Sentiment AnalysisSiamese NetworksHidden Markov ModelTransformersAttention ModelsMachine TranslationWord EmbeddingsLocality-Sensitive HashingVector Space ModelsWord2vecParts-of-Speech TaggingN-gram Language Models
自然语言处理 专项课程
Break into the NLP space. Master cutting-edge NLP techniques through four hands-on courses!
提供方
自然语言处理 专项课程deeplearning.ai您将学到的内容有
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
您将获得的技能
关于此 专项课程
112,090 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.
应用的学习项目
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!
可分享的证书
完成后获得证书
100% 在线课程
立即开始，按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics
完成时间大约为4 个月
建议 6 小时/周
英语（English）
字幕：英语（English）, 日语, 法语（French）, 巴西葡萄牙语, 俄语（Russian）, 西班牙语（Spanish）
可分享的证书
完成后获得证书
100% 在线课程
立即开始，按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics
完成时间大约为4 个月
建议 6 小时/周
英语（English）
字幕：英语（English）, 日语, 法语（French）, 巴西葡萄牙语, 俄语（Russian）, 西班牙语（Spanish）
此专项课程包含 4 门课程
Natural Language Processing with Classification and Vector Spaces
1,641 个评分
In Course 1 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:
Natural Language Processing with Probabilistic Models
637 个评分
In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:
Natural Language Processing with Sequence Models
365 个评分
In Course 3 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:
Natural Language Processing with Attention Models
175 个评分
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.
常见问题
What is the refund policy?
如果订阅，您可以获得 7 天免费试听，在此期间，您可以取消课程，无需支付任何罚金。在此之后，我们不会退款，但您可以随时取消订阅。请阅读我们完整的退款政策。
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可以！点击您感兴趣的课程卡开始注册即可。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分，系统会自动为您订阅完整的专项课程。访问您的学生面板，跟踪您的进度。
Is financial aid available?
是的，Coursera 可以为无法承担费用的学生提供助学金。通过点击左侧“注册”按钮下的“助学金”链接可以申请助学金。您可以根据屏幕提示完成申请，申请获批后会收到通知。您需要针对专项课程中的每一门课程完成上述步骤，包括毕业项目。了解更多。
我可以免费学习课程吗？
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!
还有其他问题吗？请访问 学生帮助中心。
