- Word2vec
- Machine Translation
- Sentiment Analysis
- Transformers
- Attention Models
- Word Embeddings
- Locality-Sensitive Hashing
- Vector Space Models
- Parts-of-Speech Tagging
- N-gram Language Models
- Autocorrect
- Word Embedding
자연 언어 처리 특화 과정
Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest techniques in October '21.
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배울 내용
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 networks in 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.
귀하가 습득할 기술
이 전문 분야 정보
응용 학습 프로젝트
This Specialization will equip you with machine learning basics and 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, translate words, and use locality-sensitive hashing to 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. Learn T5, BERT, transformer, reformer, and more with 🤗 Transformers!
Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics
Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics
특화 과정 이용 방법
강좌 수강
Coursera 특화 과정은 한 가지 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 특화 과정에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 특화 과정에 속하는 강좌에 등록하면 해당 특화 과정 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료할 수도 있으며, 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.
실습 프로젝트
모든 특화 과정에는 실습 프로젝트가 포함되어 있습니다. 특화 과정을 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 특화 과정에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우, 다른 모든 강좌를 완료해야 프로젝트 강좌를 시작할 수 있습니다.
수료증 취득
모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.

이 전문 분야에는 4개의 강좌가 있습니다.
Natural Language Processing with Classification and Vector Spaces
In Course 1 of the Natural Language Processing Specialization, you will:
Natural Language Processing with Probabilistic Models
In Course 2 of the Natural Language Processing Specialization, you will:
Natural Language Processing with Sequence Models
In Course 3 of the Natural Language Processing Specialization, you will:
Natural Language Processing with Attention Models
In Course 4 of the Natural Language Processing Specialization, you will:
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deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
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