自然语言处理 專項課程

• 退款政策是如何规定的？

  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。

• 我可以只注册一门课程吗？

  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。

• 有助学金吗？

  是的。如果您负担不起学习计划的注册费，则可以在选择学习计划时申请助学金或奖学金。如果您选择的学习计划提供助学金或奖学金，则您将在描述页面上看到一个申请链接。

• 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。

• What is Natural Language Processing (NLP)?

  Natural language processing is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language.

• What is the Natural Language Processing Specialization about?

  In the Natural Language Processing (NLP) Specialization, you will learn how to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages, summarize text, and even build chatbots. These and other NLP applications will be at the forefront of the coming transformation to an AI-powered future.

  NLP is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. 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.

• What will I be able to do after completing the Natural Language Processing Specialization?

  This Specialization will equip you with both the machine learning basics as well as 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, 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 build chatbots. Learn include T5, BERT, transformer, reformer, and more!

• What makes this Specialization so different?

  The 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.

• What background knowledge is necessary for the Natural Language Processing Specialization?

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

• Who is this Specialization for?

  This Specialization is for students of machine learning or artificial intelligence and software engineers looking for a deeper understanding of how NLP models work and how to apply them.

• How long does it take to complete the Natural Language Processing Specialization?

  This Specialization consists of four Courses. At the rate of 5 hours a week, it typically takes 4 weeks to complete each Course.

• Who created the Natural Language Processing Specialization?

  Younes Bensouda Mourri and Lukasz Kaiser created the Natural Language Processing Specialization.

  Younes Bensouda Mourricompleted his Bachelor's in Applied Math and CS and Master's in Statistics from Stanford University. Younes helped create 3 AI courses at Stanford - Applied Machine Learning, Deep Learning, and Teaching AI - and taught two for a few years. Currently, he is an adjunct lecturer of computer science at Stanford University.

  Lukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the “Attention is all you need” Transformer paper.

• The Natural Language Processing Specialization was updated in October 2021. What is different in the new version?

  In Course 1: NLP with Classification and Vector Spaces,

  • All the programming assignments and ungraded labs have been refactored
  • All programming assignments have new automatic graders

  In Course 2: NLP with Probabilistic Models,

  • All the programming assignments and ungraded labs have been refactored
  • All programming assignments have new automatic graders

  In Course 3: NLP with Sequence Models,

  • A new section on visualizing embeddings from the trained model has been added to the Week 1 assignment
  • Corrections have been made to the evaluation section of the Week 2 assignment Week 3 assignment has been refactored
  • The following lectures have been updated to new versions:
  • The following ungraded labs have been updated to new versions:

  In Course 4: NLP with Attention Models,

  • Two new ungraded labs concerned with how attention is implemented in deep learning models have been added
  • The following lectures have been updated to new versions or modified (excluding HuggingFace content):
  • Four new lectures on Hugging Face have been added:
  • Two new ungraded labs on Hugging Face have been added:

• I’m currently enrolled in one or more courses in the Natural Language Processing Specialization. What does this mean for me?

  If you would like to update to the new material, reset your deadlines. If you’re in the middle of a course, you will lose your progress when you reset your deadlines. Please save any notebook assignments you've submitted by downloading your existing notebooks before switching to the new version.

  • Your certificates will carry over for any courses you’ve already completed. 

  • If your subscription is currently active, you can access the updated labs and submit assignments without paying for the month again. 

  • If you go to the Specialization, you will see the original version of the lecture videos and assignments. You can complete the original version if so desired (this is not recommended).

  • If you would like to update to the new material, reset your deadlines. If you’re in the middle of a course, you will lose your notebook work when you reset your deadlines.• Please save your work by downloading your existing notebooks before switching to the new version. 

  • If you do not see the option to reset deadlines, contact Coursera via the Learner Help Center.

• I’ve already completed one or more courses in the Natural Language Processing Specialization but don’t have an active subscription. What does this mean for me?

  • Your certificates will carry over for any courses you’ve already completed. • If your subscription is currently inactive, you will need to pay again to access the labs and submit assignments for those courses.

還有其他問題嗎？請訪問 學生幫助中心。