Break into the NLP space. Master cutting-edge NLP techniques through four hands-on courses!

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Younes Bensouda Mourri +2 enseignants de plus

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Spécialisation Traitement automatique du langage natureldeeplearning.ai

Ce que vous allez apprendre

Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words

Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words

Use recurrent neural networks, LSTMs, GRUs & Siamese network in TensorFlow & Trax for sentiment analysis, text generation & named entity recognition

Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering

Compétences que vous acquerrez

Sentiment AnalysisSiamese NetworksHidden Markov ModelTransformersAttention ModelsMachine TranslationWord EmbeddingsLocality-Sensitive HashingVector Space ModelsWord2vecParts-of-Speech TaggingN-gram Language Models

À propos de ce Spécialisation

55 982 consultations récentes

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Projet d'apprentissage appliqué

This Specialization will equip you with the state-of-the-art deep learning techniques needed to build cutting-edge NLP systems:

• Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, and translate words, and use locality sensitive hashing for approximate nearest neighbors.

• Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.

• Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions.

• Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering and to build chatbots. Models covered include T5, BERT, transformer, reformer, and more!

Certificat partageable

Obtenez un Certificat lorsque vous terminez

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Planning flexible

Définissez et respectez des dates limites flexibles.

Niveau intermédiaire

Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics

Approximativement 4 mois pour terminer

Rythme recommandé de 7 heures/semaine

Anglais

Sous-titres : Anglais, Chinois (traditionnel), Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Espagnol, Japonais

Certificat partageable

Obtenez un Certificat lorsque vous terminez

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Planning flexible

Définissez et respectez des dates limites flexibles.

Niveau intermédiaire

Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics

Approximativement 4 mois pour terminer

Rythme recommandé de 7 heures/semaine

Anglais

Sous-titres : Anglais, Chinois (traditionnel), Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Espagnol, Japonais

Comment fonctionne la Spécialisation

Suivez les cours

Une Spécialisation Coursera est une série de cours axés sur la maîtrise d'une compétence. Pour commencer, inscrivez-vous directement à la Spécialisation ou passez en revue ses cours et choisissez celui par lequel vous souhaitez commencer. Lorsque vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Il est possible de terminer seulement un cours : vous pouvez suspendre votre formation ou résilier votre abonnement à tout moment. Rendez-vous sur votre tableau de bord d'étudiant pour suivre vos inscriptions aux cours et vos progrès.

Projet pratique

Chaque Spécialisation inclut un projet pratique. Vous devez réussir le(s) projet(s) pour terminer la Spécialisation et obtenir votre Certificat. Si la Spécialisation inclut un cours dédié au projet pratique, vous devrez terminer tous les autres cours avant de pouvoir le commencer.

Obtenir un Certificat

Lorsque vous aurez terminé tous les cours et le projet pratique, vous obtiendrez un Certificat que vous pourrez partager avec des employeurs éventuels et votre réseau professionnel.

Cette Spécialisation compte 4 cours

Cours1

Cours 1

Natural Language Processing with Classification and Vector Spaces

4.6

étoiles

2,936 évaluations

•

602 avis

In Course 1 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:

a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbor search. Please make sure that you’re comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Cours2

Cours 2

Natural Language Processing with Probabilistic Models

4.7

étoiles

1,198 évaluations

•

209 avis

In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:

a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is important for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. Please make sure that you’re comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Cours3

Cours 3

Natural Language Processing with Sequence Models

4.5

étoiles

777 évaluations

•

158 avis

In Course 3 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:

a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning. Please make sure that you’ve completed Course 2 and are familiar with the basics of TensorFlow. If you’d like to prepare additionally, you can take Course 1: Neural Networks and Deep Learning of the Deep Learning Specialization. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Cours4

Cours 4

Natural Language Processing with Attention Models

4.4

étoiles

650 évaluations

•

160 avis

In Course 4 of the Natural Language Processing Specialization, offered by DeepLearning.AI, you will:

a) Translate complete English sentences into German using an encoder-decoder attention model, b) Build a Transformer model to summarize text, c) Use T5 and BERT models to perform question-answering, and d) Build a chatbot using a Reformer model. This course is for students of machine learning or artificial intelligence as well as software engineers looking for a deeper understanding of how NLP models work and how to apply them. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you’ve completed course 3 - Natural Language Processing with Sequence Models - before starting this course. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Enseignants

Offert par

deeplearning.ai

DeepLearning.AI is an education technology company that develops a global community of AI talent.

