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Куцев Роман Анатольевич, Саркисян Вероника Вагановна, Бардуков Анатолий Андреевич, Космачев Алексей Дмитриевич, and Зимовнов Андрей Вадимович
И исследователи в лабораториях, и инженеры в больших корпорациях бьются над одной задачей — получить наиболее точную предсказательную модель по имеющимся данным. Эту задачу можно решить, используя современные методы из области машинного обучения. К сожалению,...
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И исследователи в лабораториях, и инженеры в больших корпорациях бьются над одной задачей — получить наиболее точную предсказательную модель по имеющимся данным. Эту задачу можно решить, используя современные методы из области машинного обучения. К сожалению, когда данных становится слишком много, классические алгоритмы становятся неэффективными или перестают работать вовсе. В этом онлайн-курсе НИУ ВШЭ мы рассмотрим основные проблемы, которые возникают при попытке обучить машину на больших данных, и методы их решения. Изучим подходы для эффективной разметки данных, модификации в классических алгоритмах, которые позволяют им эффективно работать, а также наиболее популярные инструменты для решения задач интеллектуального анализа данных.
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Taught by distinguished instructors in academia and industry, this course is ideal for professionals who need to build predictive models using big data
Provides practical knowledge of advanced machine learning techniques for building predictive models from large datasets
Covers modern approaches to data labeling and modification of classical algorithms for large data scenarios
The curriculum aligns with industry standards for data analysis and modeling
Utilizes popular tools and techniques in data analysis, making the course immediately applicable to real-world projects
Requires prior knowledge of machine learning and data analysis fundamentals

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Reviews summary

Data science and python

This course introduces fundamental concepts of data science and machine learning, with a focus on solving real-world problems. Students will learn to use Python for data analysis and visualization, and they will gain experience building and evaluating supervised learning models. The course also covers advanced topics such as natural language processing and deep learning.
Concepts explained clearly
Hands-on approach with Python
"Больше всего понравились практические задания на Python..."
Some topics covered superficially
"... Некоторые темы даются обзорно..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Машинное обучение на больших данных with these activities:
Find a mentor to help you with your machine learning journey
Finding a mentor who can provide guidance and support will help you accelerate your progress in machine learning.
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  • Identify the skills and knowledge that you want to develop.
  • Network with people in your field.
  • Ask for introductions to potential mentors.
Review calculus
Calculus is also essential to understand concepts such as the gradient and optimization in machine learning.
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  • Review the basics of derivatives and integrals.
  • Apply derivatives to find the maximum and minimum values of a function.
  • Apply integrals to find the area under a curve.
Review linear algebra
Linear algebra is the foundation of machine learning and is required to understand many of the concepts encountered in this course.
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  • Review the basics of vector spaces, matrices, and linear transformations.
  • Solve systems of linear equations.
  • Find the eigenvalues and eigenvectors of a matrix.
Six other activities
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Follow a machine learning tutorial
Following a machine learning tutorial will help you learn the basics of machine learning and get started with building your own models.
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  • Find a machine learning tutorial that is right for your skill level.
  • Follow the tutorial step-by-step.
  • Complete the exercises and quizzes in the tutorial.
Solve coding problems on LeetCode
Solving coding problems on LeetCode will help you improve your programming skills and prepare you for the challenges of working with big data.
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  • Choose a problem that is appropriate for your skill level.
  • Understand the problem statement and write a test case.
  • Implement a solution to the problem.
  • Test your solution and debug any errors.
Attend a machine learning workshop
Attending a machine learning workshop will help you learn new techniques and best practices from experienced practitioners.
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  • Find a machine learning workshop that is right for your skill level.
  • Register for the workshop and pay the registration fee.
  • Attend the workshop and participate in the activities.
  • Follow up with the workshop organizers after the event.
Write a blog post about machine learning
Writing a blog post about machine learning will help you solidify your understanding of the concepts and share your knowledge with others.
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  • Choose a topic for your blog post.
  • Research your topic and gather information.
  • Write your blog post.
  • Publish your blog post and promote it on social media.
Build a machine learning project
Building a machine learning project will help you apply your skills to a real-world problem and gain hands-on experience.
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  • Choose a problem to solve.
  • Gather data.
  • Develop a machine learning model.
  • Deploy your model and evaluate its performance.
Participate in a Kaggle competition
Participating in a Kaggle competition will help you apply your machine learning skills to real-world problems and compete with other data scientists.
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  • Find a Kaggle competition that is appropriate for your skill level.
  • Read the competition rules and download the data.
  • Develop a machine learning model to solve the competition problem.
  • Submit your model to the competition.

