• 退款政策是如何规定的？

• 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？

• 我可以免费学习课程吗？

• 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。

• 完成专项课程需要多长时间？

  Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 8-10 months.

• What background knowledge is necessary?

  As prerequisites we assume calculus and linear algebra (especially derivatives, matrices and operations with them), probability theory (random variables, distributions, moments), basic programming in python (functions, loops, numpy), basic machine learning (linear models, decision trees, boosting and random forests). Our intended audience are all people who are already familiar with basic machine learning and want to get a hand-on experience of research and development in the field of modern machine learning.

• Do I need to take the courses in a specific order?

  We recommend taking the “Intro to Deep Learning” course first as most of the subsequent courses will build on its material. All other courses can be taken in any order.

• What will I be able to do upon completing the Specialization?

  After completing 7 courses of the Specialization you will be able to:

  Use modern deep neural networks for various machine learning problems with complex inputs;

  Participate in data science competitions and use the most popular and effective machine learning tools;

  Adopt the best practices of data exploration, preprocessing and feature engineering;

  Perform Bayesian inference, understand Bayesian Neural Networks and Variational Autoencoders;

  Use reinforcement learning methods to build agents for games and other environments;

  Solve computer vision problems with a combination of deep models and classical computer vision algorithms;

  Outline state-of-the-art techniques for natural language tasks, such as sentiment analysis, semantic slot filling, summarization, topics detection, and many others;

  Build goal-oriented dialogue agents and train them to hold a human-like conversation;

  Understand limitations of standard machine learning methods and design new algorithms for new tasks.

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