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Hands-on Data Centric Visual AI
Master data-centric approaches to improve visual AI models through dataset quality management and advanced annotation techniques.
Course Cost
₹ 2,699
Intermediate
Skill Level
15 Hours
Self-paced lessons
This course provides a comprehensive, hands-on guide to developing and maintaining high-quality datasets for visual AI applications. Students will learn to implement various labeling approaches, assess and improve annotation quality for object detection tasks, and analyze the impact of data quality on model performance. The curriculum covers advanced tools like FiftyOne and CVAT for dataset exploration and refinement, addressing complex challenges in computer vision such as overlapping detections and small object detection. Learners will gain practical skills in data augmentation, sample hardness analysis, and entropy-based dataset curation to optimize visual AI model accuracy and reliability.
English
What you'll learn
Implement and evaluate various labeling approaches for visual AI datasets
Assess and improve annotation quality for object detection tasks
Analyze the impact of bounding box quality on model performance
Use advanced tools like FiftyOne and CVAT for dataset exploration and refinement
Address complex challenges in computer vision, including overlapping detections and small object detection
Apply data augmentation techniques to improve model robustness and generalization
Develop strategies for maintaining dataset quality over time
Implement end-to-end workflows for iterative model improvement using data-centric approaches
Skills you'll gain
This course includes:
5.58 Hours PreRecorded video
12 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
CREATED BY
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There are 4 modules in this course
This course offers a comprehensive exploration of data-centric approaches to visual AI, focusing on developing and maintaining high-quality datasets for computer vision applications. Students will learn advanced techniques for dataset management, annotation quality assessment, and model performance optimization. The curriculum covers a wide range of topics, including various labeling approaches, handling complex scenarios like overlapping detections and occlusions, and addressing challenges in small object detection. Learners will gain hands-on experience with tools like FiftyOne and CVAT for dataset exploration, error correction, and annotation refinement. The course also delves into advanced concepts such as data augmentation, sample hardness analysis, and entropy-based dataset curation. Through a combination of theoretical knowledge and practical exercises, students will develop the skills to create, maintain, and optimize datasets that lead to more accurate and reliable visual AI models.
Getting Started and the Data-Centric AI Paradigm
Module 1 · 6 Hours to complete
Image Quality and Its Impact on Model Performance
Module 2 · 8 Hours to complete
Label Quality and Its Impact on Model Performance
Module 3 · 5 Hours to complete
Putting It All Together
Module 4 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
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Students
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Course
Frequently asked Questions
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