Sentiment AnalysisSiamese NetworksHidden Markov ModelTransformersAttention ModelsMachine TranslationWord EmbeddingsLocality-Sensitive HashingVector Space ModelsWord2vecParts-of-Speech TaggingN-gram Language Models
自然语言处理 專項課程
Break into the NLP space. Master cutting-edge NLP techniques through four hands-on courses!
提供方
自然语言处理 專項課程deeplearning.ai您將學到的內容有
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
您將獲得的技能
關於此 專項課程
110,653 次近期查看
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.
應用的學習項目
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!
可分享的證書
完成後獲得證書
100% 在線課程
立即開始，按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
中級
Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics
完成時間大約為4 個月
建議 6 小時/週
英語（English）
字幕：英語（English）, 日語, 法語（French）, 巴西葡萄牙語, 俄語（Russian）, 西班牙語（Spanish）
可分享的證書
完成後獲得證書
100% 在線課程
立即開始，按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
中級
Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics
完成時間大約為4 個月
建議 6 小時/週
英語（English）
字幕：英語（English）, 日語, 法語（French）, 巴西葡萄牙語, 俄語（Russian）, 西班牙語（Spanish）
此專項課程包含 4 門課程
Natural Language Processing with Classification and Vector Spaces
1,671 個評分
In Course 1 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:
Natural Language Processing with Probabilistic Models
653 個評分
In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:
Natural Language Processing with Sequence Models
371 個評分
In Course 3 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:
Natural Language Processing with Attention Models
191 個評分
In Course 4 of the Natural Language Processing Specialization, offered by DeepLearning.AI, you will:
提供方
deeplearning.ai
Founded by Andrew Ng, DeepLearning.AI is an education technology company that develops a global community of AI talent.
常見問題
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完成注册课程后，您可以学习专项课程中的所有课程，并且完成作业后可以获得证书。如果您只想阅读和查看课程内容，可以免费旁听该课程。如果您无法承担课程费用，可以申请助学金。
此课程是 100% 在线学习吗？是否需要现场参加课程？
此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。
完成专项课程后我会获得大学学分吗？
此专项课程不提供大学学分，但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校了解详情。
完成专项课程后我会获得大学学分吗？
此专项课程不提供大学学分，但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校，了解详情。Coursera 上的在线学位和 Mastertrack™ 证书提供获得大学学分的机会。
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.
完成专项课程需要多长时间？
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!
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