- Algorithms
- Data Validation
- Machine Learning
- Artificial Intelligence
- Decision Tree Learning
- Performance Metric
- Data Quality
- Artificial Neural Networks
- Responsible AI
- MLOps (Machine Learning Operations)
- Strategic Decision-Making
- System Requirements
Introduction to AI: Key Concepts and Applications
Completed by yash garg
April 15, 2025
23 hours (approximately)
yash garg's account is verified. Coursera certifies their successful completion of Introduction to AI: Key Concepts and Applications
What you will learn
Understand core AI and ML concepts, key vocabulary, and the R.O.A.D. Framework for effective AI project management and implementation.
Evaluate machine learning models using performance metrics and understand the tradeoffs in algorithm selection and optimization.
Analyze AI algorithms like SVM, Decision Trees, and Neural Networks, identifying their strengths, weaknesses, and practical applications.
Assess data quality, calculate inter-annotator agreement, and address resource and performance tradeoffs in AI and ML systems.
Skills you will gain
