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Rosmiyana Shekhovtsova,
Ivan Karpeev,
Ivan Stelmakh,
Vladimir Zubkov,
Ekaterina Fedorenko,
Sergey Koshelev,
Ivan Semchuk,
Daria Baidakova,
Damir Sibgatullin,
Oleg Pavlov,
and
Alexey Moga
This course will teach you efficient and scalable data labeling for ML and various business processes. The key here is the crowdsourcing approach, based on splitting complex challenges into small tasks and distributing them among a vast cloud of performers.
You will get acquainted with crowdsourcing as a methodology, mastering certain steps and techniques that ensure quality and stable performance. All these techniques will be implemented in practice straight away: throughout the course, you’ll design your own crowdsourcing project.
Register for this course and see more details by visiting:
OpenCourser.com/course/g119v8/practical
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops strong crowdsourcing techniques, which are useful for efficient data labeling in ML and business processes
Taught by instructors at the Moscow Institute of Physics and Technology and Yandex, who are recognized for their work in ML and data science
Provides a strong foundation for beginners in crowdsourcing and data labeling
Coursework involves designing a crowdsourcing project, offering hands-on experience in the field
Caution: Course requires some background knowledge in ML and data science
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Save Practical Crowdsourcing for Efficient Machine Learning to your list so you can find it easily later:
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Reviews summary
Crowdsourcing for ml and business
Practical Crowdsourcing for Efficient Machine Learning will teach you how to use crowdsourcing to label data and improve business processes. The course has a balanced approach of theory and practical and will be helpful for anyone working in machine learning or for those who want to learn more about crowdsourcing.
The course includes many hands-on tasks.
"All these techniques will be implemented in practice straight away: throughout the course, you’ll design your own crowdsourcing project."
Students would like to see ground truth for final project.
"Would be better if participants can see the ground-truth of final project after passing it for learning purposes."
Students want clear instructions.
"It is only upsetting that instructions aren't clear enough to be able to complete this course successfully."
"Instructions/ assignment guidelines are not clear enough and unfortunately no one is there to support and resolve issues quickly."
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 Practical Crowdsourcing for Efficient Machine Learning with these
activities:
Review data labeling techniques
Show steps
Strengthen your foundation by reviewing essential data labeling techniques.
Browse courses on
Data Labeling
Show steps
-
Review online resources or tutorials on data labeling.
-
Practice data labeling using online datasets or tools.
-
Discuss data labeling techniques with peers or mentors.
Practice crowdsourcing techniques
Show steps
Reinforce your understanding of key concepts related to crowdsourcing techniques.
Show steps
-
Complete online exercises and simulations related to crowdsourcing.
-
Participate in online forums or communities dedicated to crowdsourcing.
-
Experiment with different crowdsourcing platforms and tools.
Show all two activities
Review data labeling techniques
Show steps
Strengthen your foundation by reviewing essential data labeling techniques.
Browse courses on
Data Labeling
Show steps
Show steps
Show steps
- Review online resources or tutorials on data labeling.
- Practice data labeling using online datasets or tools.
- Discuss data labeling techniques with peers or mentors.
Practice crowdsourcing techniques
Show steps
Reinforce your understanding of key concepts related to crowdsourcing techniques.
Show steps
Show steps
Show steps
- Complete online exercises and simulations related to crowdsourcing.
- Participate in online forums or communities dedicated to crowdsourcing.
- Experiment with different crowdsourcing platforms and tools.
Show all two activities
Career center
Learners who complete Practical Crowdsourcing for Efficient Machine Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers implement machine learning algorithms to solve real-world problems. These experts analyze real-time data to create products like recommendation systems, fraud detection, and more. This course in Practical Crowdsourcing for Efficient Machine Learning helps build a foundation in using crowdsourcing strategies for large-scale data labeling. This can aid a Machine Learning Engineer by streamlining data labeling processes to improve model accuracy and efficiency.
Data Scientist
Data Scientist
Data Scientists use data to extract meaningful insights. They can use these insights to solve complex business problems. Those interested in becoming Data Scientists can take Practical Crowdsourcing for Efficient Machine Learning. This course helps build a strong foundation in data labeling techniques.