Foire Aux Questions

What is the refund policy?
Si vous vous abonnez, vous bénéficiez d'une période d'essai gratuite de 7 jours, durant laquelle vous pouvez annuler votre abonnement sans pénalité. Ensuite, nous n'accordons plus de remboursements, mais vous pouvez annuler votre abonnement à tout instant. Consultez notre politique de remboursement complète.
Puis-je m'inscrire à un seul cours ?
Oui ! Pour commencer, cliquez sur la carte du cours qui vous intéresse et inscrivez-vous. Vous pouvez vous inscrire et terminer le cours pour obtenir un Certificat partageable, ou vous pouvez accéder au cours en auditeur libre afin d'en visualiser gratuitement le contenu. Si vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Visitez votre tableau de bord d'étudiant(e) pour suivre vos progrès.
Une aide financière est-elle possible ?
Oui, Coursera offre une Aide Financière aux étudiants qui n'ont pas les moyens d'acquitter les frais. Demandez-la en cliquant sur le lien Aide Financière sous le bouton S'inscrire situé à gauche. Vous devrez remplir un formulaire de demande et vous serez averti(e) si elle est acceptée. Vous devrez répéter cette procédure pour chaque cours de la Spécialisation, y compris pour le Projet Final. En savoir plus.
Puis-je suivre le cours gratuitement ?
Si vous vous inscrivez au cours, vous pouvez accéder à tous les cours de la Spécialisation et obtenir un Certificat lorsque vous terminez le travail. Si vous souhaitez seulement lire et examiner le contenu du cours, vous pouvez accéder gratuitement au cours en auditeur libre. Si vous n'avez pas les moyens d'acquitter les frais, vous pouvez faire une demande d'Aide Financière.
Ce cours est-il vraiment accessible en ligne à 100 % ? Dois-je assister à certaines activités en personne ?
Ce cours est entièrement en ligne : vous n'avez donc pas besoin de vous présenter physiquement dans une salle de classe. Vous pouvez accéder à vos vidéos de cours, lectures et devoirs en tout temps et en tout lieu, par l'intermédiaire du Web ou de votre appareil mobile.
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
Cette Spécialisation n'est pas associée à des crédits universitaires, mais certaines universités peuvent décider d'accepter des Certificats de Spécialisation pour des crédits. Vérifiez-le auprès de votre établissement pour en savoir plus.
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
This Specialization is for students of machine learning or artificial intelligence as well as software engineers looking for a deeper understanding of how NLP models work and how to apply them.
What background knowledge is necessary?
Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. If you would like to brush up on these skills, we recommend the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng.
Is this a standalone Course or a Specialization?
This is a Specialization made up of 4 Courses. Course 3 is scheduled for the end of July. Course 4 will launch in September.
What makes this Specialization so different?
The deeplearning.ai Natural Language Processing Specialization is one-of-a-kind.
It teaches cutting-edge techniques drawn from recent academic papers, some of which were only first published in 2019.
It covers practical methods for handling common NLP use cases (autocorrect, autocomplete), as well as advanced deep learning techniques for chatbots and question-answering.
It starts with the foundations and takes you to a stage where you can build state-of-the-art attention models that allow for parallel computing.
You will not only use packages but also learn how to build these models from scratch. We walk you through all the steps, from data processing to the finished products you can use in your own projects.
You will complete one project every week to make sure you understand the concepts for a total of 16 programming assignments.
Do I need to take the courses in a specific order?
We recommend taking the courses in the prescribed order for a logical and thorough learning experience.
How long does it take to complete the Specialization?
This Specialization consists of four Courses. At the rate of 5 hours a week, it typically takes 4 weeks to complete each Course.
Quelle est la durée nécessaire pour terminer la Spécialisation ?
Learn classical machine learning skills and state-of-the-art deep learning techniques and perform a number of functions:
• Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, and translate words, and use locality sensitive hashing for approximate nearest neighbors.
• Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.
• Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions.
• Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering and to build chatbots. Models covered include T5, BERT, transformer, reformer, and more!

D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.