À propos de ce cours
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Niveau avancé

Approx. 33 heures pour terminer

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


Sous-titres : Anglais

Compétences que vous acquerrez

ChatterbotTensorflowDeep LearningNatural Language Processing

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau avancé

Approx. 33 heures pour terminer

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


Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

5 heures pour terminer

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 vidéos (Total 114 min), 3 lectures, 3 quiz
11 vidéos
Welcome video5 min
Main approaches in NLP7 min
Brief overview of the next weeks7 min
[Optional] Linguistic knowledge in NLP10 min
Text preprocessing14 min
Feature extraction from text14 min
Linear models for sentiment analysis10 min
Hashing trick in spam filtering17 min
Neural networks for words14 min
Neural networks for characters8 min
3 lectures
Prerequisites check-list2 min
Hardware for the course5 min
Getting started with practical assignments20 min
2 exercices pour s'entraîner
Classical text mining10 min
Simple neural networks for text10 min
5 heures pour terminer

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 vidéos (Total 84 min), 2 lectures, 3 quiz
8 vidéos
Perplexity: is our model surprised with a real text?8 min
Smoothing: what if we see new n-grams?7 min
Hidden Markov Models13 min
Viterbi algorithm: what are the most probable tags?11 min
MEMMs, CRFs and other sequential models for Named Entity Recognition11 min
Neural Language Models9 min
Whether you need to predict a next word or a label - LSTM is here to help!11 min
2 lectures
Perplexity computation10 min
Probabilities of tag sequences in HMMs20 min
2 exercices pour s'entraîner
Language modeling15 min
Sequence tagging with probabilistic models20 min
5 heures pour terminer

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 vidéos (Total 83 min), 3 quiz
8 vidéos
Explicit and implicit matrix factorization13 min
Word2vec and doc2vec (and how to evaluate them)10 min
Word analogies without magic: king – man + woman != queen11 min
Why words? From character to sentence embeddings11 min
Topic modeling: a way to navigate through text collections7 min
How to train PLSA?6 min
The zoo of topic models13 min
2 exercices pour s'entraîner
Word and sentence embeddings15 min
Topic Models10 min
5 heures pour terminer

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 vidéos (Total 98 min), 4 quiz
9 vidéos
Noisy channel: said in English, received in French6 min
Word Alignment Models12 min
Encoder-decoder architecture6 min
Attention mechanism9 min
How to deal with a vocabulary?12 min
How to implement a conversational chat-bot?11 min
Sequence to sequence learning: one-size fits all?10 min
Get to the point! Summarization with pointer-generator networks12 min
3 exercices pour s'entraîner
Introduction to machine translation10 min
Encoder-decoder architectures20 min
Summarization and simplification15 min
90 avisChevron Right


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Principaux examens pour Traitement automatique du langage naturel

par 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!

par 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

À propos de Université nationale de recherche, École des hautes études en sciences économiques

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 www.hse.ru...

À propos de la Spécialisation Apprentissage automatique avancé

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....
Apprentissage automatique avancé

Foire Aux Questions

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  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

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