About this Specialization
59,063 recent views

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Advanced Level

English

Subtitles: English, Korean

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Advanced Level

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

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

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

Introduction to Deep Learning

4.6
1,196 ratings
265 reviews
Course2

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

4.7
745 ratings
158 reviews
Course3

Bayesian Methods for Machine Learning

4.6
445 ratings
119 reviews
Course4

Practical Reinforcement Learning

4.1
285 ratings
74 reviews

Instructors

Avatar

Mikhail Hushchyn

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

Alexey Zobnin

Accosiate professor
HSE Faculty of Computer Science
Avatar

Alexey Artemov

Senior Lecturer
HSE Faculty of Computer Science
Avatar

Sergey Yudin

Analyst-developer
Yandex
Avatar

Alexander Guschin

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

Nikita Kazeev

Researcher
HSE Faculty of Computer Science
Avatar

Andrei Ustyuzhanin

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

Dmitry Ulyanov

Visiting lecturer
HSE Faculty of Computer Science
Avatar

Marios Michailidis

Research Data Scientist
H2O.ai
Avatar

Daniil Polykovskiy

Sr. Research Scientist
HSE Faculty of Computer Science
Avatar

Ekaterina Lobacheva

Senior Lecturer
HSE Faculty of Computer Science
Avatar

Andrei Zimovnov

Senior Lecturer
HSE Faculty of Computer Science
Avatar

Alexander Novikov

Researcher
HSE Faculty of Computer Science
Avatar

Dmitry Altukhov

Visiting lecturer
HSE Faculty of Computer Science
Avatar

Pavel Shvechikov

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

Anton Konushin

Senior Lecturer
HSE Faculty of Computer Science
Avatar

Anna Kozlova

Team Lead
Yandex
Avatar

Mikhail Trofimov

Visiting lecturer
HSE Faculty of Computer Science
Avatar

Evgeny Sokolov

Senior Lecturer
HSE Faculty of Computer Science
Avatar

Alexander Panin

Lecturer
HSE Faculty of Computer Science
Avatar

Anna Potapenko

Researcher
HSE Faculty of Computer Science

Industry Partners

Industry Partner Logo #0

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.

  • 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.

More questions? Visit the Learner Help Center.