Business Analyst
Business Analyst
Business Analysts use data to identify business needs and opportunities. They can use these skills to improve business processes and make better decisions. Practical Crowdsourcing for Efficient Machine Learning may be useful for those interested in becoming Business Analysts. This course teaches data labeling techniques which can help extract meaningful insights from data.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They can use data to make decisions about product features, pricing, and marketing. Practical Crowdsourcing for Efficient Machine Learning may be useful for Product Managers who want to improve their data labeling processes. The course will teach techniques that can help improve the quality and accuracy of data used for decision-making.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They can use data to improve the efficiency and performance of software. Practical Crowdsourcing for Efficient Machine Learning may be useful for those interested in becoming Software Engineers who want to specialize in developing machine learning applications. The course will teach techniques that can help improve the quality and accuracy of data used for training machine learning models.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They can use data to improve the efficiency and performance of data processing systems. Practical Crowdsourcing for Efficient Machine Learning may be useful for those interested in becoming Data Engineers who want to specialize in developing machine learning applications. The course will teach techniques that can help improve the quality and accuracy of data used for training machine learning models.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test software applications to ensure that they meet requirements. They can use data to identify and fix defects. Practical Crowdsourcing for Efficient Machine Learning may be useful for Quality Assurance Analysts who want to improve their data labeling processes. The course will teach techniques that can help improve the quality and accuracy of data used for testing software applications.
Technical Writer
Technical Writer
Technical Writers create documentation for software applications and other technical products. They can use data to improve the clarity and accuracy of documentation. Practical Crowdsourcing for Efficient Machine Learning may be useful for those interested in becoming Technical Writers who want to specialize in writing documentation for machine learning applications. The course will teach techniques that can help improve the quality and accuracy of data used for creating documentation.
User Experience Designer
User Experience Designer
User Experience Designers design and develop user interfaces for software applications and other products. They can use data to improve the usability and satisfaction of users. Practical Crowdsourcing for Efficient Machine Learning may be useful for those interested in becoming User Experience Designers who want to specialize in designing user interfaces for machine learning applications. The course will teach techniques that can help improve the quality and accuracy of data used for designing user interfaces.
Project Manager
Project Manager
Project Managers plan and execute projects. They can use data to track progress and identify risks. Practical Crowdsourcing for Efficient Machine Learning may be useful for Project Managers who want to improve their data labeling processes. The course will teach techniques that can help improve the quality and accuracy of data used for project planning and execution.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns. They can use data to track results and identify opportunities. Practical Crowdsourcing for Efficient Machine Learning may be useful for Marketing Managers who want to improve their data labeling processes. The course will teach techniques that can help improve the quality and accuracy of data used for marketing campaigns.
Sales Manager
Sales Manager
Sales Managers plan and execute sales campaigns. They can use data to track results and identify opportunities. Practical Crowdsourcing for Efficient Machine Learning may be useful for Sales Managers who want to improve their data labeling processes. The course will teach techniques that can help improve the quality and accuracy of data used for sales campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/g119v8/practical
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
Practical Crowdsourcing for Efficient Machine Learning.
Save
Explores the potential of machine learning to revolutionize the world. It valuable resource for anyone who wants to learn more about the future of artificial intelligence.
Save
Explores the potential risks and benefits of artificial intelligence. It valuable resource for anyone who wants to learn more about the future of technology.
Save
Explores the potential impact of artificial intelligence on the workplace. It valuable resource for anyone who wants to learn more about the future of work.
Save
Explores the potential impact of the Internet of Things on human intelligence. It valuable resource for anyone who wants to learn more about the future of technology.
Save
Explores the potential future of humanity. It valuable resource for anyone who wants to learn more about the future of our species.
Explores the potential for the Singularity, a hypothetical future event in which artificial intelligence surpasses human intelligence. It valuable resource for anyone who wants to learn more about the future of technology.
Save
Explores the potential impact of artificial intelligence on human life. It valuable resource for anyone who wants to learn more about the future of our species.
Explores the potential future of humanity. It valuable resource for anyone who wants to learn more about the future of our species.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/g119v8/practical
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Time
5 hours
Via
Coursera
Institution
Yandex
Instructors
Rosmiyana Shekhovtsova
Ivan Karpeev
Ivan Stelmakh
Vladimir Zubkov
Ekaterina Fedorenko
Sergey Koshelev
Ivan Semchuk
Daria Baidakova
Damir Sibgatullin
Oleg Pavlov
Alexey Moga
Good to know
Develops strong crowdsourcing techniques, which are useful for efficient data labeling in ML and business processes
Taught by instructors at the Moscow Institute of Physics and Technology and Yandex, who are recognized for their work in ML and data science
Provides a strong foundation for beginners in crowdsourcing and data labeling
Coursework involves designing a crowdsourcing project, offering hands-on experience in the field
Caution: Course requires some background knowledge in ML and data science
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