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.
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.
Offered By
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.
There are 7 Courses in this Specialization
Course1
Introduction to Deep Learning
4.6
649 ratings
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
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
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
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
Mikhail Hushchyn
Researcher at Laboratory for Methods of Big Data AnalysisHSE Faculty of Computer Science
Alexey Zobnin
Accosiate professorHSE Faculty of Computer Science
Alexey Artemov
Senior LecturerHSE Faculty of Computer Science
Sergey Yudin
Analyst-developerYandex
Alexander Guschin
Visiting lecturer at HSE, Lecturer at MIPTHSE Faculty of Computer Science
Nikita Kazeev
ResearcherHSE Faculty of Computer Science
Andrei Ustyuzhanin
Head of Laboratory for Methods of Big Data AnalysisHSE Faculty of Computer Science
Dmitry Ulyanov
Visiting lecturerHSE Faculty of Computer Science
Marios Michailidis
Research Data ScientistH2O.ai
Daniil Polykovskiy
ResearcherHSE Faculty of Computer Science
Ekaterina Lobacheva
Senior LecturerHSE Faculty of Computer Science
Andrei Zimovnov
Senior LecturerHSE Faculty of Computer Science
Alexander Novikov
ResearcherHSE Faculty of Computer Science
Dmitry Altukhov
Visiting lecturerHSE Faculty of Computer Science
Pavel Shvechikov
Researcher at HSE and Sberbank AI LabHSE Faculty of Computer Science
Anton Konushin
Senior LecturerHSE Faculty of Computer Science
Anna Kozlova
Team LeadYandex
Mikhail Trofimov
Visiting lecturerHSE Faculty of Computer Science
Evgeny Sokolov
Senior LecturerHSE Faculty of Computer Science
Alexander Panin
LecturerHSE Faculty of Computer Science
Anna Potapenko
ResearcherHSE Faculty of Computer Science
Industry Partners
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
What is the 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?
Can I take the course for free?
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.
Will I earn university credit for completing the Specialization?
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.
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.
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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