About this Specialization
100% online courses

100% online courses

Start instantly and learn at your own schedule.
Flexible Schedule

Flexible Schedule

Set and maintain flexible deadlines.
Advanced Level

Advanced Level

Available languages

English

Subtitles: English...
100% online courses

100% online courses

Start instantly and learn at your own schedule.
Flexible Schedule

Flexible Schedule

Set and maintain flexible deadlines.
Advanced Level

Advanced Level

Available languages

English

Subtitles: English...

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
649 ratings
155 reviews
The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. The prerequisites for this course are: 1) Basic knowledge of Python. 2) Basic linear algebra and probability. Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand: 1) Linear regression: mean squared error, analytical solution. 2) Logistic regression: model, cross-entropy loss, class probability estimation. 3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions. 4) The problem of overfitting. 5) Regularization for linear models....
Course2

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

4.7
405 ratings
88 reviews
If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales’ forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science. In this course, you will learn to analyse and solve competitively such predictive modelling tasks. When you finish this class, you will: - Understand how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks. - Learn how to preprocess the data and generate new features from various sources such as text and images. - Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions. - Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data. - Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them. - Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance. - Master the art of combining different machine learning models and learn how to ensemble. - Get exposed to past (winning) solutions and codes and learn how to read them. Disclaimer : This is not a machine learning course in the general sense. This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them. Prerequisites: - Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM. - Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks....
Course3

Bayesian Methods for Machine Learning

4.6
235 ratings
72 reviews
Bayesian methods are used in lots of fields: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a really desirable feature for fields like medicine. When Bayesian methods are applied to deep learning, it turns out that they allow you to compress your models 100 folds, and automatically tune hyperparametrs, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can fully automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods....
Course4

Practical Reinforcement Learning

4.3
115 ratings
33 reviews
Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also known as "the hype train" - state of the art RL algorithms --- and how to apply duct tape to them for practical problems. - and, of course, teaching your neural network to play games --- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits. Jump in. It's gonna be fun!...

Instructors

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Mikhail Hushchyn

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

Accosiate professor
HSE Faculty of Computer Science
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Alexey Artemov

Senior Lecturer
HSE Faculty of Computer Science
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Sergey Yudin

Analyst-developer
Yandex
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Alexander Guschin

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

Researcher
HSE Faculty of Computer Science
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Andrei Ustyuzhanin

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

Visiting lecturer
HSE Faculty of Computer Science
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Marios Michailidis

Research Data Scientist
H2O.ai
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Daniil Polykovskiy

Researcher
HSE Faculty of Computer Science
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Ekaterina Lobacheva

Senior Lecturer
HSE Faculty of Computer Science
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Andrei Zimovnov

Senior Lecturer
HSE Faculty of Computer Science
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Alexander Novikov

Researcher
HSE Faculty of Computer Science
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Dmitry Altukhov

Visiting lecturer
HSE Faculty of Computer Science
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Pavel Shvechikov

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

Senior Lecturer
HSE Faculty of Computer Science
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Anna Kozlova

Team Lead
Yandex
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Mikhail Trofimov

Visiting lecturer
HSE Faculty of Computer Science
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Evgeny Sokolov

Senior Lecturer
HSE Faculty of Computer Science
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Alexander Panin

Lecturer
HSE Faculty of Computer Science
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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 communications, IT, mathematics, 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.

  • This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • 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.

  • 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.