What you'll learn?
Description
This course equips learners with foundational and practical skills in AI data collection, data preparation, annotation, labeling, validation, and data quality assurance. Students will learn how to gather datasets, organize information, apply annotation standards, use annotation tools, and maintain ethical and privacy compliance for artificial intelligence and machine learning projects.
Course content
106 lessons, 00:00:00
- Welcome to AI Data Annotation Preview Open
- Introduction to Artificial Intelligence Enroll to access
- AI and data basics Enroll to access
- Professional communication Enroll to access
- Reporting standards Enroll to access
- Documentation practices Enroll to access
- Communication in AI projects Enroll to access
- Activity 1: Team communication exercise Enroll to access
- Module 1 — Workplace Communication — Knowledge Check Enroll to access
- Working in annotation teams Enroll to access
- Project coordination Enroll to access
- Escalation procedures Enroll to access
- Workflow collaboration Enroll to access
- Activity 2: Team annotation simulation Enroll to access
- Module 2 — Teamwork & Collaboration — Knowledge Check Enroll to access
- Identifying annotation errors Enroll to access
- Resolving inconsistencies Enroll to access
- Data quality troubleshooting Enroll to access
- Activity 3: Data correction exercise Enroll to access
- Module 3 — Problem Solving & Analytical Thinking — Knowledge Check Enroll to access
- Workplace ethics Enroll to access
- Responsible AI Enroll to access
- Data confidentiality Enroll to access
- Ethical annotation standards Enroll to access
- Activity 4: AI ethics and compliance Enroll to access
- Module 4 — Safety, Ethics & Professionalism — Knowledge Check Enroll to access
- Data quality standards Enroll to access
- Annotation consistency Enroll to access
- Quality assurance procedures Enroll to access
- Review workflows Enroll to access
- Activity 5: Quality review exercise Enroll to access
- Module 5 — Apply Quality Standards — Knowledge Check Enroll to access
- File management Enroll to access
- Spreadsheet basics Enroll to access
- Cloud storage usage Enroll to access
- Dataset organization Enroll to access
- Activity 6: Managing annotation datasets Enroll to access
- Module 6 — Computer Operations — Knowledge Check Enroll to access
- Privacy regulations Enroll to access
- Data protection Enroll to access
- Ethical AI practices Enroll to access
- Secure handling of datasets Enroll to access
- Activity 7: Privacy violation scenarios Enroll to access
- Module 7 — Data Privacy & Ethics — Knowledge Check Enroll to access
- Types of Data Sources Enroll to access
- Data Formats Enroll to access
- Data Quality Metrics Enroll to access
- Ethical Considerations Enroll to access
- Activity 8: Dataset evaluation exercise Enroll to access
- Module 8 — Data Sources — Knowledge Check Enroll to access
- Collection Methods Enroll to access
- Collection Tools Enroll to access
- Secure Data Collection Enroll to access
- Workflow Planning Enroll to access
- Activity 9: Data collection planning Enroll to access
- Module 9 — Data Collection Planning — Knowledge Check Enroll to access
- Organizing Data Enroll to access
- Data Cleaning Enroll to access
- Metadata Documentation Enroll to access
- Data Validation Enroll to access
- Activity 10: Cleaning raw datasets Enroll to access
- Module 10 — Data Preparation — Knowledge Check Enroll to access
- Surveys & Forms Enroll to access
- API Collection Enroll to access
- Web Scraping Fundamentals Enroll to access
- Validation Techniques Enroll to access
- Activity 11: Lab — Simple data collection exercise Enroll to access
- Module 11 — Data Collection Methods — Knowledge Check Enroll to access
- Combining Data Sources Enroll to access
- Data Transformation Enroll to access
- Duplicate Checking Enroll to access
- Integrity Verification Enroll to access
- Activity 12: Merging datasets Enroll to access
- Module 12 — Data Aggregation — Knowledge Check Enroll to access
- Dataset Organization Enroll to access
- Backup & Recovery Enroll to access
- Access Controls Enroll to access
- Monitoring Quality Enroll to access
- Activity 13: Data quality standards Enroll to access
- Module 13 — Data Quality Management — Knowledge Check Enroll to access
- Annotation Concepts Enroll to access
- Annotation Standards Enroll to access
- Data Types Enroll to access
- Project Requirements Enroll to access
- Activity 14: Annotation guideline review Enroll to access
- Module 14 — Annotation Fundamentals — Knowledge Check Enroll to access
- Annotation Tools Enroll to access
- Annotation Techniques Enroll to access
- Accuracy Checking Enroll to access
- Annotation Reviews Enroll to access
- Activity 15: Image annotation exercise Enroll to access
- Module 15 — Performing Data Annotation — Knowledge Check Enroll to access
- Labeling Standards Enroll to access
- Dataset Categorization Enroll to access
- Label Verification Enroll to access
- Project Compliance Enroll to access
- Activity 16: Dataset labeling activity Enroll to access
- Module 16 — Data Labeling — Knowledge Check Enroll to access
- Workflow Documentation Enroll to access
- Storage Requirements Enroll to access
- Submission Procedures Enroll to access
- Metadata Requirements Enroll to access
- Activity 17: Complete annotated dataset submission Enroll to access
- Module 17 — Documentation & Submission — Knowledge Check Enroll to access
- Final Capstone: AI dataset annotation project Enroll to access
- Practical Examination Enroll to access
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This course includes
- 106 lessons
- 00:00:00 total duration
- Downloadable resources
- Full lifetime access
- Certificate of completion
Course Features
- Duration 00:00:00 hours
- Lectures 106
- Resources
- Language English
- Skill level Intermediate
- Students 2
- Certificate Yes
- Progression Flexible
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