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Савин Иван Ильич,
Новиков Максим Евгеньевич,
Космачев Алексей Дмитриевич,
and
Бардуков Анатолий Андреевич
Модель машинного обучения, обученная с высокой точностью — это хорошо, но не достаточно. Чтобы полностью раскрыть ее потенциал и начать решать с ее помощью реальные задачи, необходимо провести дополнительную работу по запуску модели в виде какого-то сервиса....
Read more
Модель машинного обучения, обученная с высокой точностью — это хорошо, но не достаточно. Чтобы полностью раскрыть ее потенциал и начать решать с ее помощью реальные задачи, необходимо провести дополнительную работу по запуску модели в виде какого-то сервиса....
Read more
Модель машинного обучения, обученная с высокой точностью — это хорошо, но не достаточно. Чтобы полностью раскрыть ее потенциал и начать решать с ее помощью реальные задачи, необходимо провести дополнительную работу по запуску модели в виде какого-то сервиса. В эту работу входит проектирование системы обработки данных, создание инфраструктуры для этой системы, оптимизация работы модели и последующий анализ работы полученного сервиса.
В этом онлайн-курсе НИУ ВШЭ мы рассмотрим наиболее важные аспекты построения систем машинного обучения и познакомимся с популярными инструментами, которые могут облегчить нам эту задачу.
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possible dealbreakers
Emphasizes the practical aspects of deploying machine learning models, which is crucial for real-world applications
Instructors are recognized experts in machine learning and data science, providing credibility and depth to the course
Suitable for intermediate-level learners with a foundational understanding of machine learning concepts
Focuses on popular and industry-standard tools, ensuring relevance to current practices
May require some additional background in data processing and infrastructure management
Assumes learners have access to appropriate computing resources and software
Save this course
Save Проектирование и реализация систем машинного обучения to your list so you can find it easily later:
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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 Проектирование и реализация систем машинного обучения with these
activities:
Review Linear Algebra and Calculus
Show steps
Strengthen your mathematical foundation for machine learning.
Browse courses on
Linear Algebra
Show steps
-
Review key concepts in linear algebra, such as matrices, vectors, and linear transformations.
-
Refresh your knowledge of differential and integral calculus.
-
Complete practice problems to reinforce your understanding.
Read 'Designing Data-Intensive Applications'
Show steps
Gain a foundational understanding of the principles and patterns needed to design, build, and maintain reliable, scalable, and performant data systems.
View
Secret Colors
on Amazon
Show steps
-
Read Chapters 1-3 to grasp the fundamentals of data engineering and data-intensive applications.
-
Complete the exercises in Chapters 1-3 to apply your understanding.
-
Summarize the key concepts of each chapter in your own words.
Solve LeetCode Problems on Machine Learning Algorithms
Show steps
Sharpen your problem-solving skills and reinforce your understanding of machine learning algorithms.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a specific LeetCode problem related to machine learning algorithms.
-
Analyze the problem and design a solution.
-
Implement your solution in your preferred programming language.
-
Test and debug your solution.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Tutorials on Cloud Computing for Machine Learning
Show steps
Gain practical experience with cloud computing platforms for machine learning.
Browse courses on
Cloud Computing
Show steps
-
Choose a cloud computing platform that supports machine learning, such as AWS or Azure.
-
Follow guided tutorials to set up a cloud environment for machine learning.
-
Deploy a machine learning model to the cloud and monitor its performance.
Build a Machine Learning Model Pipeline
Show steps
Develop a comprehensive understanding of the end-to-end process of building and deploying machine learning models.
Browse courses on
Machine Learning
Show steps
-
Define the problem you want to solve and gather the necessary data.
-
Preprocess and clean the data.
-
Train and evaluate multiple machine learning models.
-
Deploy the best-performing model into production.
Write a Blog Post on Machine Learning System Design
Show steps
Reinforce your understanding of machine learning system design by explaining it to others.
Browse courses on
Machine Learning
Show steps
-
Choose a specific aspect of machine learning system design to focus on, such as data pipelines or model evaluation.
-
Research and gather information from credible sources.
-
Write a well-structured and informative blog post that shares your knowledge and insights.
Attend a Workshop on Machine Learning Optimization
Show steps
Enhance your knowledge and skills in machine learning optimization techniques.
Browse courses on
Machine Learning Optimization
Show steps
-
Find and register for a relevant workshop on machine learning optimization.
-
Actively participate in the workshop and engage with the instructors and attendees.
-
Apply the learned techniques to your own machine learning projects.
Design and Implement a Data Visualization Dashboard
Show steps
Enhance your ability to communicate insights effectively by creating an interactive data visualization dashboard.
Browse courses on
Data Visualization
Show steps
-
Identify the key metrics and data points you want to visualize.
-
Choose an appropriate visualization library and tool.
-
Design and implement the dashboard, ensuring it is user-friendly and visually appealing.
Review Linear Algebra and Calculus
Show steps
Strengthen your mathematical foundation for machine learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review key concepts in linear algebra, such as matrices, vectors, and linear transformations.
- Refresh your knowledge of differential and integral calculus.
- Complete practice problems to reinforce your understanding.
Read 'Designing Data-Intensive Applications'
Show steps
Gain a foundational understanding of the principles and patterns needed to design, build, and maintain reliable, scalable, and performant data systems.
View
Secret Colors
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 to grasp the fundamentals of data engineering and data-intensive applications.
- Complete the exercises in Chapters 1-3 to apply your understanding.
- Summarize the key concepts of each chapter in your own words.
Solve LeetCode Problems on Machine Learning Algorithms
Show steps
Sharpen your problem-solving skills and reinforce your understanding of machine learning algorithms.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a specific LeetCode problem related to machine learning algorithms.
- Analyze the problem and design a solution.
