5-week online course
Lectures by field experts
3 full-cycle hands-on projects
Why master crowdsourcing?
  • Data is essential
    AI-based products and services rely on large amounts of high-quality labeled data for training, tuning, and evaluating machine learning algorithms. We strongly believe that machine learning workflows will focus increasingly on data production.
  • Optimize quality, speed and cost
    The crowdsourcing approach is a popular way to collect and label large datasets with faster turnaround and lower costs compared to using a limited group of experts for data collection and annotation. Our 10 years of industry experience and research show that building top-quality datasets requires a strict methodology.
Who is this course for?
If you want to explore cutting-edge data science and acquire skills needed for the future of ML, this is an excellent place to start. This is an introductory course that does not require any special knowledge. Mastering crowdsourcing will help you excel in your career and meet the rising demand for data labeling expertise. This course is a great fit for:
ML engineers
Data scientists
Crowd solution architects
What will I gain?
This course will introduce you to crowdsourcing as a practical methodology and help you master the essential steps and techniques to ensure top-quality data. More importantly, you will put the theory from lectures directly into practice as you design your own crowdsourcing projects throughout the course. By the end of this course, you will:
  • Understand the benefits and limitations of the crowdsourcing approach, from training computer vision algorithms to creative copywriting.
  • Integrate an on-demand workforce directly into your business and data processes.
  • Control the quality and accuracy of data labeling to develop high-performing ML models.
  • Create a ready-to-use three project portfolio.
  • Gain experience working on a project directly related to your job needs.
Apply new knowledge in hands-on projects
Project 1.
Ecommerce search
Classify queries and define search result relevance to improve a search engine’s performance.
Project 2.
Computer vision
Detect objects on photos and outline them to teach a CV algorithm to see furniture pieces in a room.
Project 3.
Voice recognition
Get a uniform transcription of random audio files to create a speech recognition model that will work with various voices and accents.
Learn from field experts
  • ML engineers, researchers, and crowd solution architects share their expertise.
  • Our team has presented shorter versions of this course as tutorials and workshops at leading data analytics conferences: KDD 2019, CVPR 2020, WSDM 2020, SIGMOD 2020, NeurIPS 2020, WWW 2021 and NAACL 2021.
  • Course instructors are engaged in research and teach at prestigious universities and the Yandex School of Data Analysis.
Daria Baidakova
Education & Customer Success Team Lead at Toloka
Ivan Stelmakh
Carnegie Mellon’s School of Computer Science
PhD candidate
Ivan Semchuk
at Yandex Self-Driving Group
Rosmiyana Shekhovtsova
MBA, Nanyang Technology University (Singapore)
Crowd Solutions Architect
at Toloka
Start learning
Wed Nov 17 2021 19:27:09 GMT+0300 (Moscow Standard Time)