關於此 專項課程

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。

高級

英語(English)

字幕:英語(English), 韓語

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。

高級

英語(English)

字幕:英語(English), 韓語

專項課程的運作方式

加入課程

Coursera 專項課程是幫助您掌握一門技能的一系列課程。若要開始學習,請直接註冊專項課程,或預覽專項課程並選擇您要首先開始學習的課程。當您訂閱專項課程的部分課程時,您將自動訂閱整個專項課程。您可以只完成一門課程,您可以隨時暫停學習或結束訂閱。訪問您的學生面板,跟踪您的課程註冊情況和進度。

實踐項目

每個專項課程都包括實踐項目。您需要成功完成這個(些)項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程,則需要在開始之前完成其他所有課程。

獲得證書

在結束每門課程並完成實踐項目之後,您會獲得一個證書,您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。

how it works

此專項課程包含 7 門課程

課程1

Introduction to Deep Learning

4.6
1,245 個評分
273 條評論
課程2

How to Win a Data Science Competition: Learn from Top Kagglers

4.7
777 個評分
164 條評論
課程3

Bayesian Methods for Machine Learning

4.6
465 個評分
124 條評論
課程4

Practical Reinforcement Learning

4.1
296 個評分
78 條評論

講師

授課教師 Mikhail Hushchyn 的圖片

Mikhail Hushchyn

Researcher at Laboratory for Methods of Big Data Analysis
HSE Faculty of Computer Science
授課教師 Alexey Zobnin 的圖片

Alexey Zobnin

Accosiate professor
HSE Faculty of Computer Science
授課教師 Alexey Artemov 的圖片

Alexey Artemov

Senior Lecturer
HSE Faculty of Computer Science
授課教師 Sergey Yudin 的圖片

Sergey Yudin

Analyst-developer
Yandex
授課教師 Alexander Guschin 的圖片

Alexander Guschin

Visiting lecturer at HSE, Lecturer at MIPT
HSE Faculty of Computer Science
授課教師 Nikita Kazeev 的圖片

Nikita Kazeev

Researcher
HSE Faculty of Computer Science
授課教師 Andrei Ustyuzhanin 的圖片

Andrei Ustyuzhanin

Head of Laboratory for Methods of Big Data Analysis
HSE Faculty of Computer Science
授課教師 Dmitry Ulyanov 的圖片

Dmitry Ulyanov

Visiting lecturer
HSE Faculty of Computer Science
授課教師 Marios Michailidis 的圖片

Marios Michailidis

Research Data Scientist
H2O.ai
授課教師 Daniil Polykovskiy 的圖片

Daniil Polykovskiy

Sr. Research Scientist
HSE Faculty of Computer Science
授課教師 Ekaterina Lobacheva 的圖片

Ekaterina Lobacheva

Senior Lecturer
HSE Faculty of Computer Science
授課教師 Andrei Zimovnov 的圖片

Andrei Zimovnov

Senior Lecturer
HSE Faculty of Computer Science
授課教師 Alexander Novikov 的圖片

Alexander Novikov

Researcher
HSE Faculty of Computer Science
授課教師 Dmitry Altukhov 的圖片

Dmitry Altukhov

Visiting lecturer
HSE Faculty of Computer Science
授課教師 Pavel Shvechikov 的圖片

Pavel Shvechikov

Researcher at HSE and Sberbank AI Lab
HSE Faculty of Computer Science
授課教師 Anton Konushin 的圖片

Anton Konushin

Senior Lecturer
HSE Faculty of Computer Science
授課教師 Anna Kozlova 的圖片

Anna Kozlova

Team Lead
Yandex
授課教師 Mikhail Trofimov 的圖片

Mikhail Trofimov

Visiting lecturer
HSE Faculty of Computer Science
授課教師 Evgeny Sokolov 的圖片

Evgeny Sokolov

Senior Lecturer
HSE Faculty of Computer Science
授課教師 Alexander Panin 的圖片

Alexander Panin

Lecturer
HSE Faculty of Computer Science
授課教師 Anna Potapenko 的圖片

Anna Potapenko

Researcher
HSE Faculty of Computer Science

行業合作夥伴

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關於 国立高等经济大学

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more. Learn more on www.hse.ru...

常見問題

  • 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。

  • 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。

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

  • 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.

  • 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.

  • Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • 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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