Advanced Machine Learning Specialization
Deep Dive Into The Modern AI Techniques. You will teach computer to see, draw, read, talk, play games and solve industry problems.
About this Specialization
Applied Learning Project
You will master your skills by solving a wide variety of real-world problems like image captioning and automatic game playing throughout the course projects. You will gain the hands-on experience of applying advanced machine learning techniques that provide the foundation to the current state-of-the art in AI.
Designed for those already in the industry.
Designed for those already in the industry.
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.
Frequently Asked Questions
What is the refund policy?
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.
Can I just enroll in a single course?
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.
Is financial aid available?
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.
Can I take the course for free?
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.
Is this course really 100% online? Do I need to attend any classes in person?
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.
How long does it take to complete the Specialization?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 8-10 months.
What background knowledge is necessary?
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.
Do I need to take the courses in a specific order?
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.
Will I earn university credit for completing the Specialization?
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.
What will I be able to do upon completing the Specialization?
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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