Career center

Learners who complete Машинное обучение на больших данных will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer uses their in-depth knowledge of machine learning algorithms, such as those utilized in the online course "Machine Learning on Big Data" from HSE University, to design and develop artificial intelligence applications. The course covers the challenges and techniques for effective machine learning when dealing with extensive datasets. This knowledge is critical in building and improving predictive models and creating data-driven solutions for various domains, making this course highly valuable for those seeking a career as a Machine Learning Engineer.
Data Scientist
As a Data Scientist, one leverages their expertise in data analysis, modeling, and machine learning techniques to extract insights from massive amounts of data. The online course "Machine Learning on Big Data" from HSE University delves into the complexities of machine learning in the context of big data. By understanding the algorithms and approaches for efficiently handling and learning from such datasets, aspiring Data Scientists can enhance their analytical skills and become more effective in discovering patterns and deriving meaningful conclusions from data.
Software Engineer
Software Engineers specializing in Machine Learning and Big Data are in high demand. The online course "Machine Learning on Big Data" from HSE University provides a solid foundation in the techniques and algorithms used for building and optimizing machine learning systems. Software Engineers working with big data will find this course particularly valuable, as it equips them with the skills to design efficient and scalable software solutions that leverage the power of machine learning on vast and diverse datasets.
Quantitative Analyst
In the financial industry, Quantitative Analysts use mathematical and statistical models to evaluate and manage risk, forecast trends, and develop investment strategies. The online course "Machine Learning on Big Data" from HSE University provides a deep understanding of machine learning algorithms and their application in big data analysis. This knowledge is invaluable for Quantitative Analysts who seek to leverage advanced machine learning techniques to enhance their modeling capabilities and make more accurate predictions in the complex world of finance.
Business Analyst
Business Analysts are responsible for analyzing data and providing insights to businesses to help them make better decisions. The online course "Machine Learning on Big Data" from HSE University offers a solid foundation in machine learning algorithms and techniques for handling big data. Business Analysts can benefit from this course by enhancing their ability to extract meaningful insights from complex and umfangreich datasets and make data-driven recommendations that drive business success.
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines and infrastructure. The online course "Machine Learning on Big Data" from HSE University provides a comprehensive understanding of the challenges and techniques involved in handling and processing big data. Data Engineers will find this course highly valuable as it covers the practical aspects of data management and engineering, enabling them to build scalable and efficient data systems that support machine learning and advanced analytics.
Research Scientist
Research Scientists specializing in data science, machine learning, and big data are at the forefront of innovation. The online course "Machine Learning on Big Data" from HSE University provides a comprehensive foundation in the theoretical and practical aspects of machine learning in the context of big data. Research Scientists can expand their expertise and stay abreast of the latest advancements in the field by taking this course, which will enhance their ability to conduct groundbreaking research and make significant contributions to the domain.
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. The online course "Machine Learning on Big Data" from HSE Universityequips learners with the skills and knowledge to handle and analyze big data effectively. Data Analysts will find this course particularly valuable as it provides a strong foundation in machine learning algorithms and techniques, enabling them to extract meaningful insights from diverse and complex datasets.
Statistician
Statisticians use their expertise in data analysis and statistical modeling to solve real-world problems. The online course "Machine Learning on Big Data" from HSE University provides a comprehensive overview of machine learning algorithms and their application to big data. Statisticians can benefit from this course by gaining a deeper understanding of the theoretical and practical aspects of machine learning, which will enhance their ability to develop and implement statistical models for various domains.
Actuary
Actuaries use mathematical and statistical techniques to assess and manage risk in various fields, including insurance, finance, and healthcare. The online course "Machine Learning on Big Data" from HSE University provides a solid foundation in machine learning algorithms and their application to big data. Actuaries can benefit from this course by gaining a deeper understanding of machine learning techniques and their potential in risk assessment and modeling, which will enhance their ability to develop innovative solutions and make more informed decisions.
Financial Analyst
Financial Analysts use their expertise in financial modeling and analysis to make investment decisions and provide recommendations to clients. The online course "Machine Learning on Big Data" from HSE University provides a foundation in machine learning algorithms and their application to big data. Financial Analysts can benefit from this course by gaining a deeper understanding of machine learning techniques and their potential in financial analysis and forecasting, which will enhance their ability to make more accurate predictions and develop more effective investment strategies.
Market Researcher
Market Researchers use their expertise in data collection and analysis to understand consumer behavior and market trends. The online course "Machine Learning on Big Data" from HSE University provides a solid foundation in machine learning algorithms and their application to big data. Market Researchers can benefit from this course by gaining a deeper understanding of machine learning techniques and their potential in market research, which will enhance their ability to conduct more effective research and provide more valuable insights to clients.
Operations Research Analyst
Operations Research Analysts use their expertise in mathematical modeling and optimization techniques to improve the efficiency of business processes. The online course "Machine Learning on Big Data" from HSE University provides a solid foundation in machine learning algorithms and their application to big data. Operations Research Analysts can benefit from this course by gaining a deeper understanding of machine learning techniques and their potential in operations research, which will enhance their ability to develop more effective models and make more informed decisions.
Data Architect
Data Architects are responsible for designing and managing the architecture of data systems and infrastructure. The online course "Machine Learning on Big Data" from HSE University provides a comprehensive overview of the challenges and techniques involved in handling and processing big data. Data Architects will find this course highly valuable as it covers the practical aspects of data management and engineering, enabling them to build scalable and efficient data systems that support machine learning and advanced analytics.
Information Security Analyst
Information Security Analysts are responsible for protecting an organization's computer systems and networks from cyber threats. The online course "Machine Learning on Big Data" from HSE University provides a foundation in machine learning algorithms and their application to big data. Information Security Analysts can benefit from this course by gaining a deeper understanding of machine learning techniques and their potential in cybersecurity, which will enhance their ability to detect and respond to cyber threats more effectively.

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Машинное обучение на больших данных.
Can provide important background knowledge in ML from a probabilistic perspective.

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