About this Specialization

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.
Learner Career Outcomes
50%
Started a new career after completing this specialization.
43%
Got a pay increase or promotion.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Advanced Level
Approx. 10 months to complete
Suggested 6 hours/week
English
Subtitles: English, Korean
Learner Career Outcomes
50%
Started a new career after completing this specialization.
43%
Got a pay increase or promotion.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Advanced Level
Approx. 10 months to complete
Suggested 6 hours/week
English
Subtitles: English, Korean

There are 7 Courses in this Specialization

Course1

Course 1

Introduction to Deep Learning

4.6
stars
1,552 ratings
360 reviews
Course2

Course 2

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

4.7
stars
936 ratings
214 reviews
Course3

Course 3

Bayesian Methods for Machine Learning

4.5
stars
573 ratings
163 reviews
Course4

Course 4

Practical Reinforcement Learning

4.2
stars
365 ratings
104 reviews

Instructors

Offered by

National Research University Higher School of Economics logo

National Research University Higher School of Economics

The logo of one of the Industry Partners

Frequently Asked Questions

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • 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.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • 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.

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