- Implement your solution in your preferred programming language.
- Test and debug your solution.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Tutorials on Cloud Computing for Machine Learning
Show steps
Gain practical experience with cloud computing platforms for machine learning.
Browse courses on
Cloud Computing
Show steps
Show steps
Show steps
- Choose a cloud computing platform that supports machine learning, such as AWS or Azure.
- Follow guided tutorials to set up a cloud environment for machine learning.
- Deploy a machine learning model to the cloud and monitor its performance.
Build a Machine Learning Model Pipeline
Show steps
Develop a comprehensive understanding of the end-to-end process of building and deploying machine learning models.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define the problem you want to solve and gather the necessary data.
- Preprocess and clean the data.
- Train and evaluate multiple machine learning models.
- Deploy the best-performing model into production.
Write a Blog Post on Machine Learning System Design
Show steps
Reinforce your understanding of machine learning system design by explaining it to others.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a specific aspect of machine learning system design to focus on, such as data pipelines or model evaluation.
- Research and gather information from credible sources.
- Write a well-structured and informative blog post that shares your knowledge and insights.
Attend a Workshop on Machine Learning Optimization
Show steps
Enhance your knowledge and skills in machine learning optimization techniques.
Browse courses on
Machine Learning Optimization
Show steps
Show steps
Show steps
- Find and register for a relevant workshop on machine learning optimization.
- Actively participate in the workshop and engage with the instructors and attendees.
- Apply the learned techniques to your own machine learning projects.
Design and Implement a Data Visualization Dashboard
Show steps
Enhance your ability to communicate insights effectively by creating an interactive data visualization dashboard.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify the key metrics and data points you want to visualize.
- Choose an appropriate visualization library and tool.
- Design and implement the dashboard, ensuring it is user-friendly and visually appealing.
Career center
Learners who complete Проектирование и реализация систем машинного обучения will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists build and deploy machine learning systems to solve business problems. Being able to implement these systems in a real-world context is necessary for almost any data scientist. This course would be an excellent way to learn how to do this.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain the infrastructure that powers machine learning systems. This course would help someone in this role to build and refine their systems.
Business Analyst
Business Analyst
Business Analysts help businesses understand how to use data to make better decisions. Being able to interpret the results of machine learning systems is important for any business analyst who works with these systems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. Being able to understand and implement machine learning systems is important for any operations research analyst who works with these systems.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. Being able to understand and implement machine learning systems is important for any statistician who works with these systems.
Software Engineer
Software Engineer
Software Engineers write the code that runs machine learning systems. Being able to understand and implement machine learning systems is important for any software engineer who works on these systems.
Data Analyst
Data Analyst
Data Analysts use data to make informed decisions. Being able to interpret the results of machine learning systems is important for any data analyst who works with these systems.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. Being able to understand and implement machine learning systems is important for any database administrator who works with these systems.
Network Administrator
Network Administrator
Network Administrators manage and maintain computer networks. Being able to understand and implement machine learning systems is important for any network administrator who works with these systems.
Product Manager
Product Manager
Product Managers develop and manage products that use machine learning. Being able to understand the technical aspects of machine learning systems is important for any product manager who works on these products.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems. Being able to understand and implement machine learning systems is important for any cloud architect who works with these systems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to make investment decisions. Being able to understand and implement machine learning systems is important for any quantitative analyst who works with these systems.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computing technologies. Being able to understand and implement machine learning systems is important for any computer scientist who works on these technologies.
Systems Administrator
Systems Administrator
Systems Administrators manage and maintain computer systems. Being able to understand and implement machine learning systems is important for any systems administrator who works with these systems.
Information Systems Manager
Information Systems Manager
Information Systems Managers plan, implement, and maintain information systems. Being able to understand and implement machine learning systems is important for any information systems manager who works with these systems.
For more career information including salaries, visit:
OpenCourser.com/course/nlfyze/proektirovanie
Reading list
We've selected seven 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
Проектирование и реализация систем машинного обучения.
Save
An authoritative reference on deep learning, covering theoretical foundations, architectures, and applications. Provides in-depth coverage of topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Save
Explores the challenges and techniques for working with large-scale datasets in machine learning. Covers topics such as data sampling, feature selection, and distributed computing.
A classic textbook on reinforcement learning, providing a comprehensive overview of the theory, algorithms, and applications of this important technique.
Save
Covers the architectural patterns and design principles for building scalable, reliable, and maintainable data-intensive applications. Provides valuable insights into data modeling, storage, and processing.
Introduces Bayesian statistics and its applications in machine learning. Provides a solid theoretical foundation for understanding probabilistic modeling and inference techniques.
A practical guide to building and deploying machine learning models using popular Python libraries. Covers essential concepts and techniques for data preprocessing, model training, and evaluation.
Provides an accessible introduction to the fundamental concepts and algorithms of machine learning. Suitable for learners with no prior knowledge of the subject.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nlfyze/proektirovanie
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Time
24 hours
Via
Coursera
Institution
HSE University
Instructors
Савин Иван Ильич
Новиков Максим Евгеньевич
Космачев Алексей Дмитриевич
Бардуков Анатолий Андреевич
This course belongs to a
collection
called
Промышленное машинное обучение. It's comprised of three courses:
- Обработка больших данных
- Проектирование и реализация систем машинного обучения (viewing)
- Машинное обучение на больших данных
Good to know
Emphasizes the practical aspects of deploying machine learning models, which is crucial for real-world applications
Instructors are recognized experts in machine learning and data science, providing credibility and depth to the course
Suitable for intermediate-level learners with a foundational understanding of machine learning concepts
Focuses on popular and industry-standard tools, ensuring relevance to current practices
May require some additional background in data processing and infrastructure management
Assumes learners have access to appropriate computing resources and software
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