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

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Younes Bensouda Mourri +2 mais instrutores

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Programa de cursos integrados Processamento da Linguagem Naturaldeeplearning.ai

O que você vai aprender

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

Habilidades que você terá

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

Sobre este Programa de cursos integrados

55.956 visualizações recentes

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.

Projeto de Aprendizagem Aplicada

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!

Certificados compartilháveis

Tenha o certificado após a conclusão

Cursos 100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Cronograma flexível

Definição e manutenção de prazos flexíveis.

Nível intermediário

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

Aproximadamente 4 meses para ser concluído

Ritmo sugerido de 7 horas/semana

Inglês

Legendas: Inglês, Chinês (tradicional), Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Espanhol, Japonês

Certificados compartilháveis

Tenha o certificado após a conclusão

Cursos 100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Cronograma flexível

Definição e manutenção de prazos flexíveis.

Nível intermediário

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

Aproximadamente 4 meses para ser concluído

Ritmo sugerido de 7 horas/semana

Inglês

Legendas: Inglês, Chinês (tradicional), Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Espanhol, Japonês

Como funciona o programa de cursos integrados

Fazer cursos

Um programa de cursos integrados do Coursera é uma série de cursos para ajudá-lo a dominar uma habilidade. Primeiramente, inscreva-se no programa de cursos integrados diretamente, ou avalie a lista de cursos e escolha por qual você gostaria de começar. Ao se inscrever em um curso que faz parte de um programa de cursos integrados, você é automaticamente inscrito em todo o programa de cursos integrados. É possível concluir apenas um curso — você pode pausar a sua aprendizagem ou cancelar a sua assinatura a qualquer momento. Visite o seu painel de aprendiz para controlar suas inscrições em cursos e progresso.

Projeto prático

Todos os programas de cursos integrados incluem um projeto prático. Você precisará completar com êxito o(s) projeto(s) para concluir o programa de cursos integrados e obter o seu certificado. Se o programa de cursos integrados incluir um curso separado para o projeto prático, você precisará completar todos os outros cursos antes de iniciá-lo.

Obtenha um certificado

Ao concluir todos os cursos e completar o projeto prático, você obterá um certificado que pode ser compartilhado com potenciais empregadores e com sua rede profissional.

Este Programa de cursos integrados contém 4 cursos

Curso1

Curso 1

Natural Language Processing with Classification and Vector Spaces

4.6

estrelas

2,946 classificações

•

603 avaliações

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.

Curso2

Curso 2

Natural Language Processing with Probabilistic Models

4.7

estrelas

1,201 classificações

•

210 avaliações

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.

Curso3

Curso 3

Natural Language Processing with Sequence Models

4.5

estrelas

781 classificações

•

160 avaliações

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.

Curso4

Curso 4

Natural Language Processing with Attention Models

4.3

estrelas

656 classificações

•

164 avaliações

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.

Instrutores

oferecido por

deeplearning.ai

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

Perguntas Frequentes – FAQ

What is the refund policy?
Se você se inscrever, terá 7 dias para testar sem custo e, durante este período, pode cancelar sem multa. Depois disso, não reembolsamos, mas você pode cancelar sua inscrição a qualquer momento. Veja nossa política para o reembolso total.
Posso me inscrever em um único curso?
Sim! Para começar, clique na carta de curso que lhe interessa e se inscreva. Você pode se inscrever e concluir o curso para ganhar um certificado compartilhável ou você pode auditar para ver os materiais do curso de graça. Quando você se inscrever em um curso que faz parte de uma especialização, você está automaticamente inscrito para a especialização completa. Visite o seu painel de aluno para acompanhar o seu progresso.
Existe algum auxílio financeiro disponível?
Sim, a Coursera oferece auxílio financeiro ao aluno que não possa pagar a taxa. Faça a solicitação clicando no link Auxílio Financeiro, abaixo do botão "Inscreva-se" à esquerda. Preencha uma solicitação e será notificado caso seja aprovado. Você terá que completar esta etapa para cada curso na Especialização, incluindo o Trabalho de Conclusão de Curso. Saiba mais .
Posso fazer o curso gratuitamente?
Quando se inscrever no curso, você terá acesso a todos os cursos na Especialização e ganhará um certificado quando concluir o trabalho. Se você só quiser ler e visualizar o conteúdo do curso, pode auditar o curso gratuitamente. Se não puder pagar a taxa, você pode solicitar ajuda financeira.
Este curso é realmente 100% on-line? Eu preciso assistir alguma aula pessoalmente?
Este curso é totalmente on-line, então não existe necessidade de aparecer em uma sala de aula pessoalmente. Você pode acessar suas palestras, leituras e atribuições a qualquer hora e qualquer lugar, via web ou dispositivo móvel.
Vou ganhar créditos universitários por concluir a Especialização?
Esta Especialização não carrega créditos universitários, mas algumas universidades podem optar por aceitar certificados de especialização como crédito. Verifique com sua instituição para saber mais.
Vou ganhar créditos universitários por concluir a Especialização?
Este Programa de cursos integrados não oferece créditos universitários, mas algumas universidades podem aceitar certificados de Programas de cursos integrados que podem ser convertidos em créditos. Entre em contato com sua instituição para saber mais. Com os cursos on-line e os certificados Mastertrack™ do Coursera, é possível ganhar créditos universitários.
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.
Quanto tempo é necessário para concluir a Especialização?
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!

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.