

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 88 / 100

Course Description

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.Do you have technical problems? Write to us: coursera@hse.ru

Time: 34h

Instructor Details

Anna Potapenko

4.4 Rating
110 Reviews

Anna Potapenko graduated from Moscow State University with majors in Computer Science and Machine Learning. Now she is doing her PhD in natural language processing, particularly interested in learning semantic representations of words and documents. Anna was also working at Yandex and twice interning in Google, where she conducted research on deep neural networks and reinforcement learning for dialogue systems.

Reviews

4.4

110 total reviews

5 star 4 star 3 star 2 star 1 star

Write a Review

By Alexander G on 9-Dec-18

Content is so good. Cheers for the makers.

By kripa s on 19-Apr-19

The course is very nice. I wish there were some more examples in slides to understand the working of algorithms. Maybe its advance that's why I felt it.If notes of every week were available then it would have been very beneficial.

By Eyob on 31-Mar-19

I have the best experience ever with the learning methodologies.all over it was quite awesome.

By Guillermo K on 29-Mar-19

The assignments are great!

By Weichi C on 2-Mar-18

Excellent Course.

By david w on 28-May-18

That course is Amazing! I love it.

By Jordi a on 15-Apr-18

I really recommend this course. They cover latest papers in NLP as well as both statistical and neural approaches to current NLP problems. There are also assignments where you can apply what you learned in practice.

By BogdanC on 17-May-18

nicely organized! amazing course. I am doing my PHD in NLP, and I had prior NLP classes in coursera, but I still can learn quite a lot knew things from this course. It gives NLP from another perspective, and it is really up-to-date with deep learning and tensor flow. Love such classes. Hope there are more classes offered from these instructors.

By Marc m on 3-Mar-18