Informações sobre o curso
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Nível avançado

Aprox. 33 horas para completar

Sugerido: 5 weeks of study, 4-5 hours per week...


Legendas: Inglês

Habilidades que você terá

ChatterbotTensorflowDeep LearningNatural Language Processing

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível avançado

Aprox. 33 horas para completar

Sugerido: 5 weeks of study, 4-5 hours per week...


Legendas: Inglês

Programa - O que você aprenderá com este curso

5 horas para concluir

Intro and text classification

In this module we will have two parts: first, a broad overview of NLP area and our course goals, and second, a text classification task. It is probably the most popular task that you would deal with in real life. It could be news flows classification, sentiment analysis, spam filtering, etc. You will learn how to go from raw texts to predicted classes both with traditional methods (e.g. linear classifiers) and deep learning techniques (e.g. Convolutional Neural Nets).

11 vídeos ((Total 114 mín.)), 3 leituras, 3 testes
11 videos
Welcome video5min
Main approaches in NLP7min
Brief overview of the next weeks7min
[Optional] Linguistic knowledge in NLP10min
Text preprocessing14min
Feature extraction from text14min
Linear models for sentiment analysis10min
Hashing trick in spam filtering17min
Neural networks for words14min
Neural networks for characters8min
3 leituras
Prerequisites check-list2min
Hardware for the course5min
Getting started with practical assignments20min
2 exercícios práticos
Classical text mining10min
Simple neural networks for text10min
5 horas para concluir

Language modeling and sequence tagging

In this module we will treat texts as sequences of words. You will learn how to predict next words given some previous words. This task is called language modeling and it is used for suggests in search, machine translation, chat-bots, etc. Also you will learn how to predict a sequence of tags for a sequence of words. It could be used to determine part-of-speech tags, named entities or any other tags, e.g. ORIG and DEST in "flights from Moscow to Zurich" query. We will cover methods based on probabilistic graphical models and deep learning.

8 vídeos ((Total 84 mín.)), 2 leituras, 3 testes
8 videos
Perplexity: is our model surprised with a real text?8min
Smoothing: what if we see new n-grams?7min
Hidden Markov Models13min
Viterbi algorithm: what are the most probable tags?11min
MEMMs, CRFs and other sequential models for Named Entity Recognition11min
Neural Language Models9min
Whether you need to predict a next word or a label - LSTM is here to help!11min
2 leituras
Perplexity computation10min
Probabilities of tag sequences in HMMs20min
2 exercícios práticos
Language modeling15min
Sequence tagging with probabilistic models20min
5 horas para concluir

Vector Space Models of Semantics

This module is devoted to a higher abstraction for texts: we will learn vectors that represent meanings. First, we will discuss traditional models of distributional semantics. They are based on a very intuitive idea: "you shall know the word by the company it keeps". Second, we will cover modern tools for word and sentence embeddings, such as word2vec, FastText, StarSpace, etc. Finally, we will discuss how to embed the whole documents with topic models and how these models can be used for search and data exploration.

8 vídeos ((Total 83 mín.)), 3 testes
8 videos
Explicit and implicit matrix factorization13min
Word2vec and doc2vec (and how to evaluate them)10min
Word analogies without magic: king – man + woman != queen11min
Why words? From character to sentence embeddings11min
Topic modeling: a way to navigate through text collections7min
How to train PLSA?6min
The zoo of topic models13min
2 exercícios práticos
Word and sentence embeddings15min
Topic Models10min
5 horas para concluir

Sequence to sequence tasks

Nearly any task in NLP can be formulates as a sequence to sequence task: machine translation, summarization, question answering, and many more. In this module we will learn a general encoder-decoder-attention architecture that can be used to solve them. We will cover machine translation in more details and you will see how attention technique resembles word alignment task in traditional pipeline.

9 vídeos ((Total 98 mín.)), 4 testes
9 videos
Noisy channel: said in English, received in French6min
Word Alignment Models12min
Encoder-decoder architecture6min
Attention mechanism9min
How to deal with a vocabulary?12min
How to implement a conversational chat-bot?11min
Sequence to sequence learning: one-size fits all?10min
Get to the point! Summarization with pointer-generator networks12min
3 exercícios práticos
Introduction to machine translation10min
Encoder-decoder architectures20min
Summarization and simplification15min
84 avaliaçõesChevron Right


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Melhores avaliações

por GYMar 24th 2018

Great thanks to this amazing course! I learned a lot on state-to-art natural language processing techniques! Really like your awesome programming assignments! See you HSE guys in next class!

por MVMar 18th 2019

Definitely best course in the Specialization! Lecturers, projects and forum - everything is super organized. Only StarSpace was pain in the ass, but I managed :)



Anna Potapenko

HSE Faculty of Computer Science

Alexey Zobnin

Accosiate professor
HSE Faculty of Computer Science

Anna Kozlova

Team Lead

Sergey Yudin


Andrei Zimovnov

Senior Lecturer
HSE Faculty of Computer Science

Sobre National Research University Higher School of Economics

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more. Learn more on

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Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

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